TY - GEN
T1 - QoS-Aware virtual machine consolidation in cloud datacenter
AU - Monil, Mohammad Alaul Haque
AU - Malony, Allen D.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/9
Y1 - 2017/5/9
N2 - With the rapid growth of the cloud industry in recent years, energy consumption of warehouse-scale datacenters has become a major concern. Energy-aware Virtual Machine consolidation has proven to be one of the most effective solutions for tackling this problem. Among the sub-problems of VM consolidation, VM placement is the trickiest and can be treated as a bin packing problem which is NP-hard, hence, it is logical to apply a heuristic approach. The main challenge of VM consolidation is to achieve a balance between energy consumption and quality of service (QoS). In this research, we evaluate this problem and design a combined strategy using best fit decreasing bin packing method and multi-pass optimization in VM placement for an efficient VM consolidation. We have used CloudSim toolkit to simulate our experiments. To evaluate the performance of the proposed algorithms we used real-world workload traces from thousand VMs. Results demonstrate that our proposed methods outperform other existing methods.
AB - With the rapid growth of the cloud industry in recent years, energy consumption of warehouse-scale datacenters has become a major concern. Energy-aware Virtual Machine consolidation has proven to be one of the most effective solutions for tackling this problem. Among the sub-problems of VM consolidation, VM placement is the trickiest and can be treated as a bin packing problem which is NP-hard, hence, it is logical to apply a heuristic approach. The main challenge of VM consolidation is to achieve a balance between energy consumption and quality of service (QoS). In this research, we evaluate this problem and design a combined strategy using best fit decreasing bin packing method and multi-pass optimization in VM placement for an efficient VM consolidation. We have used CloudSim toolkit to simulate our experiments. To evaluate the performance of the proposed algorithms we used real-world workload traces from thousand VMs. Results demonstrate that our proposed methods outperform other existing methods.
KW - CloudSim
KW - SLA
KW - VM placement
KW - Virtual Machine Consolidation
UR - http://www.scopus.com/inward/record.url?scp=85020199491&partnerID=8YFLogxK
U2 - 10.1109/IC2E.2017.31
DO - 10.1109/IC2E.2017.31
M3 - Conference contribution
AN - SCOPUS:85020199491
T3 - Proceedings - 2017 IEEE International Conference on Cloud Engineering, IC2E 2017
SP - 81
EP - 87
BT - Proceedings - 2017 IEEE International Conference on Cloud Engineering, IC2E 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Cloud Engineering, IC2E 2017
Y2 - 4 April 2017 through 7 April 2017
ER -