TY - GEN
T1 - Energy-efficient dynamic consolidation of virtual machines in big data centers
AU - Xu, Shuting
AU - Wu, Chase Q.
AU - Hou, Aiqin
AU - Wang, Yongqiang
AU - Wang, Meng
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - There is a rapidly growing demand for computing power driven by big data applications, which is typically met by constructing large-scale data centers provisioning virtualized resources. Such data centers consume an enormous amount of energy, resulting in high operational cost and carbon dioxide emission. Meanwhile, cloud providers need to ensure Quality of Service (QoS) in the computing solution delivered to their customers, and hence must consider the power-performance trade-off. We propose a virtual machine (VM) consolidation optimization framework, consisting of three optimization processes in big data centers: (i) VM allocation, (ii) overloaded physical machine (PM) detection and consolidation, and (iii) underloaded PM detection and consolidation. We show that the optimization problem is NP-complete, and design a resource management scheme that integrates three algorithms, one for each optimization process. We implement and evaluate the proposed resource management scheme in CloudSim and conduct simulations on a real workload trace of PlanetLab. Extensive simulation results show that the proposed solution yields up to 21.5% reduction in energy consumption, 34.2% reduction in performance degradation due to migration, 70.2% reduction in SLA violation time per active host, and 68% reduction in Energy and SLA Violations (ESV), respectively, in comparison with state-of-the-art solutions.
AB - There is a rapidly growing demand for computing power driven by big data applications, which is typically met by constructing large-scale data centers provisioning virtualized resources. Such data centers consume an enormous amount of energy, resulting in high operational cost and carbon dioxide emission. Meanwhile, cloud providers need to ensure Quality of Service (QoS) in the computing solution delivered to their customers, and hence must consider the power-performance trade-off. We propose a virtual machine (VM) consolidation optimization framework, consisting of three optimization processes in big data centers: (i) VM allocation, (ii) overloaded physical machine (PM) detection and consolidation, and (iii) underloaded PM detection and consolidation. We show that the optimization problem is NP-complete, and design a resource management scheme that integrates three algorithms, one for each optimization process. We implement and evaluate the proposed resource management scheme in CloudSim and conduct simulations on a real workload trace of PlanetLab. Extensive simulation results show that the proposed solution yields up to 21.5% reduction in energy consumption, 34.2% reduction in performance degradation due to migration, 70.2% reduction in SLA violation time per active host, and 68% reduction in Energy and SLA Violations (ESV), respectively, in comparison with state-of-the-art solutions.
KW - Big data centers
KW - Energy consumption
KW - VM consolidation
UR - http://www.scopus.com/inward/record.url?scp=85019262696&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019262696&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-57186-7_16
DO - 10.1007/978-3-319-57186-7_16
M3 - Conference contribution
AN - SCOPUS:85019262696
SN - 9783319571850
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 191
EP - 206
BT - Green, Pervasive, and Cloud Computing - 12th International Conference, GPC 2017, Proceedings
A2 - Au, Man Ho Allen
A2 - Choo, Kim-Kwang Raymond
A2 - Li, Kuan-Ching
A2 - Castiglione, Arcangelo
A2 - Palmieri, Francesco
PB - Springer Verlag
T2 - 12th International Conference on Green, Pervasive and Cloud Computing, GPC 2017
Y2 - 11 May 2017 through 14 May 2017
ER -