Here you will find the latest information and insights on storage virtualization, parallel I/O, software-defined storage, hyper-convergence and the virtual world in general.

VMblog DataCore Predictions: Parallel Processing Software Will be a 'Productivity Disrupter' and Game Changer in 2017

by SVM Press Agency | Jan 19, 2017

VMblog 2017 prediction

VMblog 2017 Virtualization and Cloud Prediction 
Contributed by George Teixeira, President and CEO, DataCore Software

With so much computing power still sitting idle - despite all of the incredible technology advancements that have occurred - in 2017, the time is right for parallel processing software to go mainstream and unleash the immense processing power of today's multicore systems to positively disrupt the economic and productivity impact of what computing can do and where it can be applied.

New software innovations will make 2017 a breakout year for parallel processing. The key is that the software has to become simple to use and non-disruptive to applications to allow it to move from specialized use cases to general application usage. By doing so, the impact of this will be massive because application performance, enterprise workloads and greater consolidation densities on virtual platforms and in cloud computing that have been stifled by the growing gap between compute and I/O will no longer be held back. This will be realized with new parallel I/O software technologies now available that are easy to use, require no changes to the applications and are capable of fully leveraging the power of multicores to dramatically increase productivity and overcome the I/O bottleneck that has been holding back our industry; this is the catalyst of change.

Parallel processing software can now go beyond the realm of specialized uses such as HPC and areas like genomics that have focused primarily on computation, and impact the broader world of applications that require real-time responses and interactions. This includes mainstream applications and storage that drive business transactions, cloud computing, databases, data analytics, as well as the interactive worlds of machine learning and the Internet of Things (IoT).

The real driver of change is the economic and productivity disruption. Today, many new applications such as analytics are not practical because they require hundreds if not thousands of servers to get the job done; yet each server is becoming capable of supporting hundreds of multi-threading computing cores, all available to drive workloads that until now have sat there idle, waiting for work to do. We are ushering in an era where one server will do the work of 10 -- or 100 servers -- of the past. This will be the result of parallel processing software that unlocks the full utilization of multicores, leading to a revolution in productivity and making a new world of applications affordable to mainstream IT in 2017.

The Impact on Real-time Analytics and Big Data Performance will be Profound

The combination of faster response times and the multiplying impact on productivity through parallelization will fuel the next step forward in ‘real-time' analytics, big data and database performance. DataCore sees this as the next step forward in 2017. Our background in parallel processing, real-time I/O and software-defined storage has made our company uniquely well positioned to take advantage of the next big challenge in a world that requires the rate and amount of interactions and transactions to happen at a far faster pace with much faster response times.

The ability to do more work by doing it in parallel -- and to react quickly -- is the key. DataCore sees itself as helping to drive the step function change needed to make real-time analytics and big data performance practical and affordable. The implications on productivity and business decision making based on insights from data in areas such as financial, banking, retail, fraud detection, healthcare, and genomics, as well as machine learning and Internet of Things type applications, will be profound.

The Microsoft Impact Arrives: Azure Stack, Hybrid Cloud, Windows and SQL Server 2016

The success and growth of Microsoft's Azure Cloud has already become evident, however the real impact is the larger strategy of how Microsoft has worked to reconcile the world of on-premise and cloud computing. Microsoft was one of the first cloud vendors to recognize that the world is not just public clouds but that it will continue to be a mix of on-premise and cloud. Microsoft's Azure Stack continues to advance in making it seamless to get the benefits of cloud-like computing whether in the cloud or within a private cloud. It has become the model for hybrid cloud computing. Likewise, Microsoft continues to further integrate its Windows and server solutions to work more seamlessly with cloud capabilities.

While Windows and Azure get most of the attention, one of the most dramatic changes at Microsoft has been how it has reinvented and transformed its database offerings into a true big data and analytics platform for the future. It is time to take another look at SQL Server 2016; it is far more powerful and capable, and now deals with all types of data. As a platform, it is primed to work with Microsoft's large eco-system of marketplace partners, including DataCore with its parallel processing innovations, to redefine what is possible in the enterprise, the cloud, and with big data performance and real-time analytic use cases for traditional business applications, as well as new developing use cases in machine learning, cognitive computing and the Internet of Things.

Storage has Transformed; It's Servers + Software-Defined Infrastructure!

We are the midst of an inevitable and increasing trend in which servers are defining what storage is. Escalating this trend DataCore used parallel I/O software technologies to power off-the-shelf multicore servers to drive the world's fastest storage systems in terms of performance, lowest latencies and best price-performance. Traditional storage systems can no longer keep up and are on the decline, and as a result, are increasingly being replaced by commodity servers and software-defined infrastructure solutions that can leverage their power to solve the growing data storage problem. The storage function and associated data services are now being driven by software and becoming another "application workload" running on these cost-efficient server platforms, and this wave of flexible server-based storage systems are already having a disruptive industry impact.

Marketed as server-SANs, virtual SANs, web-scale, scale-out and hyper-converged systems, they are a collection of standard off-the-shelf servers, flash cards and disk drives - but it is the software that truly defines their value differentiation. Storage has become a server game. Parallel processing software and the ability to leverage multicore server technology is the major game-changer. In combination with software-defined infrastructure, it will lead to a productivity revolution and further solidify "servers as the new storage." For additional information, see the following report: http://wikibon.com/server-san-readies-for-enterprise-and-cloud-domination/

What's Beyond Flash?

Remember when flash was the next big thing? Now it's here. What is the next step -- how do we go faster and do more with less? The answer is obvious; if flash is now here and yet performance and productivity are still an issue for many enterprise applications especially database use cases, then we need to parallelize the I/O processing. Why? It multiplies what can be done as a result of many compute engines working in parallel to process and remove bottlenecks and queuing delays higher up in the stack, near the application, so we avoid as much device level I/O as possible and drive performance and response times far beyond any single device level optimization that flash/SSD alone can deliver. The power of the ‘many' far exceed what only ‘one' can do - combining flash and parallel I/O enables users to drive more applications faster, do more work and open up applications and use cases that have been previously impossible to do.

Going Beyond Hyper-Convergence: Hyper-Productivity is the Real Objective

As 2017 progresses, hyper-converged software will continue to grow in popularity but to cement its success, users will need to be able take full advantage of its productivity promise. The incredible power of parallel processing software will enable users to take advantage of what their hardware and software can do (see this video from ESG as an example).

Hyper-converged systems today are in essence a server plus a software-defined infrastructure, but often they are severely restricted in terms of performance and use cases and too often lack needed flexibility and a path for integration within the larger IT environment (for instance not supporting fibre channel, which often is key to enterprise and database connectivity). Powerful software-defined storage technologies that can do parallel I/O effectively provide a higher level of flexibility and leverage the power of multicore servers so fewer nodes are needed to get the work done, making them more cost-effective. Likewise, the software can incorporate existing flash and disk storage without creating additional silos; migrate and manage data across the entire storage infrastructure; and effectively utilize data stored in the cloud.

Data infrastructures including hyper-converged systems can all benefit from these advances through advanced parallel I/O software technologies that can dramatically increase their productivity by untapping the power that lies within standard multicore servers. While hyper-converged has become the buzzword of the day, let's remember the real objective is to achieve the most productivity at the lowest cost, therefore better utilization of one's storage and servers to drive applications is the key.

The Next Giant Leap Forward - Leveraging the Multiplier Impact of Parallel Processing on Productivity

This combination of powerful software and servers will drive greater functionality, more automation, and comprehensive services to productively manage and store data across the entire data infrastructure. It will lead to a new era where the benefits of multicore parallel processing can be applied universally. These advances (which are already before us) are key to solving the problems caused by slow I/O and inadequate response times that have been responsible for holding back application workload performance and cost savings from consolidation. The advances in multicore processing, parallel processing software and software-defined infrastructure, collectively, are fundamental to achieving the next giant leap forward in business productivity.