Hyper-Converged Infrastructure
Business Wire | September 26, 2023
Cloudflare, Inc. (NYSE: NET), the security, performance, and reliability company helping to build a better Internet, today shared a new independent report published by Analysys Mason that shows switching enterprise network services from on premises devices to Cloudflare’s cloud-based services can cut related carbon emissions up to 78% for very large businesses to up to 96% for small businesses. The report is one of the first of its kind to calculate potential emissions savings achieved by replacing enterprise network and security hardware boxes with more efficient cloud services.
Global Internet usage accounts for 3.7% of global CO2 emissions, about equal to the CO2 emissions of all air traffic around the world. The Internet needs to reduce its overall energy consumption, especially as regulators continue to implement the Paris Climate Accord, including plans to transition to a zero emissions economy. The European Climate Law requires that Europe’s economy and society become climate-neutral by 2050, with a target of reducing net GHG emissions by at least 55% by 2030, compared to 1990 levels. Regulators in the United States and the European Union, among others, have also announced plans to require companies to disclose climate-related information including carbon emissions resulting from their operations and supply chains, as well as climate related risks and opportunities. Finally, among the Fortune Global 500, 63% of companies now set 2050 targets for emissions reductions. Companies large and small will increasingly be looking to reduce carbon throughout their supply chains, particularly their IT infrastructure.
“The best way to reduce your IT infrastructure’s carbon footprint is easy: move to the cloud,” said Matthew Prince, CEO and co-founder, Cloudflare. “At Cloudflare, we’ve built one of the world’s most efficient networks, getting the most out of every watt of energy and every one of our servers. That’s why, with Cloudflare, companies can help hit their sustainability goals without sacrificing security, speed, performance, or innovation.”
The Analysys Mason study found that switching enterprise network services from on premises devices to Cloudflare services can cut related carbon emissions up to 96%, depending on the current network footprint. The greatest reduction comes from consolidating services, which improves carbon efficiency by increasing the utilization of servers that are providing multiple network functions. On premises devices are designed to host multiple workloads and consume power constantly, but are only used for part of the day and part of the week. Cloud infrastructure is shared by millions of customers, often all over the world. As a result, cloud providers are able to achieve economies of scale that result in less downtime, less waste, and lower emissions. Furthermore, the Analysys Mason study found that there are additional gains due to the high Power Usage Effectiveness of cloud data centres, and differences in the carbon intensity of generation in the local electricity grid.
“Happy Cog is a full-service digital agency that designs, builds, and markets experiences that engage our clients and their audiences. We’ve relied on Cloudflare for many of those websites and apps because it's secure, reliable, fast, and affordable – but also aligns with many of our clients’ sustainability roadmaps and goals,” said Matt Weinberg, Co-Founder and President of Technology at Happy Cog. “Switching our clients from their previous on premises or other constant-usage infrastructure to Cloudflare's network and services has let them be greener, more efficient, and more cost effective. It's ideal when you can offer your clients a solution that covers all their needs and provides a delightful experience now, without having to compromise on their longer term priorities.”
About Cloudflare
Cloudflare, Inc. (www.cloudflare.com / @cloudflare) is on a mission to help build a better Internet. Cloudflare’s suite of products protect and accelerate any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare have all web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was awarded by Reuters Events for Global Responsible Business in 2020, named to Fast Company's Most Innovative Companies in 2021, and ranked among Newsweek's Top 100 Most Loved Workplaces in 2022.
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Hyper-Converged Infrastructure
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
Business Wire | September 21, 2023
Astera Labs, the global leader in semiconductor-based connectivity solutions for AI infrastructure, today announced that its Leo Memory Connectivity Platform enables data center servers with unprecedented performance for memory intensive workloads. Leo is the industry’s first Compute Express Link™ (CXL™) memory controller that increases total server memory bandwidth by 50% while also decreasing latency by 25% when integrated with the forthcoming 5th Gen Intel® Xeon® Scalable Processor. Through new hardware-based interleaving of CXL-attached and CPU native memory, Astera Labs and Intel eliminate any application-level software changes to augment server memory resources via CXL. Existing applications can effortlessly “plug-and-play” to take advantage of the highest possible memory bandwidth and capacity in the system.
“The growth of computing cores and performance has historically outpaced memory throughput advancements, resulting in degraded server performance efficiency over time,” said Sanjay Gajendra, COO of Astera Labs. “This performance scaling challenge has led to the infamous ‘memory wall,’ and thanks to our collaboration with Intel, our Leo Memory Connectivity Platform breaks through this barrier by delivering on the promise of PCIe 5.0 and CXL memory.”
Data center infrastructure scaling limitations due to the memory wall are none more evident than in AI servers where memory bandwidth and capacity bottlenecks result in inefficient processor utilization. The CXL innovations delivered by Astera Labs and Intel directly address these bottlenecks and lay the foundation for cloud, hybrid-cloud and enterprise data centers to maximize accelerated computing performance.
Extending leadership of PCIe® 5.0 and CXL 2.0 solutions
Astera Labs has a history of delivering industry-first solutions that are critical to advancing the PCIe and CXL ecosystems. In addition to memory performance advancements with Leo, Astera Labs is also driving interoperability leadership with its Aries PCIe 5.0 / CXL 2.0 Smart Retimers on state-of-the-art Intel server platforms. As the most widely deployed and proven PCIe/CXL retimer family in the industry, Aries features a low-latency CXL mode that complements Leo to form the most robust CXL memory connectivity solution.
“We applaud Astera Labs for their contributions to the CXL ecosystem and are delighted to extend our ongoing collaboration. We believe Memory Connectivity Platforms containing innovations from companies like Astera Labs will help deliver enhanced performance on next generation Intel Xeon processors, and accelerate a myriad of memory intensive workloads,” said Zane Ball, Corporate Vice President and General Manager, Data Center Platform Engineering and Architecture Group, Intel.
Visit Astera Labs at Intel Innovation!
Astera Labs will showcase Leo and Aries together with Intel’s latest Xeon® Scalable processors at Booth #210, September 19-20 at the San Jose Convention Center. Talk to Astera Labs’ experts to learn more about industry benchmarks and how to optimize PCIe/CXL memory solutions in data center architectures to deliver optimized performance for applications ranging from AI, real time analytics, genomics and modeling.
About Astera Labs
Astera Labs, Inc. is a global leader in semiconductor-based connectivity solutions purpose-built to unleash the full potential of intelligent data infrastructure at cloud-scale. Its class-defining, first-to-market products based on PCIe, CXL, and Ethernet technologies deliver critical connectivity in accelerated computing platforms optimized for AI applications.
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