Application Infrastructure

5G FWA, the Fastest Growing Residential Broadband Service, to Exceed 58 Million Subscriptions in 2026

The worldwide residential broadband market reached a subscriber's base of over 1.1 billion in 2020, a 4% increase from the previous year. Not surprisingly, the COVID-19 pandemic accelerated demand for broadband connectivity. The need for high-capacity residential broadband will remain strong, even after the pandemic recovery. According to global tech market advisory firm ABI Research, 5G Fixed Wireless Access (FWA) will be the fastest-growing residential broadband segment to increase at a CAGR of 71%, exceeding 58 million subscribers in 2026.

Remote working, online learning, e-commerce, and virtual healthcare drove high-speed broadband demand throughout 2020. The significant increase in the use of internet-based home entertainment such as video streaming and online gaming also pushed existing broadband users to upgrade their broadband service to a higher-tier package, while households without broadband access signed up for new subscriptions. "Increasing adoption of internet-connected devices, smart TVs, and smart home devices, as well as consumers' media consumption through internet applications, will continue to drive high-speed broadband adoption in the years to come. In addition, many businesses are allowing remote working for some of their employees after the pandemic, which will boost the need for home broadband services even further," explains Khin Sandi Lynn, Industry Analyst at ABI Research.

To fulfill demand, broadband operators are investing heavily in expanding higher-capacity broadband networks. While some cable operators continue to invest in and upgrade to the DOCSIS 3.1 specification, the cable standardization body, CableLabs, and other industry players are already working toward DOCSIS 4.0 technology. "Although cable companies don't anticipate the need to deploy the new cable standard any time soon, Comcast has completed a lab test of DOCSIS 4.0 full-duplex system-on-chip from Broadband in April 2021. Cable companies are likely to stretch the life of the existing DOCSIS 3.1 standard for a few more years. However, DOCSIS 4.0 can support speeds of up to 10 Gbps downstream and 6 Gbps upstream, enabling improved customer experiences as well as the use of AR/VR or bandwidth-demanding services, which will certainly emerge in the future," says Lynn.

Similarly, telcos continue to upgrade their xDSL to Fiber-to-the-Home (FTTH) networks. In addition, FWA services are a cost-effective alternative when the deployment of a high-speed fixed broadband network is not economically feasible. Ongoing 5G network deployment alongside the development of extended 5G mmWave solutions will allow service providers to offer high-speed 5G FWA services in both urban and low-density areas. 5G FWA services are expected to represent 4% of residential broadband services in 2026, growing from less than 1% in 2020.

As residential broadband penetration saturates mature markets, competition among broadband operators is likely to create challenges to maintain market shares. "In addition to network upgrades, broadband operators need to invest in cutting-edge software and hardware to optimize network performance and support better user experiences. Providing advanced home networking devices, internet security, and home network self-diagnosis tools can help service providers reduce churn and improve average revenue per user," Lynn concludes.

These findings are from ABI Research's Pay TV and Residential Broadband Subscriptions market data report. This report is part of the company's Consumer Technologies research service, which includes research, data, and ABI Insights. Market Data spreadsheets are composed of deep data, market share analysis, and highly segmented, service-specific forecasts to provide detailed insight where opportunities lie.

About ABI Research
ABI Research provides actionable research and strategic guidance to technology leaders, innovators, and decision makers around the world. Our research focuses on the transformative technologies that are dramatically reshaping industries, economies, and workforces today. ABI Research's global team of analysts publish groundbreaking studies often years ahead of other technology advisory firms, empowering our clients to stay ahead of their markets and their competitors.

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Storage Management

SoftIron Recognized as a Sample Vendor in Gartner Hype Cycle for Edge Computing

GlobeNewswire | October 25, 2023

SoftIron, the worldwide leader in private cloud infrastructure, today announced it has been named as a Sample Vendor for the “Gartner Hype Cycle for Edge Computing, 2023.” Gartner Hype Cycle provides a view of how a technology or application will evolve over time, providing a sound source of insight to manage its deployment within the context of your specific business goals. The five phases of a Hype cycle are innovation trigger, Peak of Inflated Expectations, Trough of Disillusionment, Slope of Enlightenment and the Plateau of Productivity. SoftIron is recognized in the Gartner report as a Sample Vendor for Edge Storage and the report defines the technology as those that enable the creation, analysis, processing and delivery of data services at, or close to, the location where the data is generated or consumed, rather than in a centralized environment. Gartner predicts that infrastructure and operations (I&O) leaders are beginning the process of laying out a strategy for how they intend to manage data at the edge. Although I&O leaders embrace infrastructure as a service (IaaS) cloud providers, they also realize that a significant part of the infrastructure services will remain on-premises, and would require edge storage data services. Gartner Hype Cycles provide a graphic representation of the maturity and adoption of technologies and applications, and how they are potentially relevant to solving real business problems and exploiting new opportunities. Gartner Hype Cycle methodology gives you a view of how a technology or application will evolve over time, providing a sound source of insight to manage its deployment within the context of your specific business goals. The latest Gartner Hype Cycle analyzed 31 emerging technologies and included a Priority Matrix that provides perspective on the edge computing innovations that will have a bigger impact, and those that might take longer to fully mature. “We are excited to be recognized in the 2023 Garter Hype Cycle for Edge Computing,” said Jason Van der Schyff, COO at SoftIron. “We believe at SoftIron to be well positioned to help our customers address and take advantage of the latest trends and developments in Edge Computing as reported in Gartner’s Hype Cycle.”

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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. 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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

Astera Labs First to Break Through the Memory Wall with Industry’s Highest Performance CXL Memory Controllers

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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