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In-Memory Computing: Powering Enterprise High-Performance

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In-Memory Computing: Powering Enterprise High-Performance • Cognizant 20-20 Insights In-Memory Computing: Powering Enterprise High-Performance Computing To succeed in today’s modern digital era, organizations must embrace the next wave of hyperscale computing into mainstream business by considering in-memory computing technologies that not only bolster their large-scale data processing capabilities but accelerate the transformation of raw information into applied knowledge. Executive Summary interconnect technology, etc. that enable IT orga- nizations to fast-track enterprise computing to Traditional high performance computing (HPC)/ better serve the ever-growing data needs of the supercomputing, analytics and mainstream real- business. time/batch computing are quickly converging. Mainstream workloads are crossing over the high Significant enthusiasm is building around the performance computing arena, demanding faster IMC paradigm for large-scale data analysis. His- analytics/batching, resource-intensive computa- torically, in-memory grid technologies were tions and algorithms. To succeed in today’s accel- primarily data-focused and used by the orga- erating digital world, enterprises must collect and nizations for distributed caching patterns to analyze mind-boggling amounts of data, in real achieve low latency reads of critical transac- time, and at ever-faster speeds that most legacy tional data. However, IMC technology is progres- enterprise HPC technologies and systems were sively emerging as a key empowering agent for not originally designed to accommodate. enterprises seeking to accelerate their real-time decision-making ability and agility, by enabling In our view, organizations need to embark on what Web-scale data processing, which are capabilities we call Enterprise HPC 2.0. This term refers to the necessary for staying relevant and competitive in ecosystem that leverages/utilizes various latest today’s digital era. commodity-hardware-based hyperscale grid tech- nologies such as in-memory computing (IMC), IMC’s impact is typically felt where organiza- compute and data grid technologies, streaming tions are creating new and more innovative ways analytics, graph analytics, etc. These are in con- of working. A dramatic reduction in memory junction with infrastructure advancements such hardware costs also favors the growth of IMC as solid state drives (SSD)-enabled technology, technologies. However, several factors continue GPGPU acceleration, general purpose Infiniband cognizant 20-20 insights | november 2015 to slow the adoption at the enterprise, such as This white paper summarizes the features and a fragmented technology and vendor landscape, benefits of using IMC for large-scale data-set a lack of commonly agreed upon industry aggregations using multiple popular IMC standards, scarcity of skills and still-emerging approaches. The paper presents results from an industry best practices. internal study performed in which we created an evaluation scenario to compare various IMC Given that the technology remains in its adoles- approaches/technology architectures. The study cence, the selection of the right IMC technology results establish that simple migration to an IMC is critical to any strategic digital business trans- technology yields performance levels 13 times formation decision. Soaring enterprise workloads greater for a given batch workload previously and the use cases that make use of in-memory implemented using a disk-based architecture. processing are informing key decisions around This paper not only highlights the importance IMC technology platform selection. of embracing the IMC agenda for enterprise workloads but offers a formal methodology for A blind jump into the IMC technology valley will choosing the most appropriate IMC platform to fit not yield durable value. It requires clear and given business needs. effective analysis and understanding of workloads and business priorities, with a goal to increase In-Memory Computing: scalable performance and competitive benefits A Market Check for the business. This entails skilled experts to perform a focused evaluation. Furthermore, the Effective use of IMC technology along with a clear multitude of new and emerging products makes is strategy for adoption can help enterprises reap extremely challenging to select the right product multiple benefits. Figure 1 lists some of the key and approach. use cases across specific industries. While this is just an indication, the possibilities are abundant However daunting this decision may seem, it is of and are not limited to the specified list. utmost importance for organizations to use IMC technology to help address their ever-mounting There have been rapid innovations in the IMC high-performance and low-latency processing space recently to enable faster computation needs across the enterprise. and processing speeds. These include Hadoop In-Memory Computing (Enterprise HPC 2.0) Telecom Insurance Manufacturing ■ ■ Real-time ads Faster claim ■ Inventory placements. processing & management. modeling. ■ Real-time sentiment ■ Predictive analytics Retail Healthcare ■ Banking & Financial analysis. Faster actuarial to avoid unplanned ■ Real-time in-store ■ Services Faster medical science. downtime. analytics. ■ imaging processing. ■ Fraud detection. Real-time trading ■ decisions. Fast real-time ■ Genome analysis. loyalty offers. ■ Faster reporting. Figure 1 cognizant 20-20 insights 2 MapReduce — a batch processing framework for storing large-scale data. However, it provides that has added support for an in-memory file a processing platform for large-scale in-memory system called Tachyon. In addition, IBM has computing and is said to provide performance added Apache Spark — an IMC system — to its z up to 100 times faster for certain applications1 Systems to bring analytics to mainframes. Also, and is being endorsed by IBM2 and Amazon Web SQL Server 2016 Community Technology Preview Services.3 2 adds IMC power. This has led to the availability of a plethora of IMC technology-based products. Figure 2 illustrates the evolution of IMC However, these products can be classified into technology, some of the popular products under various segments, based on their inherent archi- each segment and the typical workloads for which tecture and technological approaches. Moreover, they are best used. each IMC system is not applicable for every type Given the rapid pace of innovation, the IMC of enterprise workload. It is therefore imperative product landscape requires the latest skills and to have a clear understanding of the pros and a thorough understanding of a specific IMC cons of each of these system types in order to system’s architectural underpinnings to validate effectively select and utilize IMC systems and its fit and effective use for a given enterprise reap the business benefits. workload. Furthermore, with the multiple options IMC technology has evolved from its earliest available, enterprises can find it difficult to make avatar (distributed caching) to today’s integrated the best choice and use of an IMC technology to in-memory platform that provides storage, satisfy their high performance computing needs. compute and transactional services for large-scale To address these challenges, we — at the data sets. These systems fall under the pure-play Cognizant Hyperscale Computing (HPC) Lab — IMC technologies category. The “alternate IMC” have launched a structured methodology to help segment applies to products such as Apache enterprises realize value from the next wave of Spark, which, in our view, does not represent hyperscale computing using Enterprise HPC 2.0, all-encompassing in-memory technology in the which leverages in-memory computing grids. strict sense since it does not provide a platform IMC Technology’s Progression Alternate IMC A platform for computing Pure Play IMC and transacting on large-scale data sets in A next-gen platform that parallel. integrates IMDG with IMCG and provides additional features like CEP, streaming etc. A RDBM system In-Memory that stores data in Compute Grid memory instead (IMCG) A data fabric across of on disk. large cluster of ■ Apache Spark servers for distributed In-Memory Data in-memory storage Fabric (IMDF) and management of large data sets. ■ Apache IgnIgnite In-Memory (GridGain) A cache that partitions its data Database (IMDB) among all cluster nodes. ■ SAP HANA In-Memory Data ■ Oracle Exalytics Grid (IMDG) ■ Exadata ■ Pivotal GemFire XD ■ MS SQL2014 Distributed ■ Oracleracle CoherenCoherence Caches ■ GigaSpacesaSpaces XAP ■ Memcachedd ■ Hazelcast ■ Ehcachecache ■ Infinispannispan (JBo(JBoss) ■ Pivotal GemFireire For real-time big data In-memory high speed For a single integrated initiatives, handling HPC alternative for existing platform for real-time big data For in-memory Distributed Key/Value payloads along the lines of disk-based RDBMS with management and computing, computation and Cache for Low Latency MapReduce, MPP with full SQL support, with no handling new HPC payloads processing of data access. partial SQL support. change to application. such as Streaming, CEP. stored in disks. Figure 2 cognizant 20-20 insights 3 IMC Technology Selection Process IMC Assessment Methodology Establishment (Stage I) Refinement (Stage II) 1 2 3 4 Figure 3 IMC Value Creation: Methodology readily and easily supported by the product, apart from the in-memory caching features normally A clear process, as well as a framework, is required available with such products: to establish the business goals and successfully determine the best-fit IMC technology.
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