Energy Efficient Virtual Machine Consolidation Research Articles

Improved beetle swarm optimization algorithm for energy efficient virtual machine consolidation on cloud environment

AbstractIn cloud computing, virtual machine (VM) consolidation and migration are significant schemes to enhance the energy efficiency and costs of the cloud environment. The process of VM consolidation is to execute a more number of tasks with less power consumption. However, due to unreliable physical resources, energy consumption is increased. So, to solve this issue, improved beetle swarm optimization (IBSO) algorithm based on energy‐aware VM consolidation is presented in this article. IBSO algorithm joins together beetle swarm optimization (BSO) and particle swarm optimization (PSO). The proposed algorithm is defined with the effective representation of solution and multi‐objective functions such as power consumption, resource wastage, and SLA violation. Results of the work are depicted that the proposed approach outperformed different previous methods in terms of different evaluation measures.

Applying Reinforcement Learning towards automating energy efficient virtual machine consolidation in cloud data centers

Energy awareness presents an immense challenge for cloud computing infrastructure and the development of next generation data centers. Virtual Machine (VM) consolidation is one technique that can be harnessed to reduce energy related costs and environmental sustainability issues of data centers. In recent times intelligent learning approaches have proven to be effective for managing resources in cloud data centers. In this paper we explore the application of Reinforcement Learning (RL) algorithms for the VM consolidation problem demonstrating their capacity to optimize the distribution of virtual machines across the data center for improved resource management. Determining efficient policies in dynamic environments can be a difficult task, however, the proposed RL approach learns optimal behavior in the absence of complete knowledge due to its innate ability to reason under uncertainty. Using real workload data we provide a comparative analysis of popular RL algorithms including SARSA and Q-learning. Our empirical results demonstrate how our approach improves energy efficiency by 25% while also reducing service violations by 63% over the popular Power-Aware heuristic algorithm.

SAVE: self-adaptive consolidation of virtual machines for energy efficiency of CPU-intensive applications in the cloud

In virtualized data centers, consolidation of virtual machines (VMs) on minimizing the number of total physical machines (PMs) has been recognized as a very efficient approach. This paper considers the energy-efficient consolidation of VMs in a cloud datacenter. Concentrating on CPU-intensive applications, the objective is to schedule all requests non-preemptively, subjecting to constraints of PM capacities and running time interval spans, to make the total energy consumption of all PMs is minimized (called MinTE for abbreviation). The MinTE problem is NP-complete in general. We propose a self-adaptive approach called SAVE. The approach makes decisions of the assignment and migration of VMs by probabilistic processes and is based exclusively on local information. Both simulation and real environment test show that our proposed method SAVE can reduce energy consumption about $$30\%$$ against VMWare DRS and 10–20% against ecoCloud on average. Extensive experiments show that our method outperforms the existing method and achieves significant energy savings and high utilization.

SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Robust Linear Regression Prediction Model

Virtual machine (VM) consolidation provides a promising approach to save energy and to improve resource utilization in data centers. However, the aggressive consolidation of virtual machines may lead to service-level agreements (SLA) violation, which is essential for data centers and their users. Therefore, it is very meaningful to strike a tradeoff between power efficient and reduction of SLA violation level. In this paper, we propose a host overloading/underloading detection algorithm and a new VM placement algorithm based on our proposed robust simple linear regression prediction model for SLA-aware and energy-efficient consolidation of virtual machines in cloud data centers. Different from the native linear regression, our proposed methods amend the prediction and squint toward over-prediction by adding the error to the prediction; in this paper, we propose eight methods to calculate the error. We evaluate our proposed algorithms by extended the CloudSim simulator using PlanetLab workload and random workload. The experimental results show that our proposed model can reduce SLA violation rates by at most 99.16% and energy consumption by at most 25.43% for the real-world workload.

Evolutionary Computing Based on QoS Oriented Energy Efficient VM Consolidation Scheme for Large Scale Cloud Data Centers

Abstract The high pace and increase in cloud computing technology and associated applications, especially large scale data centres, have demanded energy efficient and Quality of Service (QoS) oriented computing platform. To meet these requirements, virtualization and Virtual Machine (VM) consolidation has emerged as an effective solution. The optimization in VM consolidation by means of efficient dynamic resource-utilization prediction, VM selection and placement can achieve optimal solution for energy efficient and QoS oriented cloud computing system. In this paper, an evolutionary computing algorithm called Adaptive Genetic Algorithm (A-GA) based VM consolidation approach has been developed. A-GA based placement policy and its implementation with different VM selection policies like Minimum Migration Time (MMT), Maximum Correlation (MC) and Random Selection (RS), along with different CPU utilization estimation approaches like Inter Quartile Range (IQR), Local Regression (LR), Local Robust Regression (LRR), static THReshold (THR) and Median Absolute Deviation (MAD) has revealed that A-GA based consolidation with MMT selection policy and combined IQR and LRR can enable optimal VM consolidation for large scale infrastructures. In addition, the proposed A-GA policy has exhibited better performance as compared to other meta-heuristics such as Ant Colony Optimization (ACO) and Best Fit Decreasing. The proposed consolidation system can be used for large scale cloud infrastructures where energy conservation, minimal Service Level Agreement (SLA) violation and QoS assurance is inevitable.

An Evolutionary Computing based Energy Efficient VM Consolidation Scheme for Optimal Resource Utilization and QoS Assurance

Background: The increase in Cloud applications have demanded efficient cloud computing systems like Virtual Machine (VM) consolidation that intends to facilitate optimal resource utilization, energy conservation and quality of service. Methods: In this paper, an evolutionary computing technique called Adaptive Genetic Algorithm (A-GA) has been proposed for VM consolidation that encompasses under load and overload utilization detection, VM selection and placement, where the modified robust local regression and interquartile range schemes estimate the dynamic CPU utilization threshold for overload detection, minimum migration time works as VM selection policy, while A-GA optimizes VM placement across network to reduce energy consumption and SLA violation. Findings: PlanetLab Cloud benchmark data based simulation results confirms that the proposed VM consolidation scheme exhibits better than other existing approaches such as Ant Colony Optimization (ACO), Static Threshold (THR), Local Regression (LR), Conventional Inter Quartile Range (IQR) and Median Absolute Deviation (MAD) based virtualization schemes. The proposed system has exhibited minimal host shutdown, VM migration, energy consumption and SLA violation as compared to other existing approaches. Applications: Thus, the efficiency of the proposed VM consolidation scheme signifies that it can be a potential VM consolidation solution for large scale Cloud data centers.