Virtual Machine Selection Policies Research Articles

Utilisation aware virtual machine selection policy for workload consolidation in cloud data centres

Large-scale virtualised data centres have been constructed throughout the world in response to the rising demand for service-oriented computing and the expansion of cloud computing technologies. These vast data centres consume a significant amount of power and have a significant carbon footprint, which must be reduced to the greatest extent feasible. The dynamic virtual machine consolidation provided by live migration leads to significant energy savings. However, it also constitutes a violation of the service level agreement (SLA). The process of selecting virtual machine (VM) for migration is critical in the realm of energy-aware cloud computing. This study proposes a novel utilisation aware VM selection (UAVMS) policy that aids in VM selection for migration using server utilisation and skewness value. We use the CloudSim toolkit to build our UAVMS policy and compare its performance with existing methods. The experimental results shows that UAVMS reduces the energy usage and SLA violations.

Research on virtual machine consolidation strategy based on combined prediction and energy-aware in cloud computing platform

In the era of information explosion, the energy consumption of cloud data centers is significant. It’s critical to reduce the energy consumption of large-scale data centers while guaranteeing quality of service (QoS), especially the energy consumption of video cloud computing platforms. The application of virtual machine (VM) consolidation has been regarded as a promising approach to improve resource utilization and save energy of the data centers. In this paper, an energy efficient and QoS-aware VM consolidation method is proposed to address the issues. A combined prediction model based on grey model and ARIMA is applied to host status detection, and we provide a new scheme that VM placement policy based on resource utilization and varying energy consumption to search most suitable host and VM selection policy called AUMT selecting VM with low average CPU utilization and migration time. Extensive experimental results based on the cloudsim simulator demonstrate that proposed approach enables to achieve the objectives reducing energy consumption, number of migrations, SLAV and ESV by an average of 56.07%, 79.21%, 91.01% and 84.34% compared with the benchmark methods and the AUMT can reduce energy consumption, the number of migrations and ESV by an average of 15.46%, 28.11% and 3.96% compared with the state-of-the-art method.

An efficient host overload detection algorithm for cloud data center based on exponential weighted moving average

SummaryThe major aim of data center (DC) is to optimize the three parameters, viz., energy consumption, resource utilization, and quality of service (QoS). Our objective is to balance these three parameters by managing the virtual machine (VM) efficiently. Therefore, this paper presents a holistic view of VM consolidation and explains its constituent subprocesses: (i) host overload detection, (ii) VM selection for migration from the overloaded host, and (iii) VM placement for provisioning selected VMs on a new set of hosts. Later, this paper attempts to optimize the parameters mentioned above by proposing a host overload detection algorithm based on exponential weighted moving average (EWMA) and its variants. The performance of the proposed EWMA is compared with the state‐of‐the‐art algorithms based on (i) local regression, (ii) median absolute deviation, and (iii) interquartile range. The efficacy of the proposed EWMA is evaluated in combination with four different existing VM selection policies: (i) minimum migration time, (ii) minimum utilization, (iii) maximum correlation, and (iv) random selection. The evaluation metrics comprise energy consumption, VM migration count from the overloaded hosts representing the resource utilization, service level agreement (SLA) violations describing the QoS, and the average execution time. Simulation experiments are carried out on the CloudSim simulator using PlanetLab real cloud trace as workload. The proposed EWMA consumes 18.33% less energy, causes 47.81% less VM migration, makes 9.91% less SLA violation, and takes 43.44% less average‐time for the execution of the entire workload as compared to state‐of‐the‐art algorithms.

Auto Resource Management to Enhance Reliability and Energy Consumption in Heterogeneous Cloud Computing

A classic information processing has been replaced by cloud computing in more studies where cloud computing becomes more popular and growing than other computing models. Cloud computing works for providing on-demand services for users. Reliability and energy consumption are two hot challenges and tradeoffs problem in the cloud computing environment that requires accurate attention and research. This paper proposes an Auto Resource Management (ARM) scheme to enhance reliability by reducing the Service Level Agreement (SLA) violation and reduce energy consumed by cloud computing servers. In this context, the ARM consists of three compounds, they are static/dynamic threshold, virtual machine selection policy, and short prediction resource utilization method. The Minimum Utilization Non-Negative (MUN) virtual machine selection policy and Rate of Change (RoC) dynamic threshold present in this paper. Also, a method of choosing a value as the static threshold is proposed. To improve ARM performance, the paper proposes a Short Prediction Resource Utilization (SPRU) that aims to improve the process of decision making by including the resources utilization of future time and the current time. The output results show that SPRU enhanced the decision-making process for managing cloud computing resources and reduced energy consumption and the SLA violation. The proposed scheme tested under real workload data over the Cloud Sim simulator.

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.

An Experience-Based Scheme for Energy-SLA Balance in Cloud Data Centers

The proliferation of cloud computing has resulted in the establishment of large-scale data centers containing thousands of computing nodes and consuming enormous amounts of electrical energy. However, the low-cost and high-efficiency slogans are getting louder and louder, and the IT industry is also striving for this pursuit. Therefore, it is vital to minimizing the energy consumption for cloud providers while ensuring the quality of service for cloud users. In this paper, we propose several heuristic strategies to optimize these two metrics based on a two-level management model under a heterogeneous cloud environment. First, to detect whether a physical node is continuously overloaded, we propose an empirical forecast algorithm, which predicts the future state of the host by statistically analyzing the historical utilization data of the host. Second, we propose a weighted priority virtual machine (VM) selection algorithm. For each VM on the overloaded host, we weight several utilization factors and calculate its migration priority. Then, we simulate the proposed approach and compare it with the existing overloaded hosts detection algorithms with different VM selection policies under different workloads.

Adaptive Energy-Aware Algorithms for Minimizing Energy Consumption and SLA Violation in Cloud Computing

In cloud computing, high energy consumption and service-level agreements (SLAs) violation are the challenging issues considering that the demand for computational power is growing rapidly, thereby requiring large-scale cloud data centers. Although, there are many existing energy-aware approaches focusing on minimizing energy consumption while ignoring the SLA violation at the time of a virtual machine (VM) selection from overloaded hosts. Also, they do not consider that the current network traffic causes performance degradation and thus may not really reduce SLA violation under a variety of workloads. In this context, this paper proposes three adaptive models, namely, gradient descent-based regression (Gdr), maximize correlation percentage (MCP), and bandwidth-aware selection policy (Bw), that can significantly minimize energy consumption and SLA violation. Energy-aware methods for overloaded host detection and VM selection from an overloaded host are necessary to improve the energy efficiency and SLA violation of a cloud data center after migrating all VM from underloaded host turn to idle host, which switch to energy-saving mode is also beneficial. Gdr and MCP are adaptive energy-aware algorithms based on the robust regression model, for overloaded host detection. A Bw dynamic VM selection policy selects VM according to the network traffic from the overloaded host under SLAs. Experimental results on the real workload traces show that the proposed algorithms reduce energy consumption while maintaining the required performance levels in a cloud data center using a CloudSim simulator to validate the proposed algorithms.

Energy-Aware Dynamic Virtual Machine Consolidation for Cloud Datacenters

Resource over provisioning in cloud computing consumes energy excessively. Energy-aware dynamic virtual machine consolidation (DVMC) reduces energy consumption without compromising service level agreement. In this paper, we put forward a new framework of DVMC for green cloud computing. In particular, we propose a new virtual machine (VM) placement policy, namely, space aware best fit decreasing (SABFD) and a new migration VM selection policy, namely, high CPU utilization-based migration VM selection (called HS). Thorough simulations are carried out to evaluate the performances of different energy-aware DVMC plans based on real-world workload traces, with DVMC plans as various combinations of host overload detection, migration VM selection, and VM placement policies. The simulation results show that DVMC plans with SABFD policy or with HS policy outperforms alternative DVMC plans. What is more, a DVMC plan with both SABFD and HS policies makes the best performance.

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.

DataABC: A fast ABC based energy-efficient live VM consolidation policy with data-intensive energy evaluation model

Live Virtual Machine (VM) consolidation is an effective method of improving energy-efficiency level in green data centers. Currently, to evaluate energy consumption in green data centers, energy-efficiency evaluation model with CPU utilization rate has been proposed. However, it is not suitable for data-intensive computing due to great energy consumption by GPU-intensive processing. In this paper, we have proposed a new energy evaluation model with CPU and GPU utilization rates. There are two kinds of policies in live VM consolidation: one for VM selection and the other for VM allocation. Some researchers have proposed their solutions based on VM selection policy or VM allocation policy respectively. However, it will be a better energy-efficiency VM consolidation policy if these two polices are integrated together. Based on these two policies, a fast Artificial Bee Colony (ABC) based energy-efficiency live VM consolidation policy with data-intensive energy model, named as DataABC, is proposed. DataABC adopts the idea of Artificial Bee Colony algorithm to get a fast and global optimized decision of VM consolidation. Compared with two state-of-art policies of PS-ABC and PS-ES, the total energy consumption of DataABC evidently drop by 9.72% and 5.84% respectively. As a result, based on the ESV metric, the DataABC approach has proved that (a) the energy-efficiency evaluation model with data-intensive computing is valid and that (b) DataABC can save energy with a good Quality of Service (QoS) in green data centers.

Workload Consolidation using VM Selection and Placement Techniques in Cloud Computing

Cloud computing provides a consumer pay-per-use computing model over the Internet using numerous data centers across the globe. Power consumption by the huge data centers in Cloud environment has attracted the attention of research community. Efficient usage of energy in Cloud can be addressed in many facets. Virtual Machine (VM) consolidation is one of the techniques to save or reduce energy in virtualized data centers. VM Migration in Cloud also provides us an opportunity for reducing energy consumption. In this research, we intend to study various VM placements & selection policies and VM migration algorithms for underloaded and overloaded hosts to reduce energy consumption and SLA violation. We propose a novel method using combination of two methods, Least Increase Power (LIP) consumption with Host Sort and Minimum Correlation Coefficient (MCC) for consolidation of VM placement, placing a migratable VM on a host based on utilization thresholds. The results show performance of each combination of algorithms varies with the changing value of the parameters brings better in terms of energy consumption, VM migration time and SLA violation. The reader may plunge the appropriate method for energy consumption.

Optimization of Dynamic Virtual Machine Consolidation in Cloud Computing Data Centers

The present study aims at recognizing the problem of dynamic virtual machine (VM) Consolidation using virtualization, live migration of VMs from underloaded and overloaded hosts and switching idle nodes to the sleep mode as a very effective approach for utilizing resources and accessing energy efficient cloud computing data centres. The challenge in the present study is to reduce energy consumption thus guarantee Service Level Agreement (SLA) at its highest level. The proposed algorithm predicts CPU utilization in near future using Time-Series method as well as Simple Exponential Smoothing (SES) technique, and takes appropriate action based on the current and predicted CPU utilization and comparison of their values with the dynamic upper and lower thresholds. The four phases in this algorithm include identification of overloaded hosts, identification of underloaded hosts, selection of VMs for migration and identification of appropriate hosts as the migration destination. The study proposes solutions along with dynamic upper and lower thresholds in regard with the first two phases. By comparing current and predicted CPU utilizations with these thresholds, overloaded and underloaded hosts are accurately identified to let migration happen only from the hosts which are currently as well as in near future overloaded and underloaded. The authors have used Maximum Correlation (MC) VM selection policy in the third phase, and attempted in phase four such that hosts with moderate loads, i.e. not overloaded hosts, liable to overloading and underloaded, are selected as the migration destination. The simulation results from the Clouds framework demonstrate an average reduction of 83.25, 25.23 percent and 61.1 in the number of VM migrations, energy consumption and SLA violations (SLAV), respectively.

Virtual Machine Placement Algorithm for Both Energy-Awareness and SLA Violation Reduction in Cloud Data Centers

The problem of high energy consumption is becoming more and more serious due to the construction of large-scale cloud data centers. In order to reduce the energy consumption and SLA violation, a new virtual machine (VM) placement algorithm named ATEA (adaptive three-threshold energy-aware algorithm), which takes good use of the historical data from resource usage by VMs, is presented. In ATEA, according to the load handled, data center hosts are divided into four classes: hosts with little load, hosts with light load, hosts with moderate load, and hosts with heavy load. ATEA migrates VMs on heavily loaded or little-loaded hosts to lightly loaded hosts, while the VMs on lightly loaded and moderately loaded hosts remain unchanged. Then, on the basis of ATEA, two kinds of adaptive three-threshold algorithm and three kinds of VMs selection policies are proposed. Finally, we verify the effectiveness of the proposed algorithms by CloudSim toolkit utilizing real-world workload. The experimental results show that the proposed algorithms efficiently reduce energy consumption and SLA violation.

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.

An Adaptive Genetic Algorithm based Robust QoS Oriented Green Computing Scheme for VM Consolidation in Large Scale Cloud Infrastructures

Backgrounds: The high pace increase in cloud applications requires an optimal computing platform like, Virtual Machine (VM) Consolidationor virtualization to ensure optimal computational efficiency, energy consumption and minimal SLA violation. Methods: In this paper, an evolutionary computing approach called Adaptive Genetic Algorithm (A-GA) has been proposed for VM placement policy, to be used in VM consolidation. In the proposed model, the modified Robust Local Regression (LRR) and Inter-Quartile Range (IQR) schemes estimate the dynamic CPU utilization for overload detection, which is followed by Maximum Correlation (MC) and Minimum Migration Time (MMT) based VM selectionand A-GA based VM placement. Findings: The comparative performance analysis for the proposed system with Planet Lab cloud benchmark dataset has exhibited that the proposed model exhibits better results as compared to other heuristic approaches such as Best Fit Decreasing (BFD) algorithm and Ant Colony Optimization (ACO). The implementation of the proposed A-GA based consolidation with modified IQR and LRR, and MMT selection policyhas performed better in terms of energy efficiency and SLA violation as compared to the other heuristic approaches for placement such as Best Fit Decreasing (BFD) algorithm with conventional IQR, Local Regression (LR), robust local regression, Static Threshold (THR) and Median Absolute Deviation (MAD) based CPU utilization threshold estimation schemes. Furthermore, the proposed A-GA based scheme has outperformed Ant Colony Optimization (ACO) based consolidation scheme. The performance analysis with two distinct VM selection policies, MC and MMT has revealed that A-GA performs better with MMT selection policy and provides higher host shutdown, minimal VM migration and SLA violation, and minimal energy consumption. Applications: The proposed A-GAbased VM consolidation scheme can be significant for energy aware and QoS oriented virtualization application in large scale cloud infrastructures.

A novel virtual machine deployment algorithm with energy efficiency in cloud computing

In order to improve the energy efficiency of large-scale data centers, a virtual machine (VM) deployment algorithm called three-threshold energy saving algorithm (TESA), which is based on the linear relation between the energy consumption and (processor) resource utilization, is proposed. In TESA, according to load, hosts in data centers are divided into four classes, that is, host with light load, host with proper load, host with middle load and host with heavy load. By defining TESA, VMs on lightly loaded host or VMs on heavily loaded host are migrated to another host with proper load; VMs on properly loaded host or VMs on middling loaded host are kept constant. Then, based on the TESA, five kinds of VM selection policies (minimization of migrations policy based on TESA (MIMT), maximization of migrations policy based on TESA (MAMT), highest potential growth policy based on TESA (HPGT), lowest potential growth policy based on TESA (LPGT) and random choice policy based on TESA (RCT)) are presented, and MIMT is chosen as the representative policy through experimental comparison. Finally, five research directions are put forward on future energy management. The results of simulation indicate that, as compared with single threshold (ST) algorithm and minimization of migrations (MM) algorithm, MIMT significantly improves the energy efficiency in data centers.

The Effects of Hotspot Detection and Virtual Machine Migration Policies on Energy Consumption and Service Levels in the Cloud

Managing Cloud resources efficiently necessitates effective policies that assign applications to hardware in a way that they require the least resources possible. Applications are first assigned to virtual machines which are subsequently placed on the most appropriate server host. If a server becomes overloaded, some of its virtual machines are reassigned. This process requires a hotspot detection mechanism in combination with techniques that select the virtual machine(s) to migrate. In this work we introduce two new virtual machine selection policies, Median Migration Time and Maximum Utilisation, and show that they outperform existing approaches on the criteria of minimising energy consumption, service level agreement violations and the number of migrations when combined with different hotspot detection mechanisms. We show that parametrising the the hotspot detection policies correctly has a significant influence on the workload balance of the system.