A prediction‐based and power‐aware virtual machine allocation algorithm in three‐tier cloud data centers

Abstract

With the increasing popularity of cloud computing services, the more number of cloud data centers are constructed over the globe. This makes the power consumption of cloud data center elements as a big challenge. Hereby, several software and hardware approaches have been proposed to handle this issue. However, this problem has not been optimally solved yet. In this paper, we propose an online cloud resource management with live migration of virtual machines (VMs) to reduce power consumption. To do so, a prediction-based and power-aware virtual machine allocation algorithm is proposed. Also, we present a three-tier framework for energy-efficient resource management in cloud data centers. Experimental results indicate that the proposed solution reduces the power consumption; at the same time, service-level agreement violation (SLAV) is also improved.KEYWORDS cloud computing, dynamic consolidation, power efficiency, three-tier cloud data centers, virtual machine allocation, virtualization | INTRODUCTIONWith the increasing speed of the Internet over the world, the cloud computing technology is becoming more and more attractive for both customers and service providers. 1,2 The rapid increase in the popularity of cloud services has led to increase in the number of cloud customers. It leads to a daily increase in cloud service demands. That is why main cloud providers such as Amazon, IBM, and Google etc have forced to increase their number of cloud data centers. This makes cloud computing area as one of the most challenging areas in computer science today. It needs a markedly increase in the amount of monetary costs. Hence, there should be a push to have more efficiency in maintenance and execution costs. There has been proposed a number of approaches to get close to this aim; however, it needs much more work to reach optimality. One of these approaches is to reduce the amount of power consumed by cloud resources and other network elements. The less the amount of power consumed, the lower is the amount of monetary costs which has to be paid pack by cloud providers. 3,4 Regarding power and cost efficiency, the amount of carbon footprint has increased drastically due to the increasing amount of power consumption. Hence, reducing power consumption not only reduces the amount of monetary costs for cloud operations but also reduces the threat of a natural disaster. 5,6 To do so, the dynamic virtual machine (VM) consolidation by live migration over runtime is known as a popular and welcoming approach. 7 By this means, we can migrate in or/and out VMs among hosts. More precisely, VMs located on a host with low load (underloaded host) can be migrated out to other active hosts, and then, the host can be switched to

EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

“…The proposed mathematical formulation improves the results compared to Best-Fit heuristics techniques by 6-15%. Tarahomi and Izadi, in [8], proposed an online resource management technique with live VM migration in CDCs. The authors also designed a prediction-based and power-aware VM allocation algorithm.…”

“…The host is said to be in an under-loaded state if the resources of the hosts are below or equal to a certain utilization threshold. The host overload problem further leads to the Service-Level Agreement (SLA) violation [8]. In general, SLA violation is considered a bad experience for the provisioning of Cloud services to the Cloud customers, which means that Cloud customers would not be satisfied with the Cloud services.…”

VM Scheduling for Efficient Dynamically Migrated Virtual Machines (VMS-EDMVM) in Cloud Computing Environment

“…To achieve this objective, in this work, a series of steps are carried out on VMs in a datacenter environment. First, the resource usage data of all VMs is collected for each physical machine or host, and a Modified Weighted Linear Regression (MWLR) algorithm has been developed to determine the over-utilized host [8]. Then a dynamic and adaptive energyefficient VM migration and Best-Fit CPU, BW (BFCB) mechanism for VM Scheduling [9], has been incorporated by considering the violation of SLA, power consumption, and VM migrations count in the cloud platform [10].…”

A new metaheuristic‐based method for solving the virtual machines migration problem in the green cloud computing

“…23 Virtualization provides VM migration when faced with an increase in the demand for dynamic workloads inside cloud data centers. 24,25 Energy management, load balancing, system maintenance, and fault tolerance are considered as management objectives of resources achieved by VM migration. [26][27][28] Consumers can modify their resource demand when elasticity is provided by the cloud.…”