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Are Data Centres Able to Operate in Tropical Environments?

Given that a large percentage of the power used to run the average data centre is directly related to cooling, builders and designers do their best to locate new facilities in locales with cooler climates and lower humidity. The idea is to save money by reducing the amount of power used for temperature and humidity control. Still, the curious among us want to know if a data centre could still operate at peak performance under conditions twice the current norms. We are about to find out thanks to a test to get under way shortly in Singapore. News reports say the world's first tropical data centre is now in the planning stages and involves a number of big-name partners including Dell, Hewlett-Packard Enterprise, Intel, ERS, Fujitsu and others. The consortium will set up a controlled test environment in an existing Keppel data centre for the test.

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Dotsquares

Dotsquares are leading implementers of Web App Solutions & Application Development Services. Dotsquares use technology to make your vision a reality on web and mobile. With 14+ years experience working with big and small organisations we are successful through our Agile methodology, trust and superior communication skills.

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Hyper-Converged Infrastructure

Adapting to Changing Landscape: Challenges and Solutions in HCI

Article | October 3, 2023

Navigating the complex terrain of Hyper-Converged Infrastructure: Unveiling the best practices and innovative strategies to harness the maximum benefits of HCI for transformation of business. Contents 1. Introduction to Hyper-Converged Infrastructure 1.1 Evolution and adoption of HCI 1.2 Importance of Adapting to the Changing HCI Environment 2. Challenges in HCI 2.1 Integration & Compatibility: Legacy System Integration 2.2 Efficient Lifecycle: Firmware & Software Management 2.3 Resource Forecasting: Scalability Planning 2.4 Workload Segregation: Performance Optimization 2.5 Latency Optimization: Data Access Efficiency 3. Solutions for Adapting to Changing HCI Landscape 3.1 Interoperability 3.2 Lifecycle Management 3.3 Capacity Planning 3.4 Performance Isolation 3.5 Data Locality 4. Importance of Ongoing Adaptation in the HCI Domain 4.1 Evolving Technology 4.2 Performance Optimization 4.3 Scalability and Flexibility 4.4 Security and Compliance 4.5 Business Transformation 5. Key Takeaways from the Challenges and Solutions Discussed 1. Introduction to Hyper-Converged Infrastructure 1.1 Evolution and adoption of HCI Hyper-Converged Infrastructure has transformed by providing a consolidated and software-defined approach to data center infrastructure. HCI combines virtualization, storage, and networking into a single integrated system, simplifying management and improving scalability. It has gained widespread adoption due to its ability to address the challenges of data center consolidation, virtualization, and resource efficiency. HCI solutions have evolved to offer advanced features like hybrid and multi-cloud support, data deduplication, and disaster recovery, making them suitable for various workloads. The HCI market has experienced significant growth, with a diverse ecosystem of vendors offering turnkey appliances and software-defined solutions. It has become the preferred infrastructure for running workloads like VDI, databases, and edge computing. HCI's ability to simplify operations, improve resource utilization, and support diverse workloads ensures its continued relevance. 1.2 Importance of Adapting to the Changing HCI Environment Adapting to the changing Hyper-Converged Infrastructure is of utmost importance for businesses, as it offers a consolidated and software-defined approach to IT infrastructure, enabling streamlined management, improved scalability, and cost-effectiveness. Staying up-to-date with evolving HCI technologies and trends ensures businesses to leverage the latest advancements for optimizing their operations. Embracing HCI enables organizations to enhance resource utilization, accelerate deployment times, and support a wide range of workloads. In accordance with enhancement, it facilitates seamless integration with emerging technologies like hybrid and multi-cloud environments, containerization, and data analytics. Businesses can stay competitive, enhance their agility, and unlock the full potential of their IT infrastructure. 2. Challenges in HCI 2.1 Integration and Compatibility: Legacy System Integration Integrating Hyper-Converged Infrastructure with legacy systems can be challenging due to differences in architecture, protocols, and compatibility issues. Existing legacy systems may not seamlessly integrate with HCI solutions, leading to potential disruptions, data silos, and operational inefficiencies. This may hinder the organization's ability to fully leverage the benefits of HCI and limit its potential for streamlined operations and cost savings. 2.2 Efficient Lifecycle: Firmware and Software Management Managing firmware and software updates across the HCI infrastructure can be complex and time-consuming. Ensuring that all components within the HCI stack, including compute, storage, and networking, are running the latest firmware and software versions is crucial for security, performance, and stability. However, coordinating and applying updates across the entire infrastructure can pose challenges, resulting in potential vulnerabilities, compatibility issues, and suboptimal system performance. 2.3 Resource Forecasting: Scalability Planning Forecasting resource requirements and planning for scalability in an HCI environment is as crucial as efficiently implementing HCI systems. As workloads grow or change, accurately predicting the necessary computing, storage, and networking resources becomes essential. Without proper resource forecasting and scalability planning, organizations may face underutilization or overprovisioning of resources, leading to increased costs, performance bottlenecks, or inefficient resource allocation. 2.4 Workload Segregation: Performance Optimization In an HCI environment, effectively segregating workloads to optimize performance can be challenging. Workloads with varying resource requirements and performance characteristics may coexist within the HCI infrastructure. Ensuring that high-performance workloads receive the necessary resources and do not impact other workloads' performance is critical. Failure to segregate workloads properly can result in resource contention, degraded performance, and potential bottlenecks, affecting the overall efficiency and user experience. 2.5 Latency Optimization: Data Access Efficiency Optimizing data access latency in an HCI environment is a rising challenge. HCI integrates computing and storage into a unified system, and data access latency can significantly impact performance. Inefficient data retrieval and processing can lead to increased response times, reduced user satisfaction, and potential productivity losses. Failure to ensure the data access patterns, caching mechanisms, and optimized network configurations to minimize latency and maximize data access efficiency within the HCI infrastructure leads to such latency. 3. Solutions for Adapting to Changing HCI Landscape 3.1 Interoperability Achieved by: Standards-based Integration and API HCI solutions should prioritize adherence to industry standards and provide robust support for APIs. By leveraging standardized protocols and APIs, HCI can seamlessly integrate with legacy systems, ensuring compatibility and smooth data flow between different components. This promotes interoperability, eliminates data silos, and enables organizations to leverage their existing infrastructure investments while benefiting from the advantages of HCI. 3.2 Lifecycle Management Achieved by: Centralized Firmware and Software Management Efficient Lifecycle Management in Hyper-Converged Infrastructure can be achieved by implementing a centralized management system that automates firmware and software updates across the HCI infrastructure. This solution streamlines the process of identifying, scheduling, and deploying updates, ensuring that all components are running the latest versions. Centralized management reduces manual efforts, minimizes the risk of compatibility issues, and enhances security, stability, and overall system performance. 3.3 Capacity Planning Achieved by: Analytics-driven Resource Forecasting HCI solutions should incorporate analytics-driven capacity planning capabilities. By analyzing historical and real-time data, HCI systems can accurately predict resource requirements and assist organizations in scaling their infrastructure proactively. This solution enables efficient resource utilization, avoids underprovisioning or overprovisioning, and optimizes cost savings while ensuring that performance demands are met. 3.4 Performance Isolation Achieved by: Quality of Service and Resource Allocation Policies To achieve effective workload segregation and performance optimization, HCI solutions should provide robust Quality of Service (QoS) mechanisms and flexible resource allocation policies. QoS settings allow organizations to prioritize critical workloads, allocate resources based on predefined policies, and enforce performance guarantees for specific applications or users. This solution ensures that high-performance workloads receive the necessary resources while preventing resource contention and performance degradation for other workloads. 3.5 Data Locality Achieved by: Data Tiering and Caching Mechanisms Addressing latency optimization and data access efficiency, HCI solutions must incorporate data tiering and caching mechanisms. By intelligently placing frequently accessed data closer to the compute resources, such as utilizing flash storage or caching algorithms, HCI systems can minimize data access latency and improve overall performance. This solution enhances data locality, reduces network latency, and ensures faster data retrieval, resulting in optimized application response times and improved user experience. 4. Importance of Ongoing Adaptation in the HCI Domain continuous adaptation is of the utmost importance in the HCI domain. HCI is a swiftly advancing technology that continues to provide new capabilities. Organizations are able to maximize the benefits of HCI and maintain a competitive advantage if they stay apprised of the most recent advancements and adapt to the changing environment. Here are key reasons highlighting the significance of ongoing adaptation in the HCI domain: 4.1 Evolving Technology HCI is constantly changing, with new features, functionalities, and enhancements being introduced regularly. Ongoing adaptation allows organizations to take advantage of these advancements and incorporate them into their infrastructure. It ensures that businesses stay up-to-date with the latest technological trends and can make informed decisions to optimize their HCI deployments. 4.2 Performance Optimization Continuous adaptation enables organizations to fine-tune their HCI environments for optimal performance. By staying informed about performance best practices and emerging optimization techniques, businesses can make necessary adjustments to maximize resource utilization, improve workload performance, and enhance overall system efficiency. Ongoing adaptation ensures that HCI deployments are continuously optimized to meet evolving business requirements. 4.3 Scalability and Flexibility Adapting to the changing HCI landscape facilitates scalability and flexibility. As business needs evolve, organizations may require the ability to scale their infrastructure, accommodate new workloads, or adopt hybrid or multi-cloud environments. Ongoing adaptation allows businesses to assess and implement the necessary changes to their HCI deployments, ensuring they can seamlessly scale and adapt to evolving demands. 4.4 Security and Compliance The HCI domain is not immune to security threats and compliance requirements. Ongoing adaptation helps organizations stay vigilant and up-to-date with the latest security practices, threat landscapes, and regulatory changes. It enables businesses to implement robust security measures, proactively address vulnerabilities, and maintain compliance with industry standards and regulations. Ongoing adaptation ensures that HCI deployments remain secure and compliant in the face of evolving cybersecurity challenges. 4.5 Business Transformation Ongoing adaptation in the HCI domain supports broader business transformation initiatives. Organizations undergoing digital transformation may need to adopt new technologies, integrate with cloud services, or embrace emerging trends like edge computing. Adapting the HCI infrastructure allows businesses to align their IT infrastructure with strategic objectives, enabling seamless integration, improved agility, and the ability to capitalize on emerging opportunities. The adaptation is thus crucial in the HCI domain as it enables organizations to stay current with technological advancements, optimize performance, scale infrastructure, enhance security, and align with business transformation initiatives. By continuously adapting to the evolving HCI, businesses can maximize the value and benefits derived from their HCI investments. 5. Key Takeaways from Challenges and Solutions Discussed Hyper-Converged Infrastructure poses several challenges during the implementation and execution of systems that organizations need to address for optimal performance. Integration and compatibility issues arise when integrating HCI with legacy systems, requiring standards-based integration and API support. Efficient lifecycle management is crucial, involving centralized firmware and software management to automate updates and enhance security and stability. Accurate resource forecasting is vital for capacity planning, enabling organizations to scale their HCI infrastructure effectively. Workload segregation demands QOS mechanisms and flexible resource allocation policies to optimize performance. Apart from these, latency optimization requires data tiering and caching mechanisms to minimize data access latency and improve application response times. By tackling these challenges and implementing appropriate solutions, businesses can harness the full potential of HCI, streamlining operations, maximizing resource utilization, and ensuring exceptional performance and user experience.

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Application Infrastructure, Application Storage

5 TIPS TO ENSURE NETWORK SECURITY OF INTERNAL IT INFRASTRUCTURE

Article | July 19, 2023

What Is IT Infrastructure Security? If you are reading this blog, we would like to assume that you are either an aspiring cybersecurity professional or a business owner looking for ways to improve their network security. A business IT infrastructure includes networks, software, hardware, equipment, and other facilities that make up an IT network. These networks are applied to establish, monitor, test, manage, deliver, and support IT services. So, IT infrastructure security describes the process of safeguarding the core networking infrastructure, and it is typically applied to enterprise IT environments. You can improve IT infrastructure security by installing protective solutions to block unauthorized access, theft, deletion, and data modification.

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Hyper-Converged Infrastructure

A Look at Trends in IT infrastructure and Operations for 2022

Article | October 3, 2023

We’re all hoping that 2022 will finally end the unprecedented challenges brought by the global pandemic and things will return to a new normalcy. For IT infrastructure and operations organizations, the rising trends that we are seeing today will likely continue, but there are still a few areas that will need special attention from IT leaders over the next 12 to 18 months. In no particular order, they include: The New Edge Edge computing is now at the forefront. Two primary factors that make it business-critical are the increased prevalence of remote and hybrid workplace models where employees will continue working remotely, either from home or a branch office, resulting in an increased adoption of cloud-based businesses and communications services. With the rising focus on remote and hybrid workplace cultures, Zoom, Microsoft Teams, and Google Meet have continued to expand their solutions and add new features. As people start moving back to office, they are likely to want the same experience they had from home. In a typical enterprise setup, branch office traffic is usually backhauled all the way to the data center. This architecture severely impacts the user experience, so enterprises will have to review their network architectures and come up with a roadmap to accommodate local egress between branch offices and headquarters. That’s where the edge can help, bringing it closer to the workforce. This also brings an opportunity to optimize costs by migrating from some of the expensive multi-protocol label switching (MPLS) or private circuits to relatively low-cost direct internet circuits, which is being addressed by the new secure access service edge (SASE) architecture that is being offered by many established vendors. I anticipate some components of SASE, specifically those related to software-defined wide area network (SD-WAN), local egress, and virtual private network (VPN), will drive a lot of conversation this year. Holistic Cloud Strategy Cloud adoption will continue to grow, and along with software as a service (SaaS), there will be renewed interest in infrastructure as a service (IaaS), albeit for specific workloads. For a medium-to-large-sized enterprise with a substantial development environment, it will still be cost-prohibitive to move everything to the cloud, so any cloud strategy would need to be holistic and forward-looking to maximize its business value. Another pandemic-induced shift is from using virtual machines (VMs) as a consumption unit of compute to containers as a consumption unit of software. For on-premises or private cloud deployment architectures that require sustainable management, organizations will have to orchestrate containers and deploy efficient container security and management tools. Automation Now that cloud adoption, migration, and edge computing architectures are becoming more prevalent, the legacy methods of infrastructure provisioning and management will not be scalable. By increasing infrastructure automation, enterprises can optimize costs and be more flexible and efficient—but only if they are successful at developing new skills. To achieve the goal of “infrastructure as a code” will require a shift in the perspective on infrastructure automation to one that focuses on developing and sustaining skills and roles that improve efficiency and agility across on-premises, cloud, and edge infrastructures. Defining the roles of designers and architects to support automation is essential to ensure that automation works as expected, avoids significant errors, and complements other technologies. AIOps (Artificial Intelligence for IT Operations) Alongside complementing automation trends, the implementation of AIOps to effectively automate IT operations processes such as event correlation, anomaly detection, and causality determination will also be important. AIOps will eliminate the data silos in IT by bringing all types of data under one roof so it can be used to execute machine learning (ML)-based methods to develop insights for responsive enhancements and corrections. AIOps can also help with probable cause analytics by focusing on the most likely source of a problem. The concept of site reliability engineering (SRE) is being increasingly adopted by SaaS providers and will gain importance in enterprise IT environments due to the trends listed above. AIOps is a key component that will enable site reliability engineers (SREs) to respond more quickly—and even proactively—by resolving issues without manual intervention. These focus areas are by no means an exhaustive list. There are a variety of trends that will be more prevalent in specific industry areas, but a common theme in the post-pandemic era is going to be superior delivery of IT services. That’s also at the heart of the Autonomous Digital Enterprise, a forward-focused business framework designed to help companies make technology investments for the future.

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As Edge Applications Multiply, OpenInfra Community Delivers StarlingX 5.0, Offering Cloud Infrastructure Stack for 5G, IoT

Article | June 2, 2021

StarlingX—the open source edge computing and IoT cloud platform optimized for low-latency and high-performance applications—is available in its 5.0 release today. StarlingX combines Ceph, OpenStack, Kubernetes and more to create a full-featured cloud software stack that provides everything carriers and enterprises need to deploy an edge cloud on a few servers or hundreds of them.

Read More
Hyper-Converged Infrastructure

Transforming Data Management by Modernized Storage Solutions Using HCI

Article | October 3, 2023

Navigating the complex terrain of Hyper-Converged Infrastructure: Unveiling the best practices and innovative strategies to harness the maximum benefits of HCI for transformation of business. Contents 1. Introduction to Hyper-Converged Infrastructure 1.1 Evolution and adoption of HCI 1.2 Importance of Adapting to the Changing HCI Environment 2. Challenges in HCI 2.1 Integration & Compatibility: Legacy System Integration 2.2 Efficient Lifecycle: Firmware & Software Management 2.3 Resource Forecasting: Scalability Planning 2.4 Workload Segregation: Performance Optimization 2.5 Latency Optimization: Data Access Efficiency 3. Solutions for Adapting to Changing HCI Landscape 3.1 Interoperability 3.2 Lifecycle Management 3.3 Capacity Planning 3.4 Performance Isolation 3.5 Data Locality 4. Importance of Ongoing Adaptation in the HCI Domain 4.1 Evolving Technology 4.2 Performance Optimization 4.3 Scalability and Flexibility 4.4 Security and Compliance 4.5 Business Transformation 5. Key Takeaways from the Challenges and Solutions Discussed 1. Introduction to Hyper-Converged Infrastructure 1.1 Evolution and adoption of HCI Hyper-Converged Infrastructure has transformed by providing a consolidated and software-defined approach to data center infrastructure. HCI combines virtualization, storage, and networking into a single integrated system, simplifying management and improving scalability. It has gained widespread adoption due to its ability to address the challenges of data center consolidation, virtualization, and resource efficiency. HCI solutions have evolved to offer advanced features like hybrid and multi-cloud support, data deduplication, and disaster recovery, making them suitable for various workloads. The HCI market has experienced significant growth, with a diverse ecosystem of vendors offering turnkey appliances and software-defined solutions. It has become the preferred infrastructure for running workloads like VDI, databases, and edge computing. HCI's ability to simplify operations, improve resource utilization, and support diverse workloads ensures its continued relevance. 1.2 Importance of Adapting to the Changing HCI Environment Adapting to the changing Hyper-Converged Infrastructure is of utmost importance for businesses, as it offers a consolidated and software-defined approach to IT infrastructure, enabling streamlined management, improved scalability, and cost-effectiveness. Staying up-to-date with evolving HCI technologies and trends ensures businesses to leverage the latest advancements for optimizing their operations. Embracing HCI enables organizations to enhance resource utilization, accelerate deployment times, and support a wide range of workloads. In accordance with enhancement, it facilitates seamless integration with emerging technologies like hybrid and multi-cloud environments, containerization, and data analytics. Businesses can stay competitive, enhance their agility, and unlock the full potential of their IT infrastructure. 2. Challenges in HCI 2.1 Integration and Compatibility: Legacy System Integration Integrating Hyper-Converged Infrastructure with legacy systems can be challenging due to differences in architecture, protocols, and compatibility issues. Existing legacy systems may not seamlessly integrate with HCI solutions, leading to potential disruptions, data silos, and operational inefficiencies. This may hinder the organization's ability to fully leverage the benefits of HCI and limit its potential for streamlined operations and cost savings. 2.2 Efficient Lifecycle: Firmware and Software Management Managing firmware and software updates across the HCI infrastructure can be complex and time-consuming. Ensuring that all components within the HCI stack, including compute, storage, and networking, are running the latest firmware and software versions is crucial for security, performance, and stability. However, coordinating and applying updates across the entire infrastructure can pose challenges, resulting in potential vulnerabilities, compatibility issues, and suboptimal system performance. 2.3 Resource Forecasting: Scalability Planning Forecasting resource requirements and planning for scalability in an HCI environment is as crucial as efficiently implementing HCI systems. As workloads grow or change, accurately predicting the necessary computing, storage, and networking resources becomes essential. Without proper resource forecasting and scalability planning, organizations may face underutilization or overprovisioning of resources, leading to increased costs, performance bottlenecks, or inefficient resource allocation. 2.4 Workload Segregation: Performance Optimization In an HCI environment, effectively segregating workloads to optimize performance can be challenging. Workloads with varying resource requirements and performance characteristics may coexist within the HCI infrastructure. Ensuring that high-performance workloads receive the necessary resources and do not impact other workloads' performance is critical. Failure to segregate workloads properly can result in resource contention, degraded performance, and potential bottlenecks, affecting the overall efficiency and user experience. 2.5 Latency Optimization: Data Access Efficiency Optimizing data access latency in an HCI environment is a rising challenge. HCI integrates computing and storage into a unified system, and data access latency can significantly impact performance. Inefficient data retrieval and processing can lead to increased response times, reduced user satisfaction, and potential productivity losses. Failure to ensure the data access patterns, caching mechanisms, and optimized network configurations to minimize latency and maximize data access efficiency within the HCI infrastructure leads to such latency. 3. Solutions for Adapting to Changing HCI Landscape 3.1 Interoperability Achieved by: Standards-based Integration and API HCI solutions should prioritize adherence to industry standards and provide robust support for APIs. By leveraging standardized protocols and APIs, HCI can seamlessly integrate with legacy systems, ensuring compatibility and smooth data flow between different components. This promotes interoperability, eliminates data silos, and enables organizations to leverage their existing infrastructure investments while benefiting from the advantages of HCI. 3.2 Lifecycle Management Achieved by: Centralized Firmware and Software Management Efficient Lifecycle Management in Hyper-Converged Infrastructure can be achieved by implementing a centralized management system that automates firmware and software updates across the HCI infrastructure. This solution streamlines the process of identifying, scheduling, and deploying updates, ensuring that all components are running the latest versions. Centralized management reduces manual efforts, minimizes the risk of compatibility issues, and enhances security, stability, and overall system performance. 3.3 Capacity Planning Achieved by: Analytics-driven Resource Forecasting HCI solutions should incorporate analytics-driven capacity planning capabilities. By analyzing historical and real-time data, HCI systems can accurately predict resource requirements and assist organizations in scaling their infrastructure proactively. This solution enables efficient resource utilization, avoids underprovisioning or overprovisioning, and optimizes cost savings while ensuring that performance demands are met. 3.4 Performance Isolation Achieved by: Quality of Service and Resource Allocation Policies To achieve effective workload segregation and performance optimization, HCI solutions should provide robust Quality of Service (QoS) mechanisms and flexible resource allocation policies. QoS settings allow organizations to prioritize critical workloads, allocate resources based on predefined policies, and enforce performance guarantees for specific applications or users. This solution ensures that high-performance workloads receive the necessary resources while preventing resource contention and performance degradation for other workloads. 3.5 Data Locality Achieved by: Data Tiering and Caching Mechanisms Addressing latency optimization and data access efficiency, HCI solutions must incorporate data tiering and caching mechanisms. By intelligently placing frequently accessed data closer to the compute resources, such as utilizing flash storage or caching algorithms, HCI systems can minimize data access latency and improve overall performance. This solution enhances data locality, reduces network latency, and ensures faster data retrieval, resulting in optimized application response times and improved user experience. 4. Importance of Ongoing Adaptation in the HCI Domain continuous adaptation is of the utmost importance in the HCI domain. HCI is a swiftly advancing technology that continues to provide new capabilities. Organizations are able to maximize the benefits of HCI and maintain a competitive advantage if they stay apprised of the most recent advancements and adapt to the changing environment. Here are key reasons highlighting the significance of ongoing adaptation in the HCI domain: 4.1 Evolving Technology HCI is constantly changing, with new features, functionalities, and enhancements being introduced regularly. Ongoing adaptation allows organizations to take advantage of these advancements and incorporate them into their infrastructure. It ensures that businesses stay up-to-date with the latest technological trends and can make informed decisions to optimize their HCI deployments. 4.2 Performance Optimization Continuous adaptation enables organizations to fine-tune their HCI environments for optimal performance. By staying informed about performance best practices and emerging optimization techniques, businesses can make necessary adjustments to maximize resource utilization, improve workload performance, and enhance overall system efficiency. Ongoing adaptation ensures that HCI deployments are continuously optimized to meet evolving business requirements. 4.3 Scalability and Flexibility Adapting to the changing HCI landscape facilitates scalability and flexibility. As business needs evolve, organizations may require the ability to scale their infrastructure, accommodate new workloads, or adopt hybrid or multi-cloud environments. Ongoing adaptation allows businesses to assess and implement the necessary changes to their HCI deployments, ensuring they can seamlessly scale and adapt to evolving demands. 4.4 Security and Compliance The HCI domain is not immune to security threats and compliance requirements. Ongoing adaptation helps organizations stay vigilant and up-to-date with the latest security practices, threat landscapes, and regulatory changes. It enables businesses to implement robust security measures, proactively address vulnerabilities, and maintain compliance with industry standards and regulations. Ongoing adaptation ensures that HCI deployments remain secure and compliant in the face of evolving cybersecurity challenges. 4.5 Business Transformation Ongoing adaptation in the HCI domain supports broader business transformation initiatives. Organizations undergoing digital transformation may need to adopt new technologies, integrate with cloud services, or embrace emerging trends like edge computing. Adapting the HCI infrastructure allows businesses to align their IT infrastructure with strategic objectives, enabling seamless integration, improved agility, and the ability to capitalize on emerging opportunities. The adaptation is thus crucial in the HCI domain as it enables organizations to stay current with technological advancements, optimize performance, scale infrastructure, enhance security, and align with business transformation initiatives. By continuously adapting to the evolving HCI, businesses can maximize the value and benefits derived from their HCI investments. 5. Key Takeaways from Challenges and Solutions Discussed Hyper-Converged Infrastructure poses several challenges during the implementation and execution of systems that organizations need to address for optimal performance. Integration and compatibility issues arise when integrating HCI with legacy systems, requiring standards-based integration and API support. Efficient lifecycle management is crucial, involving centralized firmware and software management to automate updates and enhance security and stability. Accurate resource forecasting is vital for capacity planning, enabling organizations to scale their HCI infrastructure effectively. Workload segregation demands QOS mechanisms and flexible resource allocation policies to optimize performance. Apart from these, latency optimization requires data tiering and caching mechanisms to minimize data access latency and improve application response times. By tackling these challenges and implementing appropriate solutions, businesses can harness the full potential of HCI, streamlining operations, maximizing resource utilization, and ensuring exceptional performance and user experience.

Read More
Application Infrastructure, Application Storage

Mastering Infrastructure: Hyperconvergence Courses and Certifications

Article | July 19, 2023

What Is IT Infrastructure Security? If you are reading this blog, we would like to assume that you are either an aspiring cybersecurity professional or a business owner looking for ways to improve their network security. A business IT infrastructure includes networks, software, hardware, equipment, and other facilities that make up an IT network. These networks are applied to establish, monitor, test, manage, deliver, and support IT services. So, IT infrastructure security describes the process of safeguarding the core networking infrastructure, and it is typically applied to enterprise IT environments. You can improve IT infrastructure security by installing protective solutions to block unauthorized access, theft, deletion, and data modification.

Read More
Hyper-Converged Infrastructure

Adapting to Changing Landscape: Challenges and Solutions in HCI

Article | October 3, 2023

We’re all hoping that 2022 will finally end the unprecedented challenges brought by the global pandemic and things will return to a new normalcy. For IT infrastructure and operations organizations, the rising trends that we are seeing today will likely continue, but there are still a few areas that will need special attention from IT leaders over the next 12 to 18 months. In no particular order, they include: The New Edge Edge computing is now at the forefront. Two primary factors that make it business-critical are the increased prevalence of remote and hybrid workplace models where employees will continue working remotely, either from home or a branch office, resulting in an increased adoption of cloud-based businesses and communications services. With the rising focus on remote and hybrid workplace cultures, Zoom, Microsoft Teams, and Google Meet have continued to expand their solutions and add new features. As people start moving back to office, they are likely to want the same experience they had from home. In a typical enterprise setup, branch office traffic is usually backhauled all the way to the data center. This architecture severely impacts the user experience, so enterprises will have to review their network architectures and come up with a roadmap to accommodate local egress between branch offices and headquarters. That’s where the edge can help, bringing it closer to the workforce. This also brings an opportunity to optimize costs by migrating from some of the expensive multi-protocol label switching (MPLS) or private circuits to relatively low-cost direct internet circuits, which is being addressed by the new secure access service edge (SASE) architecture that is being offered by many established vendors. I anticipate some components of SASE, specifically those related to software-defined wide area network (SD-WAN), local egress, and virtual private network (VPN), will drive a lot of conversation this year. Holistic Cloud Strategy Cloud adoption will continue to grow, and along with software as a service (SaaS), there will be renewed interest in infrastructure as a service (IaaS), albeit for specific workloads. For a medium-to-large-sized enterprise with a substantial development environment, it will still be cost-prohibitive to move everything to the cloud, so any cloud strategy would need to be holistic and forward-looking to maximize its business value. Another pandemic-induced shift is from using virtual machines (VMs) as a consumption unit of compute to containers as a consumption unit of software. For on-premises or private cloud deployment architectures that require sustainable management, organizations will have to orchestrate containers and deploy efficient container security and management tools. Automation Now that cloud adoption, migration, and edge computing architectures are becoming more prevalent, the legacy methods of infrastructure provisioning and management will not be scalable. By increasing infrastructure automation, enterprises can optimize costs and be more flexible and efficient—but only if they are successful at developing new skills. To achieve the goal of “infrastructure as a code” will require a shift in the perspective on infrastructure automation to one that focuses on developing and sustaining skills and roles that improve efficiency and agility across on-premises, cloud, and edge infrastructures. Defining the roles of designers and architects to support automation is essential to ensure that automation works as expected, avoids significant errors, and complements other technologies. AIOps (Artificial Intelligence for IT Operations) Alongside complementing automation trends, the implementation of AIOps to effectively automate IT operations processes such as event correlation, anomaly detection, and causality determination will also be important. AIOps will eliminate the data silos in IT by bringing all types of data under one roof so it can be used to execute machine learning (ML)-based methods to develop insights for responsive enhancements and corrections. AIOps can also help with probable cause analytics by focusing on the most likely source of a problem. The concept of site reliability engineering (SRE) is being increasingly adopted by SaaS providers and will gain importance in enterprise IT environments due to the trends listed above. AIOps is a key component that will enable site reliability engineers (SREs) to respond more quickly—and even proactively—by resolving issues without manual intervention. These focus areas are by no means an exhaustive list. There are a variety of trends that will be more prevalent in specific industry areas, but a common theme in the post-pandemic era is going to be superior delivery of IT services. That’s also at the heart of the Autonomous Digital Enterprise, a forward-focused business framework designed to help companies make technology investments for the future.

Read More

As Edge Applications Multiply, OpenInfra Community Delivers StarlingX 5.0, Offering Cloud Infrastructure Stack for 5G, IoT

Article | June 2, 2021

StarlingX—the open source edge computing and IoT cloud platform optimized for low-latency and high-performance applications—is available in its 5.0 release today. StarlingX combines Ceph, OpenStack, Kubernetes and more to create a full-featured cloud software stack that provides everything carriers and enterprises need to deploy an edge cloud on a few servers or hundreds of them.

Read More
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Spotlight

Dotsquares

Dotsquares are leading implementers of Web App Solutions & Application Development Services. Dotsquares use technology to make your vision a reality on web and mobile. With 14+ years experience working with big and small organisations we are successful through our Agile methodology, trust and superior communication skills.

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Hyper-Converged Infrastructure

Alluxio Unveils New Data Platform for AI: Accelerating AI Products’ Time-to-Value and Maximizing Infrastructure ROI

GlobeNewswire | October 19, 2023

Alluxio, the data platform company for all data-driven workloads, today introduced Alluxio Enterprise AI, a new high-performance data platform designed to meet the rising demands of Artificial Intelligence (AI) and machine learning (ML) workloads on an enterprise’s data infrastructure. Alluxio Enterprise AI brings together performance, data accessibility, scalability and cost-efficiency to enterprise AI and analytics infrastructure to fuel next-generation data-intensive applications like generative AI, computer vision, natural language processing, large language models and high-performance data analytics. To stay competitive and achieve stronger business outcomes, enterprises are in a race to modernize their data and AI infrastructure. On this journey, they find that legacy data infrastructure cannot keep pace with next-generation data-intensive AI workloads. Challenges around low performance, data accessibility, GPU scarcity, complex data engineering, and underutilized resources frequently hinder enterprises' ability to extract value from their AI initiatives. According to Gartner®, “the value of operationalized AI lies in the ability to rapidly develop, deploy, adapt and maintain AI across different environments in the enterprise. Given the engineering complexity and the demand for faster time to market, it is critical to develop less rigid AI engineering pipelines or build AI models that can self-adapt in production.” “By 2026, enterprises that have adopted AI engineering practices to build and manage adaptive AI systems will outperform their peers in the operationalizing AI models by at least 25%.” Alluxio empowers the world’s leading organizations with the most modern Data & AI platforms, and today we take another significant leap forward, said Haoyuan Li, Founder and CEO, Alluxio. Alluxio Enterprise AI provides customers with streamlined solutions for AI and more by enabling enterprises to accelerate AI workloads and maximize value from their data. The leaders of tomorrow will know how to harness transformative AI and become increasingly data-driven with the newest technology for building and maintaining AI infrastructure for performance, seamless access and ease of management. With this announcement, Alluxio expands from a one-product portfolio to two product offerings - Alluxio Enterprise AI and Alluxio Enterprise Data - catering to the diverse needs of analytics and AI. Alluxio Enterprise AI is a new product that builds on the years of distributed systems experience accumulated from the previous Alluxio Enterprise Editions, combined with a new architecture that is optimized for AI/ML workloads. Alluxio Enterprise Data is the next-gen version of Alluxio Enterprise Edition, and will continue to be the ideal choice for businesses focused primarily on analytic workloads. Accelerating End-to-End Machine Learning Pipeline Alluxio Enterprise AI enables enterprise AI infrastructure to be performant, seamless, scalable and cost-effective on existing data lakes. Alluxio Enterprise AI helps data and AI leaders and practitioners achieve four key objectives in their AI initiatives: high-performance model training and deployment to yield quick business results; seamless data access for workloads across regions and clouds; infinite scale that has been battle-tested at internet giant’s scale; and maximized return on investments by working with existing tech stack instead of costly specialized storage. With Alluxio Enterprise AI, enterprises can expect up to 20x faster training speed compared to commodity storage, up to 10x accelerated model serving, over 90% GPU utilization, and up to 90% lower costs for AI infrastructure. Alluxio Enterprise AI has a distributed system architecture with decentralized metadata to eliminate bottlenecks when accessing massive numbers of small files, typical of AI workloads. This provides unlimited scalability beyond legacy architectures, regardless of file size or quantity. The distributed cache is tailored to AI workload I/O patterns, unlike traditional analytics. Finally, it supports analytics and full machine learning pipelines - from ingestion to ETL, pre-processing, training and serving. Alluxio Enterprise AI includes the following key features: Epic Performance for Model Training and Model Serving - Alluxio Enterprise AI offers significant performance improvements to model training and serving on an enterprise’s existing data lakes. The enhanced set of APIs for model training can deliver up to 20x performance over commodity storage. For model serving, Alluxio provides extreme concurrency and up to 10x acceleration for serving models from offline training clusters for online inference. Intelligent Distributed Caching Tailored to I/O Patterns of AI Workloads - Alluxio Enterprise AI’s distributed caching feature enables AI engines to read and write data through the high performance Alluxio cache instead of slow data lake storage. Alluxio’s intelligent caching strategies are tailored to the I/O patterns of AI engines – large file sequential access, large file random access, and massive small file access. This optimization delivers high throughput and low latency for data-hungry GPUs. Training clusters are continuously fed data from the high-performance distributed cache, achieving over 90% GPU utilization. Seamless Data Access for AI Workloads Across On-prem and Cloud Environments - Alluxio Enterprise AI provides a single pane of glass for enterprises to manage AI workloads across diverse infrastructure environments easily. Providing a source of truth of data for the machine learning pipeline, the product fundamentally removes the bottleneck of data lake silos in large enterprises. Sharing data between different business units and geographical locations becomes seamless with a standard data access layer via the Alluxio Enterprise AI platform. New Distributed System Architecture, Battle-tested At Scale - Alluxio Enterprise AI builds on a new innovative decentralized architecture, DORA (Decentralized Object Repository Architecture). This architecture sets the foundation to provide infinite scale for AI workloads. It allows an AI platform to handle up to 100 billion objects with commodity storage like Amazon S3. Leveraging Alluxio’s proven expertise in distributed systems, this new architecture has addressed the ever-increasing challenges of system scalability, metadata management, high availability, and performance. “Performance, cost optimization and GPU utilization are critical for optimizing next-generation workloads as organizations seek to scale AI throughout their businesses,” said Mike Leone, Analyst, Enterprise Strategy Group. “Alluxio has a compelling offering that can truly help data and AI teams achieve higher performance, seamless data access, and ease of management for model training and model serving.” “We've collaborated closely with Alluxio and consider their platform essential to our data infrastructure,” said Rob Collins, Analytics Cloud Engineering Director, Aunalytics. “Aunalytics is enthusiastic about Alluxio's new distributed system for Enterprise AI, recognizing its immense potential in the ever-evolving AI industry.” “Our in-house-trained large language model powers our Q&A application and recommendation engines, greatly enhancing user experience and engagement,” said Mengyu Hu, Software Engineer in the data platform team, Zhihu. “In our AI infrastructure, Alluxio is at the core and center. Using Alluxio as the data access layer, we’ve significantly enhanced model training performance by 3x and deployment by 10x with GPU utilization doubled. We are excited about Alluxio’s Enterprise AI and its new DORA architecture supporting access to massive small files. This offering gives us confidence in supporting AI applications facing the upcoming artificial intelligence wave.” Deploying Alluxio in Machine Learning Pipelines According to Gartner, data accessibility and data volume/complexity is one the top three barriers to the implementation of AI techniques within an organization. Alluxio Enterprise AI can be added to the existing AI infrastructure consisting of AI compute engines and data lake storage. Sitting in the middle of compute and storage, Alluxio can work across model training and model serving in the machine learning pipeline to achieve optimal speed and cost. For example, using PyTorch as the engine for training and serving, and Amazon S3 as the existing data lake: Model Training: When a user is training models, the PyTorch data loader loads datasets from a virtual local path /mnt/alluxio_fuse/training_datasets. Instead of loading directly from S3, the data loader will load from the Alluxio cache instead. During training, the cached datasets will be used in multiple epochs, so the entire training speed is no longer bottlenecked by retrieving from S3. In this way, Alluxio speeds up training by shortening data loading and eliminates GPU idle time, increasing GPU utilization. After the models are trained, PyTorch writes the model files to S3 through Alluxio. Model Serving: The latest trained models need to be deployed to the inference cluster. Multiple TorchServe instances read the model files concurrently from S3. Alluxio caches these latest model files from S3 and serves them to inference clusters with low latency. As a result, downstream AI applications can start inferencing using the most up-to-date models as soon as they are available. Platform Integration with Existing Systems To integrate Alluxio with the existing platform, users can deploy an Alluxio cluster between compute engines and storage systems. On the compute engine side, Alluxio integrates seamlessly with popular machine learning frameworks like PyTorch, Apache Spark, TensorFlow and Ray. Enterprises can integrate Alluxio with these compute frameworks via REST API, POSIX API or S3 API. On the storage side, Alluxio connects with all types of filesystems or object storage in any location, whether on-premises, in the cloud, or both. Supported storage systems include Amazon S3, Google GCS, Azure Blob Storage, MinIO, Ceph, HDFS, and more. Alluxio works on both on-premise and cloud, either bare-metal or containerized environments. Supported cloud platforms include AWS, GCP and Azure Cloud.

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Data Storage

AMI to Drive Intel DCM's Future and Broaden Manageability Solutions for Sustainable Data Centers

Cision Canada | October 17, 2023

AMI, the leader in foundational technology for sustainable, scalable, and secure global computing, is set to drive the future of Intel Data Center Manager (DCM) as it takes over the development, sales, and support of DCM under an agreement with Intel. This strategic transition empowers AMI to lead further the innovation and expansion of the Intel DCM product. With a unique position in the industry, AMI plays a pivotal role in enabling the cloud and data center ecosystem for all compute platforms. Intel DCM empowers data centers with the capability to manage and fine-tune server performance, energy consumption, and cooling efficiency. This operational optimization reduces the total cost of ownership, improves sustainability, and elevates performance benchmarks. We thank Intel for trusting AMI to lead Intel DCM into the future. This solution for efficient data center management will play a crucial role in enhancing the operational eco-efficiency of the data centers. It empowers data center managers with real-time insights into energy usage, thermal status, device health, and asset management, says Sanjoy Maity, CEO at AMI. AMI remains steadfast in aiding data center operators in achieving their manageability and sustainability objectives. About AMI AMI is Firmware Reimagined for modern computing. As a global leader in Dynamic Firmware for security, orchestration, and manageability solutions, AMI enables the world's compute platforms from on-premises to the cloud to the edge. AMI's industry-leading foundational technology and unwavering customer support have generated lasting partnerships and spurred innovation for some of the most prominent brands in the high-tech industry. For more information, visit ami.com.

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Data Storage

CoolIT Systems Partners with Switch Datacenters to Launch Advanced Energy-Efficient Data Centers

PRWeb | October 12, 2023

CoolIT Systems, a global leader in advanced cooling technology, and Switch Datacenters, a leading sustainable data center operator and developer, are thrilled to unveil a strategic partnership that will benefit an industry seeking to improve the sustainability of data centers. Following the recent release of the World Economic Forum's Top 10 Emerging Technologies featuring "Sustainable Computing" as the 9th-ranked emerging technology, the collaboration between Switch Datacenters and CoolIT facilitates data center space and the necessary technology to significantly curtail energy and water consumption inherent in modern data centers. With a history spanning more than a decade, Switch Datacenters has consistently demonstrated a commitment to environmental responsibility and sustainability. Their latest 45MW AMS6 data center near the Schiphol airport area features an HPC/AI-ready design that uses data center heat to warm adjacent greenhouses. Currently under development, their AMS5s is designed to make a significant contribution to the Amsterdam municipal heat grid with green, CO2-neutral heat. For both data centers, there's a marked preference for liquid cooling because it allows heat extraction at temperatures higher than traditional air cooling, offering enhanced economic value. CoolIT Systems is the industry-leading provider of efficient Direct Liquid Cooling (DLC) and Rear Door Heat Exchangers (RDHx) that enable heat reuse and help customers meet key Environmental, Social, and Governance (ESG) targets. CoolIT DLC technology is featured as a factory-installed, warranty-approved feature from most major servers OEMs. "CoolIT's DLC and RDHx technologies have been instrumental in various data center heat reuse projects for years, with customers reporting at minimum a savings of 10% on energy bills (OPEX), more than 50% on CAPEX spends, and examples of PUE lowered from 1.30 to 1.02," expressed Peggy Burroughs, Director of CoolIT Next. "Our collaborations with most major server OEMs have cultivated an expansive ecosystem for clients aspiring to achieve both business and ESG goals." CoolIT is the right company to help make our vision a reality at an industrial scale. Both CoolIT and Switch Datacenters have shared the same passion for sustainable innovation for years and truly want to elevate the industry's adoption of liquid cooling. We believe liquid cooling will be the game-changer in the next wave of sustainable data center designs, and CoolIT is one of the very few companies that can lead this upcoming demand, thanks to their long history of innovation, reliability, breadth of portfolio, and capabilities to scale with their numerous IT partners worldwide, says Gregor Snip, CEO of Switch Datacenters. Data centers are projected to account for 8% of the global electricity consumption by 20301. Technologies such as Direct Liquid Cooling can significantly reduce data center energy consumption by 25-40% and deliver water savings of 70-97%, depending on local climate and specific implementations2. Switch Datacenters is leading the charge in embracing sustainable alternatives for heating by reusing data center-generated heat. With their latest project, Switch Datacenters AMS6, they will revolutionize the way nearby greenhouses are heated by providing high-temperature heat from their data center. This innovative solution will replace traditional fossil fuel-based heating and contribute to a greener future. By harnessing the power of IT servers to generate green heat for large-scale crop cultivation, Switch Datacenters is driving the transition away from fossil fuels. They strongly advocate for the integration of heat-recapture-enabled data centers in areas with high demand for heat, making it a standard design principle. With the world calling for sustainable IT and data centers, the time is ripe for this much-needed change. With the combined expertise of CoolIT and Switch Datacenters, customers can now harness technologically advanced solutions that not only result in considerable energy and water savings but also contribute significantly to the global drive for reduced environmental impact, aligning with the United Nations Sustainable Development Goals of Affordable and Clean Energy (SDG 7), Industry, Innovation, and Infrastructure (SDG 9), and Climate Action (SDG 13). About CoolIT Systems CoolIT Systems is renowned for its scalable liquid cooling solutions tailored for the world's most challenging computing contexts. In both enterprise data centers and high-performance computing domains, CoolIT collaborates with global OEM server design leaders, formulating efficient and trustworthy liquid cooling solutions. In the desktop enthusiast arena, CoolIT delivers unmatched performance for a diverse range of gaming setups. Their modular Direct Liquid Cooling technology, Rack DLC™, empowers dramatic spikes in rack densities, component efficacy, and power savings. Jointly, CoolIT and its allies are pioneering the large-scale adoption of sophisticated cooling techniques. About Switch Datacenters Switch Datacenters is a Dutch privately-owned data center operator and developer founded in 2010 by Gregor Snip and his brother. Initially established as a private data center for their successful hosting company, the Amsterdam-based company later expanded into a fully-fledged commercial data center operator. It added several highly efficient and environmentally-friendly data center sites to its portfolio, with a current focus on constructing and managing wholesale data centers for large global customers while also providing tailor-made data center services. Switch Datacenters is an ambitious, 100% Dutch player in the Amsterdam data center sector, experiencing rapid growth by continually partnering with leading and globally recognized industry players and customers. The company maintains a steadfast commitment to innovative and sustainable site development. Currently, Switch Datacenters has over 200MW of new sustainable data center capacity in development. This year, it will launch its flagship sustainable data center, AMS4, with major customers having already pre-leased the 15-18MW facility.

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Hyper-Converged Infrastructure

Alluxio Unveils New Data Platform for AI: Accelerating AI Products’ Time-to-Value and Maximizing Infrastructure ROI

GlobeNewswire | October 19, 2023

Alluxio, the data platform company for all data-driven workloads, today introduced Alluxio Enterprise AI, a new high-performance data platform designed to meet the rising demands of Artificial Intelligence (AI) and machine learning (ML) workloads on an enterprise’s data infrastructure. Alluxio Enterprise AI brings together performance, data accessibility, scalability and cost-efficiency to enterprise AI and analytics infrastructure to fuel next-generation data-intensive applications like generative AI, computer vision, natural language processing, large language models and high-performance data analytics. To stay competitive and achieve stronger business outcomes, enterprises are in a race to modernize their data and AI infrastructure. On this journey, they find that legacy data infrastructure cannot keep pace with next-generation data-intensive AI workloads. Challenges around low performance, data accessibility, GPU scarcity, complex data engineering, and underutilized resources frequently hinder enterprises' ability to extract value from their AI initiatives. According to Gartner®, “the value of operationalized AI lies in the ability to rapidly develop, deploy, adapt and maintain AI across different environments in the enterprise. Given the engineering complexity and the demand for faster time to market, it is critical to develop less rigid AI engineering pipelines or build AI models that can self-adapt in production.” “By 2026, enterprises that have adopted AI engineering practices to build and manage adaptive AI systems will outperform their peers in the operationalizing AI models by at least 25%.” Alluxio empowers the world’s leading organizations with the most modern Data & AI platforms, and today we take another significant leap forward, said Haoyuan Li, Founder and CEO, Alluxio. Alluxio Enterprise AI provides customers with streamlined solutions for AI and more by enabling enterprises to accelerate AI workloads and maximize value from their data. The leaders of tomorrow will know how to harness transformative AI and become increasingly data-driven with the newest technology for building and maintaining AI infrastructure for performance, seamless access and ease of management. With this announcement, Alluxio expands from a one-product portfolio to two product offerings - Alluxio Enterprise AI and Alluxio Enterprise Data - catering to the diverse needs of analytics and AI. Alluxio Enterprise AI is a new product that builds on the years of distributed systems experience accumulated from the previous Alluxio Enterprise Editions, combined with a new architecture that is optimized for AI/ML workloads. Alluxio Enterprise Data is the next-gen version of Alluxio Enterprise Edition, and will continue to be the ideal choice for businesses focused primarily on analytic workloads. Accelerating End-to-End Machine Learning Pipeline Alluxio Enterprise AI enables enterprise AI infrastructure to be performant, seamless, scalable and cost-effective on existing data lakes. Alluxio Enterprise AI helps data and AI leaders and practitioners achieve four key objectives in their AI initiatives: high-performance model training and deployment to yield quick business results; seamless data access for workloads across regions and clouds; infinite scale that has been battle-tested at internet giant’s scale; and maximized return on investments by working with existing tech stack instead of costly specialized storage. With Alluxio Enterprise AI, enterprises can expect up to 20x faster training speed compared to commodity storage, up to 10x accelerated model serving, over 90% GPU utilization, and up to 90% lower costs for AI infrastructure. Alluxio Enterprise AI has a distributed system architecture with decentralized metadata to eliminate bottlenecks when accessing massive numbers of small files, typical of AI workloads. This provides unlimited scalability beyond legacy architectures, regardless of file size or quantity. The distributed cache is tailored to AI workload I/O patterns, unlike traditional analytics. Finally, it supports analytics and full machine learning pipelines - from ingestion to ETL, pre-processing, training and serving. Alluxio Enterprise AI includes the following key features: Epic Performance for Model Training and Model Serving - Alluxio Enterprise AI offers significant performance improvements to model training and serving on an enterprise’s existing data lakes. The enhanced set of APIs for model training can deliver up to 20x performance over commodity storage. For model serving, Alluxio provides extreme concurrency and up to 10x acceleration for serving models from offline training clusters for online inference. Intelligent Distributed Caching Tailored to I/O Patterns of AI Workloads - Alluxio Enterprise AI’s distributed caching feature enables AI engines to read and write data through the high performance Alluxio cache instead of slow data lake storage. Alluxio’s intelligent caching strategies are tailored to the I/O patterns of AI engines – large file sequential access, large file random access, and massive small file access. This optimization delivers high throughput and low latency for data-hungry GPUs. Training clusters are continuously fed data from the high-performance distributed cache, achieving over 90% GPU utilization. Seamless Data Access for AI Workloads Across On-prem and Cloud Environments - Alluxio Enterprise AI provides a single pane of glass for enterprises to manage AI workloads across diverse infrastructure environments easily. Providing a source of truth of data for the machine learning pipeline, the product fundamentally removes the bottleneck of data lake silos in large enterprises. Sharing data between different business units and geographical locations becomes seamless with a standard data access layer via the Alluxio Enterprise AI platform. New Distributed System Architecture, Battle-tested At Scale - Alluxio Enterprise AI builds on a new innovative decentralized architecture, DORA (Decentralized Object Repository Architecture). This architecture sets the foundation to provide infinite scale for AI workloads. It allows an AI platform to handle up to 100 billion objects with commodity storage like Amazon S3. Leveraging Alluxio’s proven expertise in distributed systems, this new architecture has addressed the ever-increasing challenges of system scalability, metadata management, high availability, and performance. “Performance, cost optimization and GPU utilization are critical for optimizing next-generation workloads as organizations seek to scale AI throughout their businesses,” said Mike Leone, Analyst, Enterprise Strategy Group. “Alluxio has a compelling offering that can truly help data and AI teams achieve higher performance, seamless data access, and ease of management for model training and model serving.” “We've collaborated closely with Alluxio and consider their platform essential to our data infrastructure,” said Rob Collins, Analytics Cloud Engineering Director, Aunalytics. “Aunalytics is enthusiastic about Alluxio's new distributed system for Enterprise AI, recognizing its immense potential in the ever-evolving AI industry.” “Our in-house-trained large language model powers our Q&A application and recommendation engines, greatly enhancing user experience and engagement,” said Mengyu Hu, Software Engineer in the data platform team, Zhihu. “In our AI infrastructure, Alluxio is at the core and center. Using Alluxio as the data access layer, we’ve significantly enhanced model training performance by 3x and deployment by 10x with GPU utilization doubled. We are excited about Alluxio’s Enterprise AI and its new DORA architecture supporting access to massive small files. This offering gives us confidence in supporting AI applications facing the upcoming artificial intelligence wave.” Deploying Alluxio in Machine Learning Pipelines According to Gartner, data accessibility and data volume/complexity is one the top three barriers to the implementation of AI techniques within an organization. Alluxio Enterprise AI can be added to the existing AI infrastructure consisting of AI compute engines and data lake storage. Sitting in the middle of compute and storage, Alluxio can work across model training and model serving in the machine learning pipeline to achieve optimal speed and cost. For example, using PyTorch as the engine for training and serving, and Amazon S3 as the existing data lake: Model Training: When a user is training models, the PyTorch data loader loads datasets from a virtual local path /mnt/alluxio_fuse/training_datasets. Instead of loading directly from S3, the data loader will load from the Alluxio cache instead. During training, the cached datasets will be used in multiple epochs, so the entire training speed is no longer bottlenecked by retrieving from S3. In this way, Alluxio speeds up training by shortening data loading and eliminates GPU idle time, increasing GPU utilization. After the models are trained, PyTorch writes the model files to S3 through Alluxio. Model Serving: The latest trained models need to be deployed to the inference cluster. Multiple TorchServe instances read the model files concurrently from S3. Alluxio caches these latest model files from S3 and serves them to inference clusters with low latency. As a result, downstream AI applications can start inferencing using the most up-to-date models as soon as they are available. Platform Integration with Existing Systems To integrate Alluxio with the existing platform, users can deploy an Alluxio cluster between compute engines and storage systems. On the compute engine side, Alluxio integrates seamlessly with popular machine learning frameworks like PyTorch, Apache Spark, TensorFlow and Ray. Enterprises can integrate Alluxio with these compute frameworks via REST API, POSIX API or S3 API. On the storage side, Alluxio connects with all types of filesystems or object storage in any location, whether on-premises, in the cloud, or both. Supported storage systems include Amazon S3, Google GCS, Azure Blob Storage, MinIO, Ceph, HDFS, and more. Alluxio works on both on-premise and cloud, either bare-metal or containerized environments. Supported cloud platforms include AWS, GCP and Azure Cloud.

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Data Storage

AMI to Drive Intel DCM's Future and Broaden Manageability Solutions for Sustainable Data Centers

Cision Canada | October 17, 2023

AMI, the leader in foundational technology for sustainable, scalable, and secure global computing, is set to drive the future of Intel Data Center Manager (DCM) as it takes over the development, sales, and support of DCM under an agreement with Intel. This strategic transition empowers AMI to lead further the innovation and expansion of the Intel DCM product. With a unique position in the industry, AMI plays a pivotal role in enabling the cloud and data center ecosystem for all compute platforms. Intel DCM empowers data centers with the capability to manage and fine-tune server performance, energy consumption, and cooling efficiency. This operational optimization reduces the total cost of ownership, improves sustainability, and elevates performance benchmarks. We thank Intel for trusting AMI to lead Intel DCM into the future. This solution for efficient data center management will play a crucial role in enhancing the operational eco-efficiency of the data centers. It empowers data center managers with real-time insights into energy usage, thermal status, device health, and asset management, says Sanjoy Maity, CEO at AMI. AMI remains steadfast in aiding data center operators in achieving their manageability and sustainability objectives. About AMI AMI is Firmware Reimagined for modern computing. As a global leader in Dynamic Firmware for security, orchestration, and manageability solutions, AMI enables the world's compute platforms from on-premises to the cloud to the edge. AMI's industry-leading foundational technology and unwavering customer support have generated lasting partnerships and spurred innovation for some of the most prominent brands in the high-tech industry. For more information, visit ami.com.

Read More

Data Storage

CoolIT Systems Partners with Switch Datacenters to Launch Advanced Energy-Efficient Data Centers

PRWeb | October 12, 2023

CoolIT Systems, a global leader in advanced cooling technology, and Switch Datacenters, a leading sustainable data center operator and developer, are thrilled to unveil a strategic partnership that will benefit an industry seeking to improve the sustainability of data centers. Following the recent release of the World Economic Forum's Top 10 Emerging Technologies featuring "Sustainable Computing" as the 9th-ranked emerging technology, the collaboration between Switch Datacenters and CoolIT facilitates data center space and the necessary technology to significantly curtail energy and water consumption inherent in modern data centers. With a history spanning more than a decade, Switch Datacenters has consistently demonstrated a commitment to environmental responsibility and sustainability. Their latest 45MW AMS6 data center near the Schiphol airport area features an HPC/AI-ready design that uses data center heat to warm adjacent greenhouses. Currently under development, their AMS5s is designed to make a significant contribution to the Amsterdam municipal heat grid with green, CO2-neutral heat. For both data centers, there's a marked preference for liquid cooling because it allows heat extraction at temperatures higher than traditional air cooling, offering enhanced economic value. CoolIT Systems is the industry-leading provider of efficient Direct Liquid Cooling (DLC) and Rear Door Heat Exchangers (RDHx) that enable heat reuse and help customers meet key Environmental, Social, and Governance (ESG) targets. CoolIT DLC technology is featured as a factory-installed, warranty-approved feature from most major servers OEMs. "CoolIT's DLC and RDHx technologies have been instrumental in various data center heat reuse projects for years, with customers reporting at minimum a savings of 10% on energy bills (OPEX), more than 50% on CAPEX spends, and examples of PUE lowered from 1.30 to 1.02," expressed Peggy Burroughs, Director of CoolIT Next. "Our collaborations with most major server OEMs have cultivated an expansive ecosystem for clients aspiring to achieve both business and ESG goals." CoolIT is the right company to help make our vision a reality at an industrial scale. Both CoolIT and Switch Datacenters have shared the same passion for sustainable innovation for years and truly want to elevate the industry's adoption of liquid cooling. We believe liquid cooling will be the game-changer in the next wave of sustainable data center designs, and CoolIT is one of the very few companies that can lead this upcoming demand, thanks to their long history of innovation, reliability, breadth of portfolio, and capabilities to scale with their numerous IT partners worldwide, says Gregor Snip, CEO of Switch Datacenters. Data centers are projected to account for 8% of the global electricity consumption by 20301. Technologies such as Direct Liquid Cooling can significantly reduce data center energy consumption by 25-40% and deliver water savings of 70-97%, depending on local climate and specific implementations2. Switch Datacenters is leading the charge in embracing sustainable alternatives for heating by reusing data center-generated heat. With their latest project, Switch Datacenters AMS6, they will revolutionize the way nearby greenhouses are heated by providing high-temperature heat from their data center. This innovative solution will replace traditional fossil fuel-based heating and contribute to a greener future. By harnessing the power of IT servers to generate green heat for large-scale crop cultivation, Switch Datacenters is driving the transition away from fossil fuels. They strongly advocate for the integration of heat-recapture-enabled data centers in areas with high demand for heat, making it a standard design principle. With the world calling for sustainable IT and data centers, the time is ripe for this much-needed change. With the combined expertise of CoolIT and Switch Datacenters, customers can now harness technologically advanced solutions that not only result in considerable energy and water savings but also contribute significantly to the global drive for reduced environmental impact, aligning with the United Nations Sustainable Development Goals of Affordable and Clean Energy (SDG 7), Industry, Innovation, and Infrastructure (SDG 9), and Climate Action (SDG 13). About CoolIT Systems CoolIT Systems is renowned for its scalable liquid cooling solutions tailored for the world's most challenging computing contexts. In both enterprise data centers and high-performance computing domains, CoolIT collaborates with global OEM server design leaders, formulating efficient and trustworthy liquid cooling solutions. In the desktop enthusiast arena, CoolIT delivers unmatched performance for a diverse range of gaming setups. Their modular Direct Liquid Cooling technology, Rack DLC™, empowers dramatic spikes in rack densities, component efficacy, and power savings. Jointly, CoolIT and its allies are pioneering the large-scale adoption of sophisticated cooling techniques. About Switch Datacenters Switch Datacenters is a Dutch privately-owned data center operator and developer founded in 2010 by Gregor Snip and his brother. Initially established as a private data center for their successful hosting company, the Amsterdam-based company later expanded into a fully-fledged commercial data center operator. It added several highly efficient and environmentally-friendly data center sites to its portfolio, with a current focus on constructing and managing wholesale data centers for large global customers while also providing tailor-made data center services. Switch Datacenters is an ambitious, 100% Dutch player in the Amsterdam data center sector, experiencing rapid growth by continually partnering with leading and globally recognized industry players and customers. The company maintains a steadfast commitment to innovative and sustainable site development. Currently, Switch Datacenters has over 200MW of new sustainable data center capacity in development. This year, it will launch its flagship sustainable data center, AMS4, with major customers having already pre-leased the 15-18MW facility.

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Events