Chelsio Storage over IP and other Networks Enable Data Infrastructures

Chelsio and Storage over IP (SoIP) continue to enable Data Infrastructures from legacy to software defined virtual, container, cloud as well as converged. This past week I had a chance to visit with Chelsio to discuss data infrastructures, server storage I/O networking along with other related topics. More on Chelsio later in this post, however, for now lets take a quick step back and refresh what is SoIP (Storage over IP) along with Storage over Ethernet (among other networks).

Spotlight

Clariba

Clariba, recognised as one of the leading SAP partners in EMEA, is an independent, expert analytics consultancy that provides industry-focused solutions for enterprise performance management, business intelligence and organisational alignment. Our mission is to deliver innovative, reliable and high-quality business analytics solutions, providing our customers with clarity and actionable insight to improve their business performance and develop new business models for long-term, sustainable competitive advantage.

OTHER ARTICLES
Hyper-Converged Infrastructure

The world is ready for 5G. Are you?

Article | October 3, 2023

At last, the wait for 5G is nearly over. As this map shows, coverage is widespread across much of the U.S., in 24 EU countries, and in pockets around the globe. The new wireless standard is worth the wait. Compared to 4G, the new wireless standard can move more data from the edge, with less latency. And connect many more users and devices—an important development given that the IDC estimates 152,000 new Internet of Things (IoT) devices per minute by 2025. Put it together, and 5G is a game-changing backhaul for public networks. (Wi-Fi 6, often mentioned in the same breath as 5G, is generally used for private WANs.

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Application Storage, Data Storage

How Managed Service Providers Drive Management in HCI

Article | July 12, 2023

Driving excellence in HCI: Unveil the crucial role of managed service providers in deploying and managing Hyper-Converged Infrastructure for optimal performance and efficiency for smooth functioning. Contents 1. Introduction 2. Role of MSPs in Deployment of HCI 3. Role of MSPs in HCI’s Management 4. Key Areas Where MSPs Help Drive Efficient HCI 4.1. Expert Deployment and Configuration 4.2. Proactive Monitoring and Management 4.3. Performance Optimization 4.4. Security and Compliance 4.5. Patch Management and Upgrades 4.6. Scalability and Flexibility 4.7. Cost Optimization 4.8. 24/7 Support and Incident Management 5. Takeaway 1. Introduction Fundamentally, a hyper-converged infrastructure comprises virtual computing, virtual hyperconverged network, and virtual SAB. However, deploying this infrastructure is a complex procedure that requires skill and attention. A managed service provider (MSP) can assist a business in implementing hyper-converged infrastructure. These are service providers that specialize in managing and maintaining hyper-converged infrastructure environments on behalf of businesses. They offer proactive monitoring, maintenance, and troubleshooting services to ensure optimal performance & availability and management excellence in HCI. 2. Role of MSPs in Deployment of HCI Managed service providers play a crucial role in the successful deployment of Hyperconverged Infrastructure. With their expertise and experience, MSPs assist businesses in planning and designing the optimal HCI solution tailored to their needs. They manage the integration of hardware and software components, ensuring compatibility and seamless integration into the existing IT infrastructure. MSPs handle data migration and transition, minimizing downtime and data loss. They also optimize performance by fine-tuning configurations and resource allocations to achieve optimal HCI operation. MSPs prioritize security and compliance, implementing robust measures to protect sensitive data and ensure regulatory compliance. They provide ongoing management and support, monitoring system health, performing maintenance, and addressing issues promptly. MSPs enable scalability and future-proofing, helping businesses scale their HCI environment as needed and ensuring flexibility for future technology advancements and changes in business requirements. Broadly, MSPs bring their specialized knowledge and services to navigate the complexities of HCI deployment, enabling businesses to maximize the benefits of this transformative HCI technology. 3. Role of MSPs in HCI’s Management Managed service providers play a crucial role in the effective management of HCI. MSPs offer a range of services to ensure the optimal performance and security of HCI environments. They proactively monitor and maintain the HCI infrastructure, identifying and addressing issues before they impact operations. MSPs specialize in performance optimization, fine-tuning configurations, and implementing load balancing techniques to maximize efficiency. They prioritize security and compliance by implementing robust measures and assisting with data backup and disaster recovery strategies. MSPs also assist with capacity planning and scalability, ensuring resources are efficiently allocated and businesses can adapt to changing demands. They provide 24/7 support, troubleshooting services, and comprehensive reporting and analytics for HCI management excellence. Additionally, MSPs handle vendor management, simplifying interactions with hardware and software providers. Overall, MSPs enable businesses to effectively manage their HCI environments, ensuring smooth operations, optimal performance, and security. 4. Key Areas Where MSPs Help Drive Efficient HCI Managed Service Providersplay a crucial role in driving deployment and management excellence in Hyperconverged Infrastructure (HCI) environments. HCI combines storage, compute, and networking into a single, software-defined platform, simplifying data center operations. Here's how MSPs contribute to HCI excellence: 1. Expert Deployment and Configuration MSPs possess deep expertise in HCI deployments. They understand the complexities of hardware, software, and networking integration required for optimal HCI implementation. MSPs ensure proper configuration, capacity planning, and performance tuning to maximize HCI efficiency and meet specific business needs. 2. Proactive Monitoring and Management MSPs provide proactive monitoring and management services, continuously monitoring the HCI environment to detect issues and resolve them before they impact performance or availability. They leverage advanced monitoring tools and technologies to monitor resource utilization, network connectivity, and storage performance, ensuring optimal HCI operation. 3. Performance Optimization MSPs specialize in fine-tuning HCI performance. They analyze workloads, assess resource requirements, and optimize configurations to ensure optimal performance and scalability. Through proactive capacity planning and performance optimization techniques, MSPs help businesses extract the maximum value from their HCI investment. 4. Security and Compliance MSPs prioritize security and compliance in HCI environments. They implement robust security measures, such as encryption, access controls, and threat detection systems, to protect critical data and ensure compliance with industry regulations. MSPs also assist businesses in implementing data backup and disaster recovery strategies to safeguard against potential data loss or system failures. 5. Patch Management and Upgrades MSPs handle patch management and upgrades in HCI environments. They ensure that the HCI platform stays up to date with the latest security patches and software updates, minimizing vulnerabilities and ensuring hyperconverged system stability. MSPs coordinate and execute seamless upgrades, minimizing disruptions and maintaining optimal HCI performance. 6. Scalability and Flexibility MSPs help businesses scale and adapt their HCI environments to meet changing demands. They assess growth requirements, optimize resource allocation, and implement expansion strategies to accommodate evolving business needs. MSPs enable businesses to scale their HCI infrastructure seamlessly without compromising performance or availability. 7. Cost Optimization MSPs assist in optimizing costs associated with HCI deployments. They evaluate resource utilization, identify inefficiencies, and implement cost-saving measures, such as workload consolidation and resource allocation optimization. MSPs help businesses achieve maximum return on investment by aligning HCI infrastructure with specific business objectives. 8. 24/7 Support and Incident Management MSPs offer round-the-clock support and incident management for HCI environments. They provide timely resolution of issues, minimizing downtime and ensuring continuous operation. MSPs also offer help desk services, ticket management, and proactive troubleshooting to address any challenges that arise in the HCI environment. 5. Takeaway The future of managed service providers is promising and dynamic. MSPs will continue to enhance their specialized expertise in HCI, offering comprehensive support for businesses' HCI environments. They will expand their services to include end-to-end managed hyperconverged solutions, covering deployment, ongoing management, performance optimization, and security. Automation and orchestration will play a significant role as MSPs leverage these technologies to streamline operations and improve efficiency. MSPs will also focus on strengthening security and compliance measures, integrating HCI with cloud services, and continuously innovating to stay ahead in the HCI landscape. Broadly, MSPs will be vital partners for businesses seeking to maximize the benefits of HCI while ensuring smooth operations and staying competitive in the digital era. MSPs in HCI offer specialized expertise, managed services, automation, AI-driven analytics, enhanced security and compliance, integration with hyper converged cloud services, and continuous innovation. Their services will cover the entire lifecycle of HCI, from deployment to ongoing management and optimization. MSPs will leverage automation and AI technologies to streamline operations, enhance security, and provide proactive monitoring and maintenance. They will assist businesses in integrating HCI with cloud services, ensuring scalability and flexibility. MSPs will continuously innovate to adapt to emerging technologies and industry trends, supporting businesses in harnessing the full potential of HCI and achieving their digital transformation goals.

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Hyper-Converged Infrastructure, IT Systems Management

Adapting to Changing Landscape: Challenges and Solutions in HCI

Article | September 14, 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

Advancing 5G with cloud-native networking and intelligent infrastructure

Article | December 15, 2021

The success of 5G technology is a function of both the infrastructure that supports it and the ecosystems that enable it. Today, the definitive focus in the 5G space is on enterprise use cases, ranging from dedicated private 5G networks to accessing edge compute infrastructure and public or private clouds from the public 5G network. As a result, vendor-neutral multitenant data center providers and their rich interconnection capabilities are pivotal in helping make 5G a reality. This is true both in terms of the physical infrastructure needed to support 5G and the ability to effectively connect enterprises to 5G. Industry experts expect 5G to enable emerging applications such as virtual and augmented reality (AR/VR), industrial robotics/controls as part of the industrial internet of things (IIoT), interactive gaming, autonomous driving, and remote medical procedures. These applications need a modern, cloud-based infrastructure to meet requirements around latency, cost, availability and scalability. This infrastructure must be able to provide real-time, high-bandwidth, low-latency access to latency-dependent applications distributed at the edge of the network. How Equinix thinks about network slicing Network slicing refers to the ability to provision and connect functions within a common physical network to provide the resources necessary to deliver service functionality under specific performance constraints (such as latency, throughput, capacity and reliability) and functional constraints (such as security and applications/services). With network slicing, enterprises can use 5G networks and services for a wide variety of use cases on the same infrastructure. Providing continuity of network slices with optimal UPF placement and intelligent interconnection Mobile traffic originates in the mobile network, but it is not contained to the mobile network domain, because it runs between the user app on a device and the server workload on multi-access edge compute (MEC) or on the cloud. Therefore, to preserve intended characteristics, the slice must be extended all the way to where the traffic wants to go. This is why we like to say “the slicing must go on.” The placement of network functions within the slice must be optimized relative to the intended traffic flow, so that performance can be ensured end-to-end. As a result, organizations must place or activate the user plane function (UPF) in optimal locations relative to the end-to-end user plane traffic flow. We expect that hybrid and multicloud connectivity will remain a key requirement for enterprises using 5G access. In this case, hybrid refers to private edge computing resources (what we loosely call “MEC”) located in data centers—such as Equinix International Business Exchange™ (IBX®) data centers—and multicloud refers to accessing multiple cloud providers from 5G devices. To ensure both hybrid and multicloud connectivity, enterprises need to make the UPF part of the multidomain virtual Layer 2/Layer 3 interconnection fabric. Because a slice must span multiple domains, automation of UPF activation, provisioning and virtual interconnection to edge compute and multicloud environments is critical. Implementing network slicing for interconnection of core and edge technology Equinix partnered with Kaloom to develop network slicing for interconnection of core and edge (NICE) technology within our 5G and Edge Technology Development Center (5G ETDC) in Dallas. NICE technology is built using cloud-native network fabric and high-performance 5G UPF from Kaloom. This is a production-ready software solution, running on white boxes built with P4 programmable application-specific integrated circuits (ASICs), allowing for deep network slicing and support for high-performance 5G UPF with extremely fast data transfer rates. With NICE technology in the 5G ETDC, Equinix demonstrates: 5G UPF deployment/activation and traffic breakout at Equinix for multiple slices. Software-defined interconnection between the 5G core and MEC resources from multiple providers. Software-defined interconnection between the 5G core and multiple cloud service providers. Orchestration of provisioning and automation of interconnection across the 5G core, MEC and cloud resources. Architecture of NICE technology in the Equinix 5G ETDC The image above shows (from left to right): The mobile domain with radio access network (RAN), devices (simulated) and mobile backhaul connected to Equinix. The Equinix domain with: Equinix Metal® supporting edge computing servers and a fabric controller from Kaloom. Network slicing fabric providing interconnection and Layer 2/Layer 3 cloud-native networking to dynamically activate UPF instances/interfaces connected with MEC environments and clouds, forming two slices (shown above in blue and red). Equinix Fabric™ and multicloud connectivity. This demonstrates the benefit of having the UPF as a feature of the interconnection fabric, effectively allowing UPF activation as part of the virtual fabric configuration. This ultimately enables high-performance UPF that’s suitable for use cases such as high-speed 5G fixed wireless access. Combining UPF instances and MEC environments into an interconnection fabric makes it possible to create continuity for the slices and influence performance and functionality. Equinix Fabric adds multicloud connectivity to slices, enabling organizations to directly integrate network slicing with their mobile hybrid multicloud architectures. Successful private 5G edge deployments deliver value in several ways. Primarily, they offer immediate access to locally provisioned elastic compute, storage and networking resources that deliver the best user and application experiences. In addition, they help businesses access a rich ecosystem of partners to unlock new technologies at the edge. Secure, reliable connectivity and scalable resources are essential at the edge. A multivendor strategy with best-of-breed components complemented by telemetry, advanced analytics with management and orchestration—as demonstrated with NICE in Equinix data centers—is a most effective way to meet those requirements. With Equinix’s global footprint of secure, well-equipped facilities, customers can maximize benefits.” - Suresh Krishnan, CTO, Kaloom Equinix and its partners are building the future of 5G NICE technology is just one example of how the Equinix 5G and Edge Technology Development Center enables the innovation and development of real-world capabilities that underpin the edge computing and interconnection infrastructure required to successfully implement 5G use cases. A key benefit of the 5G ETDC is the ability to combine cutting-edge innovations from our partners like Kaloom with proven solutions from Equinix that already serve a large ecosystem of customers actively utilizing hybrid multicloud architectures.

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Spotlight

Clariba

Clariba, recognised as one of the leading SAP partners in EMEA, is an independent, expert analytics consultancy that provides industry-focused solutions for enterprise performance management, business intelligence and organisational alignment. Our mission is to deliver innovative, reliable and high-quality business analytics solutions, providing our customers with clarity and actionable insight to improve their business performance and develop new business models for long-term, sustainable competitive advantage.

Related News

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.

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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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