TY - JOUR
T1 - Energy-Aware Virtual Machine Management in Inter-Datacenter Networks over Elastic Optical Infrastructure
AU - Zhang, Liang
AU - Han, Tao
AU - Ansari, Nirwan
N1 - Funding Information:
Manuscript received March 13, 2017; revised August 7, 2017 and October 9, 2017; accepted November 2, 2017. Date of publication November 8, 2017; date of current version March 16, 2018. This work was supported in part by the National Science Foundation under Grant CNS-1320468 and Grant CNS-1731675. The preliminary idea of this work was presented at IEEE Cloudcom, Vancouver, Canada, Nov. 30 - Dec. 2, 2015. The associate editor coordinating the review of this paper and approving it for publication was L. Chiaraviglio. (Corresponding author: Nirwan Ansari.) L. Zhang and N. Ansari are with the Advanced Networking Laboratory, Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102 USA (e-mail: lz284@njit.edu; nir-wan.ansari@njit.edu).
Publisher Copyright:
© 2017 IEEE.
PY - 2018/3
Y1 - 2018/3
N2 - Datacenters (DCs), deployed in a large scale to support the ever increasing demand for data processing applications, consume tremendous energy. Powering DCs with renewable energy can effectively reduce the brown energy consumption. Owing to geographically distributed deployment of DCs, the renewable energy generation and the data processing demands usually vary in different DCs. Migrating virtual machines (VMs) among DCs according to the availability of renewable energy helps match the energy demands and the renewable energy generation in DCs, and thus maximizes the utilization of renewable energy. We first elicit the renewable energy-aware inter-DC (inter-DC) VM migration problem in an inter-DC network over the elastic optical infrastructure, present it as a many-manycast communications problem, and then formulate it as an integer linear programming problem. The objective is to minimize the total cost of the brown energy consumption of DCs in such inter-DC network via VM migration. We use CVX and Gurobi to solve this problem for small network configurations, and we propose a few heuristic algorithms that approximate the optimal solution for large network configurations. Through extensive simulations, we show that the proposed algorithms, by migrating VM among DCs, can reduce up to 19.7% cost of the brown energy consumption.
AB - Datacenters (DCs), deployed in a large scale to support the ever increasing demand for data processing applications, consume tremendous energy. Powering DCs with renewable energy can effectively reduce the brown energy consumption. Owing to geographically distributed deployment of DCs, the renewable energy generation and the data processing demands usually vary in different DCs. Migrating virtual machines (VMs) among DCs according to the availability of renewable energy helps match the energy demands and the renewable energy generation in DCs, and thus maximizes the utilization of renewable energy. We first elicit the renewable energy-aware inter-DC (inter-DC) VM migration problem in an inter-DC network over the elastic optical infrastructure, present it as a many-manycast communications problem, and then formulate it as an integer linear programming problem. The objective is to minimize the total cost of the brown energy consumption of DCs in such inter-DC network via VM migration. We use CVX and Gurobi to solve this problem for small network configurations, and we propose a few heuristic algorithms that approximate the optimal solution for large network configurations. Through extensive simulations, we show that the proposed algorithms, by migrating VM among DCs, can reduce up to 19.7% cost of the brown energy consumption.
KW - Anycast
KW - cloud computing
KW - elastic optical networks
KW - renewable energy
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U2 - 10.1109/TGCN.2017.2771724
DO - 10.1109/TGCN.2017.2771724
M3 - Article
AN - SCOPUS:85045576529
SN - 2473-2400
VL - 2
SP - 305
EP - 315
JO - IEEE Transactions on Green Communications and Networking
JF - IEEE Transactions on Green Communications and Networking
IS - 1
ER -