Consumer Vs. Data Center SSDs – Which is Right for You?

Increase performance and reliabilty with Samsung Enterprise SSDs Learn more SOLID STATE DRIVES VS.CONSUMER - CLASS DATA CENTER - CLASS Known for their durability, reliability and lightning-fast transactional performance, solid state drives (SSDs) are increasingly being integrated into organizations' data management strategies. But not all SSDs are created equal. distinctions between them, and why you should consider investing in data center-class SSDs to protect your high-value data. Which is right for your organization? Engineered for vastly different performance demands Consumer-class SSDs are designed primarily as replacements for hard disk drives (HDDs) in PCs and are intended for more sporadic use. On the other hand, data center-class SSDs are built to sustain rapid, 24/7 responsiveness. Which type of SSD is right for your environment? Latency increases as workloads increase Generally sit idle, and are built for peak performance with short bursts of speed workload capabilities Lower latency Designed for sustained performance Consumer-class Data center-class Mixed workload I/O Reduced power consumption Built-in power-loss protection Consistent input/output operations @ + - capabilities vary widely Data corruption and loss caused by power outages or overheating can halt business operations. Data centers are Increased risk of data loss or corruption during OS crashes, power failures, and overheating Not designed for 24/7 operation and will drop performance during extended write operations Overprovisioning.

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Hyper-Converged Infrastructure

The importance of location intelligence and big data for 5G growth

Article | October 10, 2023

The pandemic has had a seismic impact on the telecom sector. This is perhaps most notably because where and how the world goes to work has been re-defined, with nearly every business deepening its commitment to mobility. Our homes suddenly became our offices, and workforces went from being centrally managed to widely distributed. This has called for a heightened need for widespread, secure and high-speed connectivity around the clock. 5G has answered the call, and 5G location intelligence and big data can provide service providers with the information they need to optimize their investments. Case in point: Juniper Research reported in its 5G Monetization study that global revenue from 5G services will reach $73 billion by the end of 2021, rising from just $20 billion last year. 5G flexes as connected devices surge Market insights firm IoT Analytics estimates there will be more than 30 billion IoT connections by 2025. That's an average of nearly four IoT devices per person. To help meet the pressure this growth in connectivity is putting on telecom providers, the Federal Communications Commission (FCC) is taking action to make additional spectrum available for 5G services and promoting the digital opportunities it provides to Americans. The FCC is urging that investments in 5G infrastructure be prioritized given the "widespread mobility opportunity" it presents, as stated by FCC Chairwoman Jessica Rosenworcel. While that's a good thing, we must also acknowledge that launching a 5G network presents high financial risk, among other challenges. The competitive pressures are significant, and network performance matters greatly when it comes to new business acquisition and retention. It's imperative to make wise decisions on network build-out to ensure investments yield the anticipated returns. Thus, telcos need not – and should not – go it blindly when considering where to invest. You don't know what you don't know, which is why 5G location intelligence and big data can provide an incredible amount of clarity (and peace of mind) when it comes to optimizing investments, increasing marketing effectiveness and improving customer satisfaction. Removing the blindfold Location data and analytics provide telcos and Communications Service Providers (CSPs) with highly-specific insights to make informed decisions on where to invest in 5G. With this information, companies can not only map strategic expansion, but also better manage assets, operations, customers and products. For example, with this intelligence, carriers can gain insight into the most desired locations of specific populations and how they want to use bandwidth. They can use this data to arm themselves with a clear understanding of customer location and mobility, mapping existing infrastructure and competitive coverage against market requirements to pinpoint new opportunities. By creating complex customer profiles rich with demographic information like age, income and lifestyle preferences, the guesswork is eliminated for where the telco should or shouldn’t deploy new 5G towers. Further, by mapping a population of consumers and businesses within a specific region and then aggregating that information by age, income or business type, for example, a vivid picture comes to life of the market opportunity for that area. This type of granular location intelligence adds important context to existing data and is a key pillar to data integrity, which describes the overall quality and completeness of a dataset. When telcos can clearly understand factors such as boundaries, movement and the customers’ surroundings, predictive insights can be made regarding demographic changes and future telecom requirements within a certain location. This then serves as the basis for a data-backed 5G expansion strategy. Without it, businesses are burdened by the trial-and-error losses that are all too common with 5G build-outs. Location precision's myriad benefits Improved location precision has many benefits for telcos looking to pinpoint where to build, market and provision 5G. Among them are: Better data: Broadening insights on commercial, residential and mixed-use locations through easy-to-consume, scalable datasets provide highly accurate in-depth analyses for marketing and meeting customer demand. Better serviceability insights: Complete and accurate location insights allow for a comprehensive view of serviceable addresses where products and services can be delivered to current and new customers causing ROI to improve and customers to be adequately served. Better subscriber returns: Companies that deploy fixed wireless services often experience plan cancellations due to inconsistencies of signal performance, which typically result from the misalignment of sites with network assets. Location-based data provides operators with the ability to adapt their networks for signal consistency and serviceability as sites and structures change. The 5G future The role of location intelligence in accelerating development of new broadband services and driving ROI in a 5G world cannot be overstated. It adds a critical element of data integrity that informs network optimization, customer targeting and service provisioning so telecom service providers can ensure their investments are not made with blind hope.

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Application Infrastructure, Application Storage

Orchestration of Infrastructure in a Hybrid Environment

Article | July 19, 2023

The cloud has dispelled many myths and self-made barriers during the past ten years. The utilization of cloud infrastructure keeps proving the innovators right. The cloud has experienced tremendous adoption, leading to the development of our most pervasive - and disorderly - IT infrastructure systems. This move calls for a new level of infrastructure orchestration to manage the complexity of changing hybrid systems. There are many challenges involved in moving from an on-premises-only architecture to a cloud environment. IT operations teams must manage a considerably more complex overall environment due to this hybrid IT approach. Because of the variable nature of the cloud, IT directors have discovered fast that what worked to manage on-premises infrastructures may not always be applicable. Utilize Infrastructure as Code Tools to Provide Cloud Infrastructure as a Service IT has traditionally managed infrastructure orchestration and automation for business tools and platforms. Service orchestration and automation platforms (SOAPs) let non-IT workers turn on and off cloud infrastructure while IT maintains control. End-users are empowered with automated workflows that spin up infrastructure on-demand instead of opening a ticket for every request and waiting on the helpdesk or cloud service team. Automation benefits both end-users and ITOps. Users gain speed, and IT decides which cloud provider and how much cloud infrastructure is used. Give End Users Access to Code, Low Code, or No Code Modern SOAP lets citizen automators access workflow automation by preference or competence. SOAPs allow end-users to utilize code or no-code, depending on their preference. SOAPs let end-users access automation through Microsoft Teams, Slack, and ServiceNow. Developers and technical team members can access the platform's scripts and code. As enterprises outgrow their legacy systems, infrastructure orchestration solutions become essential. Using a service orchestration and automation platform is one way to manage complicated infrastructures. SOAPs are built for hybrid IT environments and will help organizations master multi-cloud and on-premises tools.

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Hyper-Converged Infrastructure

Implementation of IaaS Container Security for Confidentiality and Integrity

Article | September 14, 2023

Containers have emerged as a choice for deploying and scaling applications, owing to their lightweight, isolated, and portable nature. However, the absence of robust security measures may expose containers to diverse threats, thereby compromising the confidentiality and integrity of data and apps. Contents 1 Introduction 2 IaaS Container Security Techniques 2.1 Container Image Security 2.2 Host Security 2.3 Network Security 2.4 Data Security 2.5 Identity and Access Management (IAM) 2.6 Runtime Container Security 2.7 Compliance and Auditing 3 Conclusion 1. Introduction Infrastructure as a Service has become an increasingly popular way of deploying and managing applications, and containerization has emerged as a leading technology for packaging and deploying these applications. Containers are software packages that include all the necessary components to operate in any environment. While containers offer numerous benefits, such as portability, scalability, and speed, they also introduce new security challenges that must be addressed. Implementing adequate IaaS container security requires a comprehensive approach encompassing multiple layers and techniques. This blog explores the critical components of IaaS container security. It provides an overview of the techniques and best practices for implementing security measures that ensure the confidentiality and integrity of containerized applications. By following these, organizations can leverage the benefits of IaaS and containerization while mitigating the security risks that come along. 2. IaaS Container Security Techniques The increasing IAAS security risks and security issues associated with IAAS these days are leading to a massive data breach. Thus, IAAS security concerns are taken into consideration, and seven best techniques are drafted below. 2.1. Container Image Security: Container images are the building blocks of containerized applications. Ensuring the security of these images is essential to prevent security threats. The following measures are used for container image security: Using secure registries: The registry is the location where container images are stored and distributed. Usage of centrally managed registries on campus, the International Organization for Standardization (ISO) can scan them for security issues and system managers may simply assess package gaps, etc. Signing images: Container images can be signed using digital signatures to ensure their authenticity. Signed images can be verified before being deployed to ensure they have not been tampered with. Scanning images: Although standard AppSec tools such as Software Composition Analysis (SCA) can check container images for vulnerabilities in software packages and dependencies, extra dependencies can be introduced during the development process or even at runtime. 2.2. Host Security: Host security is a collection of capabilities that provide a framework for implementing a variety of security solutions on hosts to prevent attacks. The underlying host infrastructure where containers are deployed must be secured. The following measures are used for host security: Using secure operating systems: The host operating system must be safe and up-to-date with the latest high severity security patches within 7 days of release, and others, within 30 days to prevent vulnerabilities and security issues. Applying security patches: Security patches must be applied to the host operating system and other software packages to fix vulnerabilities and prevent security threats. Hardening the host environment: The host environment must be hardened by disabling unnecessary services, limiting access to the host, and applying security policies to prevent unauthorized access. 2.3. Network Security: Network security involves securing the network traffic between containers and the outside world. The following measures are used for network security: Using Microsegmentation and firewalls: Microsegmentation tools with next-gen firewalls provide container network security. Microsegmentation software leverages network virtualization to build extremely granular security zones in data centers and cloud applications to isolate and safeguard each workload. Encryption: Encryption can protect network traffic and prevent eavesdropping and interception of data. Access control measures: Access control measures can restrict access to containerized applications based on user roles and responsibilities. 2.4. Data Security: Data stored in containers must be secured to ensure its confidentiality and integrity. The following measures are used for data security: Using encryption: Data stored in containers can be encrypted, using Transport Layer Security protocol version 1.1. (TLS 1.1) or higher, to protect it from unauthorized access and prevent data leaks. All outbound traffic from private cloud should be encrypted at the transport layer. Access control measures: Access control measures can restrict access to sensitive data in containers based on user roles and responsibilities. Not storing sensitive data in clear text: Sensitive data must not be stored in clear text within containers to prevent unauthorized access and data breaches. Backup app data, atleast weekly. 2.5. Identity and Access Management (IAM): IAM involves managing access to the container infrastructure and resources based on the roles and responsibilities of the users. The following measures are used for IAM: Implementing identity and access management solutions: IAM solutions can manage user identities, assign user roles and responsibilities, authenticate and provide access control policies. Multi-factor authentication: Multi-factor authentication can add an extra layer of security to the login process. Auditing capabilities: Auditing capabilities can monitor user activity and detect potential security threats. 2.6. Runtime Container Security: To keep its containers safe, businesses should employ a defense-in-depth strategy, as part of runtime protection. Malicious processes, files, and network activity that deviates from a baseline can be detected and blocked via runtime container security. Container runtime protection can give an extra layer of defense against malicious code on top of the network security provided by containerized next-generation firewalls. In addition, HTTP layer 7 based threats like the OWASP Top 10, denial of service (DoS), and bots can be prevented with embedded web application and API security. 2.7. Compliance and Auditing: Compliance and auditing ensure that the container infrastructure complies with relevant regulatory and industry standards. The following measures are used for compliance and auditing: Monitoring and auditing capabilities: Monitoring and auditing capabilities can detect and report cloud security incidents and violations. Compliance frameworks: Compliance frameworks can be used to ensure that the container infrastructure complies with relevant regulatory and industry standards, such as HIPAA, PCI DSS, and GDPR. Enabling data access logs on AWS S3 buckets containing high-risk Confidential Data is one such example. 3. Conclusion IaaS container security is critical for organizations that rely on containerization technology for deploying and managing their applications. There is likely to be an increased focus on the increased use of AI and ML to detect and respond to security incidents in real-time, the adoption of more advanced encryption techniques to protect data, and the integration of security measures into the entire application development lifecycle. In order to stay ahead of the challenges and ensure the continued security of containerized applications, the ongoing process of IaaS container security requires continuous attention and improvement. By prioritizing security and implementing effective measures, organizations can confidently leverage the benefits of containerization while maintaining the confidentiality and integrity of their applications and data.

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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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Spotlight

Saudi Information Technology Company

We are established for enriching local content in designing, developing, and operating secure digital computing environments through Saudi talents, by fulfilling our role as a premier consultant and provider of secure digital services and solutions for major and vital government and private entities. We are providing the latest professional services for cybersecurity and solutions, software development, data science and data analysis, in collaboration with pioneering national and international partners in the technology ecosystem.

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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