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Sadoon Azizi

Sadoon Azizi

Academic rank: Associate Professor
ORCID:
Education: PhD.
ScopusId: 65456
Faculty: Faculty of Engineering
Address: Room No. 206, Department of Computer Engineering and Information Technology , Faculty of Engineering , University of Kurdistan, Sanandaj, Iran.
Phone:

Research

Title
An energy-efficient algorithm for virtual machine placement optimization in cloud data centers
Type
JournalPaper
Keywords
Cloud computing, Infrastructure as a service (IaaS), Virtual machine placement (VMP), Optimization, Energy efficiency, Resource utilization
Year
2020
Journal Cluster Computing-The Journal of Networks Software Tools and Applications
DOI
Researchers Dawei Li ، Mazhar Zandsalimi ، Sadoon Azizi

Abstract

Cloud providers offer computing services based on user demands using the Infrastructure as a Service (IaaS) service model. In a cloud data center, it is possible that multiple Virtual Machines (VMs) run on a Physical Machine (PM) using virtualization technology. Virtual Machine Placement (VMP) problem is the mapping of virtual machines across multiple physical ones. This process plays a vital role in defining energy consumption and resource usage efficiency in the cloud data center infrastructure. However, providing an efficient solution is not trivial due to difficulties such as machine heterogeneity, multi-dimensional resources, and large scale cloud data centers. In this paper, we propose an efficient heuristic algorithm that focuses on power consumption and resource wastage optimization to solve the aforementioned problem. The proposed algorithm, called MinPR, minimizes the total power consumption by reducing the number of active physical machines and prioritizing the power-efficient ones. Also, it reduces resource wastage by maximizing and balancing resource utilization among physical machines. To achieve these goals, we propose a new Resource Usage Factor model that manages virtual machine placement on physical machines using reward and penalty mechanisms. Simulations based on cloud user-customized VMs and Amazon EC2 Instances workloads illustrate that the proposed algorithm outperforms existing approaches. In particular, the proposed algorithm reduces total energy consumption by up to 15% for cloud user-customized VMs and by up to 10% for Amazon EC2 Instances.