Efficient Virtual Machine Placement Research Articles

Energy efficient virtual machines placement in cloud datacenters using genetic algorithm and adaptive thresholds.

Cloud computing platform provides on-demand IT services to users and advanced the technology. The purpose of virtualization is to improve the utilization of resources and reduce power consumption. Energy consumption is a major issue faced by data centers management. Virtual machine placement is an effective technique used for this purpose. Different algorithms have been proposed for virtual machine placement in cloud environments. These algorithms have considered different parameters. It is obvious that improving one parameter affects other parameters. There is still a need to reduce energy consumption in cloud data centers. Data centers need solutions that reduce energy consumption without affecting other parameters. There is a need to device solutions to effectively utilize cloud resources and reduce energy consumption. In this article, we present an algorithm for Virtual Machines (VMs) placement in cloud computing. The algorithm uses adaptive thresholding to identify over utilized and underutilized hosts to reduce energy consumption and Service Level Agreement (SLA) violations. The algorithm is validated with simulations and comparative results are presented.

A Harris Hawk Optimisation system for energy and resource efficient virtual machine placement in cloud data centers.

Virtualisation is a major technology in cloud computing for optimising the cloud data centre's power usage. In the current scenario, most of the services are migrated to the cloud, putting more load on the cloud data centres. As a result, the data center's size expands resulting in increased energy usage. To address this problem, a resource allocation optimisation method that is both efficient and effective is necessary. The optimal utilisation of cloud infrastructure and optimisation algorithms plays a vital role. The cloud resources rely on the allocation policy of the virtual machine on cloud resources. A virtual machine placement technique, based on the Harris Hawk Optimisation (HHO) model for the cloud data centre is presented in this paper. The proposed HHO model aims to find the best place for virtual machines on suitable hosts with the least load and power consumption. PlanetLab's real-time workload traces are used for performance evaluation with existing PSO (Particle Swarm Optimisation) and PABFD (Best Fit Decreasing). The performance evaluation of the proposed method is done using power consumption, SLA, CPU utilisation, RAM utilisation, Execution time (ms) and the number of VM migrations. The performance evaluation is done using two simulation scenarios with scaling workload in scenario 1 and increasing resources for the virtual machine to study the performance in underloaded and overloaded conditions. Experimental results show that the proposed HHO algorithm improved execution time(ms) by 4%, had a 27% reduction in power consumption, a 16% reduction in SLA violation and an increase in resource utilisation by 17%. The HHO algorithm is also effective in handling dynamic and uncertain environments, making it suitable for real-world cloud infrastructures.

Dynamic Virtual Machine Allocation in Cloud Computing Using Elephant Herd Optimization Scheme

Cloud computing is a computing technology that is expeditiously evolving. Cloud is a type of distributed computing system that provides a scalable computational resource on demand including storage, processing power and applications as a service via Internet. Cloud computing, with the assistance of virtualization, allows for transparent data and service sharing across cloud users, as well as access to thousands of machines in a single event. Virtual machine (VM) allocation is a difficult job in virtualization that is governed as an important aspect of VM migration. This process is performed to discover the optimum way to place VMs on physical machines (PMs) since it has clear implications for resource usage, energy efficiency, and performance of several applications, among other things. Hence an efficient VM placement problem is required. This paper presents a VM allocation technique based on the elephant herd optimization scheme. The proposed method is evaluated using real-time workload traces and the empirical results show that the proposed method reduces energy consumption, and maximizes resource utilization when compared to the existing methods.

Energy efficient resource optimization algorithm for cloud infrastructure

The proliferation of cloud computing infrastructure has increased the energy demand remarkably. Energy-efficient resource management is essential for running a cost effective and environment friendly data center. Virtual Machine (VM) consolidation is a well-accepted method for reducing the energy consumption of the cloud data center. Quality of service is an equally important aspect of cloud services. VM migrations caused by consolidation often cause degradation in QoS. These two parameters have been dealt with individually in most research and very few addressed both energy efficiency and QoS simultaneously. We have proposed a new Energy and QoS Efficient (EQSE) VM selection and placement method for improving the energy efficiency along with quality of service (QoS). VM selection and placement are two critical steps of VM consolidation. EQSE uses Resource Gap Minimization (RGM) algorithm for VM selection and Utilization-Aware Best-Fit Decreasing (UABFD) algorithm for placement of these VMs. EQSE along with dynamic thresholds reduces energy consumption and improves the quality of service by reducing the number of VM migrations. CloudSim simulation performed on PlanetLab data establishes the superiority of the proposed method compared to the existing state of the art methods of VM consolidation.

Bi-Objective Optimization of Bandwidth Resources and Energy Consumption for Efficient Virtual Machine Placement in Cloud Computing

Bi-Objective Optimization of Bandwidth Resources and Energy Consumption for Efficient Virtual Machine Placement in Cloud Computing

Power-Efficient Virtual Machine Placement in Cloud Datacenters using Heuristic Assisted Enhanced Discrete Particle Swarm Optimization

The increase in cloud computing services and the large-scale construction of data centers led to excessive power consumption. Datacenters contain a large number of servers where the major power consumption takes place. An efficient virtual machine placement algorithm is substantial to attain energy consumption minimization and improve resource utilization through reducing the number of operating servers. In this paper, an enhanced discrete particle swarm optimization (EDPSO) is proposed. The enhancement of the discrete PSO algorithm is achieved through modifying the velocity update equation to bound the resultant particles and ensuring feasibility. Furthermore, EDPSO is assisted by two heuristic algorithms random first fit (RFF) and random best fit (RBF) to produce hydride algorithms termed RFF-EDPSO and RBF-EDPSO. The proposed algorithms are evaluated and compared with recent algorithms to minimize power consumption. Simulation results showed the effective performance of RFF-EDPSO and RBF-EDPSO in minimizing the number of operating servers.

An Improved Ant Colony Algorithm for Solving a Virtual Machine Placement Problem in a Cloud Computing Environment

The world is currently witnessing many successive changes in several fields, especially in the field of healthcare which forced countries to impose health policies to provide good services, which drew health sector strategies focusing on the digital transformation aspect. E-government healthcare applications are used by dozens of millions of users. This high usage of applications generates a huge network traffic and needs reliable cloud computing platforms and efficient virtual machine placement models. In this context, we proposed an improved Ant Colony algorithm to solve a virtual machine placement problem in a cloud computing environment in order to minimize the total network traffic and the maximum link utilization. The experimental results demonstrate the effectiveness and efficiency of our proposed algorithm.

Power efficient virtual machine placement in cloud data centers with a discrete and chaotic hybrid optimization algorithm

Cloud computing is a new computation technology that provides services to consumers and businesses. The main idea of Cloud computing is to present software and hardware services through the Internet to the users and organizations at all levels. In Cloud computing, the users pay for the services, which means a usage-based payment system is used in this technology. Using virtualization technology in computation resources enables the appropriate utilization of resources in cloud computing. One of the most significant challenging issues in virtualization technology is the placement of optimal virtual machines on physical machines in cloud data centers. The placement of virtual machines comprises a process wherein virtual machines are mapped onto physical machines in cloud data centers. Optimal deployment leads to the reduction in power consumption, optimal use of resources, traffic reduction in data centers, costs reduction, and efficiency enhancement of the data center in the cloud. The present article proposed a new approach using a combination of the Sine–Cosine Algorithm and Salp Swarm Algorithm as discrete multi-objective and chaotic functions for optimal virtual machine placement. The first goal of the proposed algorithm was to reduce the power consumption in cloud data centers by condensing the number of active physical machines. The second goal was to reduce the waste of resources and manage it by optimally virtual machine placement on physical machines in cloud data centers. The third objective was to minimize and reduce Service Level Agreement among the active physical machines in cloud data centers. The proposed method prevent the increase in the migration of virtual machines onto physical machines. Ultimately, the results obtained from the proposed algorithm were compared with those of previous akin algorithms in the literature, including First Fit, Virtual Machine Placement Ant Colony System, and Modified Best Fit Decreasing. The proposed scheme is tested using Amazon EC2 Instances and the result indicated that the proposed algorithm performs better than the existing algorithms for various performance metrics.

Energy and quality of service-aware virtual machine consolidation in a cloud data center

The large-scale virtualized Cloud data centers consume huge amount of electrical energy leading to high operational costs and emission of greenhouse gases. Virtual machine (VM) consolidation has been found to be a promising approach to improve resource utilization and reduce energy consumption of the data center. However, aggressive consolidation of VMs tends to increase the number of VM migrations and leads to over-utilization of hosts. This in turn affects the quality of service (QoS) of the applications running in the VMs. Thus, reduction in energy consumption and at the same time ensuring proper QoS to the Cloud users are one of the major challenges among the researchers. In this paper, we have proposed an energy efficient and QoS-aware VM consolidation technique in order to address this problem. We have used Markov chain-based prediction approach to identify the over-utilized and under-utilized hosts in the data center. We have also proposed an efficient VM selection and placement policy based on linear weighted sum approach to migrate the VMs from over-utilized and under-utilized hosts considering both energy and QoS. Extensive simulations using real-world traces and comparison with state-of-art strategies show that our VM consolidation approach substantially reduces energy consumption within a data center while delivering suitable QoS.

A hybrid energy–Aware virtual machine placement algorithm for cloud environments

The high energy consumption of cloud data centers presents a significant challenge from both economic and environmental perspectives. Server consolidation using virtualization technology is widely used to reduce the energy consumption rates of data centers. Efficient Virtual Machine Placement (VMP) plays an important role in server consolidation technology. VMP is an NP-hard problem for which optimal solutions are not possible, even for small-scale data centers. In this paper, a hybrid VMP algorithm is proposed based on another proposed improved permutation-based genetic algorithm and multidimensional resource-aware best fit allocation strategy. The proposed VMP algorithm aims to improve the energy consumption rate of cloud data centers through minimizing the number of active servers that host Virtual Machines (VMs). Additionally, the proposed VMP algorithm attempts to achieve balanced usage of the multidimensional resources (CPU, RAM, and Bandwidth) of active servers, which in turn, reduces resource wastage. The performance of both proposed algorithms are validated through intensive experiments. The obtained results show that the proposed improved permutation-based genetic algorithm outperforms several other permutation-based algorithms on two classical problems (the Traveling Salesman Problem and the Flow Shop Scheduling Problem) using various standard datasets. Additionally, this study shows that the proposed hybrid VMP algorithm has promising energy saving and resource wastage performance compared to other heuristics and metaheuristics. Moreover, this study reveals that the proposed VMP algorithm achieves a balanced usage of the multidimensional resources of active servers while others cannot.

Evidence-Efficient Affinity Propagation Scheme for Virtual Machine Placement in Data Center

In cloud data center, without efficient virtual machine placement, the overload of any types of resources on physical machines (PM) can easily cause the waste of other types of resources, and frequent costly virtual machine (VM) migration, which further negatively affects quality of service (QoS). To address this problem, in this paper we propose an evidence-efficient affinity propagation scheme for VM placement (EEAP-VMP), which is capable of balancing the workload across various types of resources on the running PMs. Our approach models the problem of searching the desirable destination hosts for the live VM migration as the propagation of responsibility and availability. The sum of responsibility and availability represent the accumulated evidence for the selection of candidate destination hosts for the VMs to be migrated. Further, in combination with the presented selection criteria for destination hosts. Extensive experiments are conducted to compare our EEAP-VMP method with the previous VM placement methods. The experimental results demonstrate that the EEAP-VMP method is highly effective on reducing VM migrations and energy consumption of data centers and in balancing the workload of PMs.

Efficient virtual machine placement algorithms for consolidation in cloud data centers

Dynamic Virtual Machine (VM) consolidation is a successful approach to improve the energy efficiency and the resource utilization in cloud environments. Consequently, optimizing the online energy-performance tradeoff directly influences quality of service. In this study, algorithms named as CPU Priority based Best-Fit Decreasing (CPBFD) and Dynamic CPU Priority based Best-Fit Decreasing (DCPBFD) are proposed for VM placement. A number of VM placement algorithms are implemented and compared with the proposed algorithms. The algorithms are evaluated through simulations with real-world workload traces and it is shown that the proposed algorithms outperform the known algorithms. The simulation results clearly show that CPBFD and DCPBFD provide the least service level agreement violations, least VM migrations, and efficient energy consumption.

Managing Performance Interference Effects for Intelligent and Efficient Virtual Machines Placement based on GWO Approach in Cloud

Managing Performance Interference Effects for Intelligent and Efficient Virtual Machines Placement based on GWO Approach in Cloud

Energy and SLA Efficient Virtual Machine Placement in Cloud Environment Using Non-Dominated Sorting Genetic Algorithm

To increase the availability of the resources and simultaneously to reduce the energy consumption of data centers by providing a good level of the service are one of the major challenges in the cloud environment. With the increasing data centers and their size around the world, the focus of the current research is to save the consumption of energy inside data centers. Thus, this article presents an energy-efficient VM placement algorithm for the mapping of virtual machines over physical machines. The idea of the mapping of virtual machines over physical machines is to lessen the count of physical machines used inside the data center. In the proposed algorithm, the problem of VM placement is formulated using a non-dominated sorting genetic algorithm based multi-objective optimization. The objectives are: optimization of the energy consumption, reduction of the level of SLA violation and the minimization of the migration count.

Energy Efficient Virtual Machine Placement With an Improved Ant Colony Optimization Over Data Center Networks

Virtualization has achieved widespread adoption in various fields. As one of the most significant technologies in network virtualization, an effective virtual machine placement can facilitate to improve resource utilization in data center networks and cut down the enterprises' operation cost. In this paper, we proposed an energy efficient virtual machine placement scheme, which pursued to reduce communication cost and power consumption over traffic-aware data center networks. To solve such an optimization problem, an improved ant colony optimization with adaptive parameter setting was presented to balance its fast convergence and robust search capability. Compared with existing algorithms, simulation results demonstrated that our scheme achieved improvements in power consumption and communication cost, and had a significant reduction in run time under different traffic patterns and configurations.

Network-aware energy saving multi-objective optimization in virtualized data centers

With the current growth of data centers, improving energy saving is becoming more important to cloud service providers. The data centers architectural design and the advancement of virtualization technologies can be exploited for energy saving. In this paper, we studied the energy saving problem in data centers using virtual machines placement and live migration taking to account the status of the network links load. The problem was formulated as multi-objective integer linear program, which solvable by CPLEX, to minimize the energy consumed by the servers and minimize the time to migrate virtual machines. To overcome CPLEX high computation, a heuristic algorithm is introduced to provide practical and efficient virtual machines placement while minimizing their migration overhead to the network. The heuristic is evaluated in terms of energy consumed and performance using a real data center testbed that is stressed by running Hadoop Hibench benchmarks. The results where compared to the ones obtained by distributed resource scheduler (DRS) and the base case. The results show that the heuristic algorithm can save up to 30% of the server’s energy. For scalability and validity of optimality, the results of the heuristic were compared to the ones provided by CPLEX where the gap difference was less than 7%.

Chemical reaction optimization for virtual machine placement in cloud computing

With the development of virtualization technologies, cloud data centers are faced with more and more virtual machines (VMs) requests. How to realize an efficient virtual machine placement (VMP) becomes a hot research topic. The optimal resource consumption and the utilization rate of a physical machine in the whole cloud data center can be realized by optimizing the process from the virtual machine to the physical machine. VMP is a combinatorial optimization problem which was demonstrated to be NP-hard. In this paper, a new formulation of VMP problem is presented by taking into consideration the optimization of the two following objectives: (i) minimize the energy consumption, and (ii) maximize the resource utilization. In order to achieve these targets, we propose two algorithms based on chemical reaction optimization (CRO) algorithm, namely CVP and CVV, with two types of solution representation. The proposed algorithms are compared with other optimal placement strategies, namely Cuckoo Search Optimization (CSO), Reordered Grouping Genetic Algorithm (RGGA), First Fit Decreasing (FFD) and Best Fit Decreasing (BFD). Experimental results show that the proposed CVP and CVV give better performance comparing with the other compared algorithms in terms of resource consumption and resource utilization. In term of scalability, the proposed CVV algorithm benefits from the high computational speed and performs well when there are a large number of virtual machine scheduling requests in the cloud data center.

Improving Resource Utilization via Virtual Machine Placement in Data Center Networks

The resource utilization of servers (such as CPU, memory) is an important performance metric in data center networks (DCNs). The cloud platform supported by DCNs aims to achieve high average resource utilization while guaranteeing the quality of cloud services. Previous papers designed various efficient virtual machine placement schemes to increase the average resource utilization and decrease the server overload ratio. Unfortunately, most of virtual machine placement schemes did not contain the service level agreements (SLAs) and statistical methods. In this paper, we propose a correlation-aware virtual machine placement scheme that effectively places virtual machines on physical machines. First, we employ neural networks model and factor model to forecast the resource utilization trend data according to the historical resource utilization data. Second, we design three correlation-aware virtual machine placement algorithms to enhance resource utilization while meeting the user-defined SLAs. The simulation results show that the efficiency of our virtual machine placement algorithms outperforms the generic algorithm and constant variance algorithm by about 15%-30%.

Energy-Efficient Load-Aware VM Placement using Multi-Metrics Analysis

Objective: Cloud data centres comprise of a huge number of physical machines which host a number of heterogeneous virtual machines. These VMs exhibit fluctuating behaviour in terms of resource usage and specifications leading to disproportionate resource utilization in physical machines. This load imbalance on a host degrades its performance and violates many SLA features. To maintain even load in a data centre, an effective virtual machine placement technique is required. This paper proposes an efficient VM placement and migration technique based on three-tier architecture and considers load as a prime objective. Method: To maintain an even-load in a data centre, many load balancing techniques have been suggested which consider either migration cost or heterogeneity as the overall objective. This paper uses a multi-metrics analysis to distribute VMs evenly and maintains a stable equilibrium inside a data centre. We have applied Analytical Hierarchy Process (AHP) for efficient virtual machine placement using four post placement metrics which defines some of the key Service Level Agreement (SLA) parameters. The metrics considered for placement and migration are all load-centric and promise fewer migrations and SLA violations. Findings: Simulation results show a remarkable reduction in migrations which improves energy conservation inside the data centre. Application of AHP in balancing a data centre’s load is still unexplored. The presented placement technique selects the best candidate machine for placement, hence upgrades the performance. Improvement: Further enhancement includes adding more metrics for placement to fine tune performance and ensure better resource utilization along with reduction in overall energy consumption of a data centre.

Energy-aware framework with Markov chain-based parallel simulated annealing algorithm for dynamic management of virtual machines in cloud data centers

Significant savings in the energy consumption, without sacrificing service level agreement (SLA), are an excellent economic incentive for cloud providers. By applying efficient virtual Machine placement and consolidation algorithms, they are able to achieve these goals. In this paper, we propose a comprehensive technique for optimum energy consumption and SLA violation reduction. In the proposed approach, the issues of allocation and management of virtual machines are divided into smaller parts. In each part, new algorithms are proposed or existing algorithms have been improved. The proposed method performs all steps in distributed mode and acts in centralized mode only in the placement of virtual machines that require a global vision. For this purpose, the population-based or parallel simulated annealing (SA) algorithm is used in the Markov chain model for virtual machines placement policy. Simulation of algorithms in different scenarios in the CloudSim confirms better performance of the proposed comprehensive algorithm.