DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IC2E.2015.83
Emergence and adoption of cloud computing have become widely prevalent given the value proposition it brings to an enterprise in terms of agility and cost effectiveness. Big data analytical capabilities (specifically treating storage/system management as a big data problem for a service provider) using Cloud delivery models is defined as Analytics as a Service or Software as a Service. This service simplifies obtaining useful insights from an operational enterprise data center leading to cost and performance optimizations.Software defined environments decouple the control planes from the data planes that were often vertically integrated in a traditional networking or storage systems. The decoupling between the control planes and the data planes enables opportunities for improved security, resiliency and IT optimization in general. This talk describes our novel approach in hosting the systems management platform (a.k.a. control plane) in the cloud offered to enterprises in Software as a Service (SaaS) model. Specifically, in this presentation, focus is on the analytics layer with SaaS paradigm enabling data centers to visualize, optimize and forecast infrastructure via a simple capture, analyze and govern framework. At the core, it uses big data analytics to extract actionable insights from system management metrics data. Our system is developed in research and deployed across customers, where core focus is on agility, elasticity and scalability of the analytics framework. We demonstrate few system/storage management analytics case studies to demonstrate cost and performance optimization for both cloud consumer as well as service provider. Actionable insights generated from the analytics platform are implemented in an automated fashion via an OpenStack based platform.
Ramani Routray, "Cloud Storage Infrastructure Optimization Analytics", , vol. 00, no. , pp. 0, 2015, doi:10.1109/IC2E.2015.83