Energy-Aware Scheduling Framework for resource allocation in a virtualized cloud data centre

Abstract

Abstract-Cloud paradigm is an embryonic computing model that in its vicinity stresses on proficient utilization of computing resources. Data centers that host and service cloud applications ingest enormous amount of energy, leading to massive emission of carbon footprints to the atmosphere and high operational expenditures. Consequently, there is a need to establish synergy between data centre resources for optimum resource utilization and strategies needs to be devised that can considerably reduce energy consumption in cloud data center. This paper elucidates an architectural framework for computation of energy spent in scheduling resources on hosts. The framework has been implemented for bin-packing techniques and explicates minutiae about broker components involved in scheduling process. Keyword-Cloud Computing, Energy Consumption, VM Migration, Resource Scheduling.I. INTRODUCTION Contemporary [1,2] resource-intensive enterprises has engendered demands for high performance computing infrastructures. Proliferation of IT services to be used by diverse range of cloud users has led to construction of large-scale energy hungry data centers that can facilitate computing services. Despite of the improvisations introduced in energy consumption models, service providers are confronted with challenges of reduction in energy consumption and CO 2 emission.The rationale [3] in the wake of explosion of energy emission is increase in number of computer usage due to increase in number of IT practitioners. As an upshot, size of data centres has increased. Moreover, exploiting energy-aware resource provisioning to its fullest extent can subsequently provide a solution to the forefront issues.[4] Service virtualization and consolidation are acting as inherent practices that can escort energyefficient datacenter architectures. It has effectually led to efficient resource utilization. VM provisioning can be sighted as a multidimensional bin packing problem comprising of capricious bin configurations and cost parameters. Virtualization technique embraces server consolidation process and a VM live-migration technique that has validated to be efficient in drastic reduction in energy consumption in high-performance cloud datacenters. However, I/O virtualization has excavated grounds for performance degradation posed by overheads encountered in vm migrations and needs to be addressed urgently.The organization of paper is laid out as literature review in beginning followed by research focus. The IV section elucidates architectural framework trailed by working prototype. The last section illustrates conclusion and future work.II. LITERATURE REVIEW The research work presented in [5] explores method to manage data intensive distributed programming paradigms (like MapReduce and Dryad) that assists practitioners to effortlessly parallelize the processing of huge data sets. Deployment of such data intensive computing infrastructures is of significant concern due to rise in cost. The work carried out in the study dynamically adjusts the size ...

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