Paper Info
Title
EnLoB: Energy and Load Balancing-Driven Container Placement Strategy for Data Centers
Abstract
Cloud data centers (DCs) can be aptly regarded as the epicenter of today's business and economy; which support seamless data processing, analysis, and storage. However, various studies advocate that the existing DCs are often underutilized. To be precise, almost 30% of the installed DCs in the United States are comatose. In addition to this, the existing DC architecture leads to extensive energy utilization, which severely hampers the environment and places a severe risk on the power sector. Thus, it is highly essential to reduce DC's energy utilization through efficient resource consolidation approaches. In this work, we investigate the joint impact of resource consolidation and load balancing on cutting down the energy utilization indices of the cloud DCs. In this vein, we formulate a multi-objective optimization problem (MOOP) for container placement across heterogeneous infrastructure, primarily with the intent to minimize the overall energy consumption and balance the load amongst the operating hosts. However, due to the hardness of the underlying problem and its infeasibility to furnish optimal solutions in polynomial time, we designed an online solution based on the incremental exploration of the solution space to map containers on the available array of hosts such that the objectives mentioned above can be attained. Finally, we evaluated the performance of the proposed algorithm in contrast to an existing algorithm on real-time workload traces obtained from PlanetLab. The obtained results confirm the superior performance of the proposed algorithm relative state-of-the-art.
Year
DOI
Venue
2019
10.1109/GCWkshps45667.2019.9024592
2019 IEEE Globecom Workshops (GC Wkshps)
Keywords
DocType
ISSN
EnLoB,cloud data centers,epicenter,business,economy,seamless data processing,United States,existing DC architecture,extensive energy utilization,power sector,joint impact,energy utilization indices,cloud DC,multiobjective optimization problem,container placement,heterogeneous infrastructure,energy consumption,operating hosts,optimal solutions,online solution,solution space,map containers,resource consolidation approaches,DC energy utilization,MOOP
Conference
2166-0069
ISBN
Citations 
PageRank 
978-1-7281-0961-9
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Kuljeet Kaur119519.59
Sahil Garg226740.16
Georges Kaddoum387494.42
Francois Gagnon413133.07
Dushantha Nalin K Jayakody518031.83