Machine Learning: Increasing GPU Utilization & Payback
Artificial intelligence, deep learning, and machine learning are gaining rapid adoption across industries from autonomous vehicles, healthcare and life sciences, financial services, manufacturing, and retail. Every day, organizations are realizing the economic value of data and analytics to gain a competitive edge.
There are many phases to the machine learning/deep learning pipeline, each with its own demands on the underlying IT infrastructure. For example, data can be ingested from myriad sources such as big data stores, SQL and NoSQL databases, and real-time streaming systems impacting capacity and throughput requirements, while data preparation and cleaning requires extremely high I/O and model training demands high-speed reads of large volumes of random small blocks.
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Introducing the Composable DataCenter
Throughout the evolution of data center IT infrastructure, one thing has remained constant. Once deployed, compute, storage and networking systems remain fixed and inflexible. The move to virtual machines better utilized the resources on the host system they were tied to, but virtual machines didn’t make data centers more dynamic or adaptable.
In the era of data-intensive computing, IT needs to find ways to quickly adapt to new workloads and ever growing data. This has many people talking about software-defined solutions. When software is pulled out of proprietary hardware, whether it’s compute, storage or networking hardware, then flexibility is increased and costs are reduced. But when the software-defined solution comes as a highly integrated stack (as it does with HCI), it undermines that key value. With next-generation composable infrastructure, software-defined takes on new meaning. For the first time, IT can create and recreate logical hardware through software. And the benefits are enormous.
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Get off your DAS!
Data centers have been populated with DAS (direct attached storage) servers for decades.These pre-configured servers performed well for traditional applications with predictable capacity needs. But the fastest growing class of dynamic new applications – data-intensive applications that drive analytics, AI, ML and web scale deployments – make it more difficult to predict resource requirements. It has become clear that DAS servers have many limitations and costly drawbacks. Read about how Composable Infrastructure meets the needs of data-intensive applications.
DriveScale’s Drive Encryption Overview
This white paper explains the security architecture of the drive encryption feature of the DriveScale Composable Platform. It details the encryption and authentication technologies to secure the application data and prevent the unauthorized access to the application data in flight and at rest. The audience of the whitepaper is presumed to be familiar with data center computing, and security principles including cryptography with no prior knowledge of DriveScale’s solution.
A Better Public Cloud Service for Modern Workloads
Composable Infrastructure delivers a cost effective, high performance platform for Big Data in the cloud. How can a public cloud provider offer a big data infrastructure that delivers bare metal, dedicated instance performance while also allowing customers to easily scale up or down as needed and capture the economic advantage of a shared resource?
In this white paper you will learn how Composable Infrastructure delivers the performance of dedicated bare metal clusters, but with the elasticity and cost effectiveness public cloud providers have come to rely upon.
Composable Infrastructure for Clusters
The developers of the Hadoop/Big Data Architecture at Google and then at Yahoo were looking to design a platform that could store and process a vast quantity of data at low cost. To achieve this, they developed several key principles around system architecture that Enterprises need to follow to achieve the goals of Big Data applications such as Hadoop, Spark, Cassandra, etc.:
Falling Out of the Clouds: When Your Big Data Needs a New Home
Running your Big Data Analytics in the Cloud? Is it becoming expensive? In this informative white paper for Data Center professionals, get exclusive insights into some of the tradeoffs of running Big Data workloads in the cloud and guidelines for when to bring it in-house. While AWS and other cloud providers can appear to offer flexibility, production Hadoop frameworks can suffer in the cloud due to lack of control, predictability and high costs.
This white paper will help you better understand when and why you may choose to move your Big Data out of the cloud, and offers insights on how to avoid the long deployment times and costs of managing your own clusters. You’ll discover how to protect your hardware investment, improve predictability, and gain increased scalability by leveraging a unique approach that separates compute and storage resources.