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SDN helps Big Data to optimize storage

SDN helps Big Data to optimize storage

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Distributed key-value stores have become the sine qua non for supporting today's large-scale web services. The extreme latency and throughput requirements of modern web applications are driving the use of distributed in-memory object caches. Similarly, the use of persistent object stores has been growing rapidly as they combine key advantages such as HTTP-based RESTfulAPIs, high availability, elasticity with a pay-as-you-go pricing model that allows applications to scale as needed. Consequently, there is an urgent need for optimizing the emerging software defined cloud data centers to efficiently support such applications at scale. In this chapter, we discuss different techniques to optimize the Big Data processing and data management using key-value stores and software defined networks in virtualized cloud data centers. Specifically, we explore two key questions. (1) How do cloud services users, i.e., tenants, get the most bang-for-the-buck with a distributed in-memory key-value store deployment in a shared multitenant environment? (2) How do tenants enhance cloud object store's capabilities through fine-grained resource management to effectively meet their SLAs while maximizing resource efficiency? Moreover, we also present the state of the art in this domain and provide a brief analysis of desirable features. We then demonstrate through experiments the impact of SDN-based Big Data storage management solution on improving performance and overall resource efficiency. Finally, we discuss open issues in SDN-based Big Data I/O stacks and future directions.

Chapter Contents:

  • 13.1 Software defined key-value storage systems for datacenter applications
  • 13.2 Related work, features, and shortcomings
  • 13.2.1 Shortcomings
  • 13.2.1.1 Default configuration
  • 13.2.1.2 FavorsSmall configuration
  • 13.2.1.3 FavorsLarge configuration
  • 13.3 SDN-based efficient data management
  • 13.4 Rules of thumb of storage deployment in software defined datacenters
  • 13.4.1 Summary of rules-of-thumb
  • 13.5 Experimental analysis
  • 13.5.1 Evaluating data management framework in software defined datacenter environment
  • 13.5.1.1 Overview of CAST framework design
  • 13.5.1.2 Methodology
  • 13.5.1.3 Effectiveness for general workload
  • 13.5.1.4 Effectiveness for data reuse
  • 13.5.2 Evaluating micro-object-store architecture in software defined datacenter environment
  • 13.5.2.1 Overview of MOS micro-object-store architecture
  • 13.5.2.2 Methodology
  • 13.5.2.3 Performance evaluation
  • 13.6 Open issue and future directions in SDN-enabled Big Data management
  • 13.6.1 Open issues in data management framework in software defined datacenter
  • 13.6.1.1 Analytics workloads with relatively fixed and stable computations
  • 13.6.1.2 Dynamic vs. static storage tiering
  • 13.6.2 Open issues in micro-object-store architecture in software defined datacenter environment
  • 13.6.2.1 Limitation on number of microstores
  • 13.6.2.2 Online optimizations of microstores
  • 13.7 Summary
  • References

Inspec keywords: software defined networking; storage management; cloud computing; Web services; cache storage; computer centres

Other keywords: storage optimization; Web services; fine-grained resource management; cloud object store capabilities; pay-as-you-go pricing model; distributed key-value stores; throughput requirements; SLAs; extreme latency; distributed in-memory key-value store deployment; Web applications; SDN-based Big Data storage management solution; HTTP-based RESTfulAPIs; virtualized cloud data centers; Big Data processing; resource efficiency maximization; shared multitenant environment; persistent object stores; software defined cloud data centers; SDN-based Big Data I/O stacks; distributed in-memory object caches

Subjects: File organisation; Semiconductor storage; Information networks; Computer networks and techniques; Internet software; Computer communications

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