TY - GEN
T1 - EnLoB
T2 - 2019 IEEE Globecom Workshops, GC Wkshps 2019
AU - Kaur, Kuljeet
AU - Garg, Sahil
AU - Kaddoum, Georges
AU - Gagnon, Francois
AU - Jayakody, Dushantha Nalin K.
PY - 2019/12
Y1 - 2019/12
N2 - 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.
AB - 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.
KW - Container Placement
KW - Energy Efficiency
KW - Heuristics
KW - Load Balancing
KW - Multi-objective Optimization
UR - http://www.scopus.com/inward/record.url?scp=85082302959&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082302959&partnerID=8YFLogxK
U2 - 10.1109/GCWkshps45667.2019.9024592
DO - 10.1109/GCWkshps45667.2019.9024592
M3 - Conference contribution
AN - SCOPUS:85082302959
T3 - 2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings
BT - 2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 December 2019 through 13 December 2019
ER -