Transactions on Machine Intelligence

Transactions on Machine Intelligence

A Novel Approach to Reducing Energy Consumption, Economic Savings, Service Quality Enhancement, and Resource Utilization in Cloud Data Centers

Document Type : Original Article

Authors
M. H. Mahmoudian 1
H. Taheri 2
A. Beik-Mohammadi 1
1 Student of Digital Electronics Department, Faculty of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
2 Associate Professor, Faculty of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
10.47176/TMI.2020.199
Abstract
Cloud data centers often provide the infrastructure for millions of virtual machines in dynamic environments. Virtual machine deployment is a process where it is determined which virtual machines should be executed on which physical machines within the virtualized infrastructure. Given the randomness of customer requests, the virtual machine deployment issue must be formulated as a dynamic optimization problem. On the other hand, providers must be able to respond to virtual resource requests in complex dynamic cloud computing environments, considering service elasticity and overbooking physical resources. In this work, five experiments were designed and conducted for the two-stage optimization of such issues. In these experiments, after evaluating online heuristic algorithms, various overbooking protection coefficients, and different scaling methods, a non-deterministic formulation was considered for optimizing four objective functions (energy consumption, economic savings, service quality, and resource utilization). The experimental results, considering 96 different scenarios, show that two of the online phase heuristic algorithms, taking into account a memetic algorithm for the offline phase, setting the overbooking protection coefficient to 0.75, and scaling the four objective functions based on the shortest Euclidean distance to the origin, yield the best performance.
Keywords
Virtual Machine Deployment
Optimization
Virtualization
Energy Consumption
Service Quality
Data Centers
Virtual Resources
Cloud Computing
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Transactions on Machine Intelligence
Volume 3, Issue 4
Autumn 2020
Pages 199-210

  • Receive Date 06 July 2020
  • Revise Date 11 September 2020
  • Accept Date 02 December 2020