1403/02/07
سعدون عزیزی

سعدون عزیزی

مرتبه علمی: دانشیار
ارکید:
تحصیلات: دکترای تخصصی
اسکاپوس: 65456
دانشکده: دانشکده مهندسی
نشانی: دانشکده مهندسی، ساختمان شماره 1، اتاق 206
تلفن:

مشخصات پژوهش

عنوان
An energy-efficient algorithm for virtual machine placement optimization in cloud data centers
نوع پژوهش
JournalPaper
کلیدواژه‌ها
Cloud computing, Infrastructure as a service (IaaS), Virtual machine placement (VMP), Optimization, Energy efficiency, Resource utilization
سال
2020
مجله Cluster Computing-The Journal of Networks Software Tools and Applications
شناسه DOI
پژوهشگران Sadoon Azizi ، Mazhar Zandsalimi ، Dawei Li

چکیده

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.