Virtual Machine Placement Approach Research Articles

DBSCAN based approach for energy efficient VM placement using medium level CPU utilization

Virtual machine placement (VMP) is a popular problem in Cloud Data Centers (CDCs). An efficient virtual machine (VM) allocation is essential for processor speed and energy saving. This is more useful where the CDC uses an Internet of Things (IoT) infrastructure. To enhance energy savings, we aim to improve the adaptive four thresholds energy-aware framework for VM deployment. We observed that the role of the threshold for identifying the over-loaded host is crucial. In order to determine the appropriate threshold, we employed density-based spatial clustering of applications with noise (DBSCAN), medium absolute deviation (MAD), and interquartile range (IQR) using the medium fit power efficient decreasing (MFPED) algorithm. Our proposed algorithm modified medium fit energy efficient decreasing (MMFEED) achieves a reduction in energy consumption of 47.3%, 46.1%, 39%, 23.2%, 10.9%, and 3.4% compared to the IQR, MAD, static threshold (THR), exponential weighted moving average (EWMA), modified energy-efficient virtual machine placement (MEEVMP), and adaptive four threshold energy-aware framework for VM deployment energy efficient (AFED-EF), respectively, under the minimum migration time (MMT) selection policy. The proposed algorithm outperforms these algorithms in terms of energy consumption for VM selection policy MMT.

High performance computing with inter-virtual-machine communication

ABSTRACT Most applications use Transport Layer Security (TLS) as a default to encrypt network traffic to increase privacy and security. The adversary, however, also used LS encryption to conceal attacks or command-and-control communications. Encryption is essential for deciphering and assessing risks associated with communications. This issue should be resolved in a way that provides effective and covert decryption without compromising overall security. As a result of server consolidation, virtualization has emerged as a key technique for increasing data center energy efficiency. Consolidation of servers relies on the location of virtual machines. The placement of virtual machines has been approached from many different perspectives in recent years. Virtual machine placement approaches, however, only take into account the energy consumption of the physical machines within a data center, not the energy consumption of the communication network. Through the application domain, CVM offers a model-driven, user-centered approach to design, synthesis, and communication solutions by utilizing a fundamentally new method for handling these issues. This proposal discusses the concept, architecture, model languages, design prototypes, and implementation of CVM, arguing that it is a significantly more effective approach to technical communication solutions.

Seagull optimization algorithm based multi-objective VM placement in edge-cloud data centers

Edge-Cloud Datacenters (ECDCs) have been massively exploited by the owners of technology and industrial centers to satisfy the user demand. At the same time, the amount of energy used by these data centers is considerable. To address this challenge, Virtual Machine (VM) placement of the ECDCs plays an important role; therefore, assigning VM properly to physical machines (PM) can significantly decrease the amount of energy consumption. The applied assigning technique simultaneously must consider additional objectives involving traffic and power usage of the network elements, which makes it a challenging problem. This paper proposes a multi-objective VM placement approach in edge-cloud data centers, which uses Seagull optimization to optimize power and network traffic together. In this strategy, the network traffic among PMs is reduced by concentrating the communications of VMs on the same PMs to reduce the amount of transferred data through the network and reduce the PMs’ power consumption by consolidating VMs to fewer PMs, which consumes less energy. We evaluate with simulations in CloudSim and test two different network topologies, VL2 (Virtual Layer 2) and three-tier, to validate that the proposed approach can effectively reduce traffic and power consumption in ECDCs. The experimental results show that our proposed method can decrease energy consumption by 5.5% while simultaneously reducing network traffic by 70% and the power consumption of the network components by 80%.

A multi-objective approach for energy-efficient and reliable dynamic VM consolidation in cloud data centers

The rapid growth of cloud computing in the last decade has led to an increasing concern about the energy requirement of cloud data centers. Dynamic virtual machine (VM) consolidation is an effective way to tackle this issue, where VMs are executed on as few physical machines (PMs) as possible. Meanwhile, VM placement must be performed strategically, by considering different factors of the available resources to optimal exploitation of them. Moreover, a major challenge is the system’s reliability degradation because of the high frequency of consolidation and placing VMs on unreliable PMs. In this paper, we address the problem by introducing a discrete-time Markov chain (DTMC) model to predict future resource usage. Using the DTMC model along with the reliability model of PMs leads to more accurate PMs categorization based on their status. Then, a multi-objective VM placement approach is proposed to achieve the optimal VMs to PMs mapping using the ε-dominance-based multi-objective artificial bee colony (ε-MOABC) algorithm which can efficiently balance the overall energy consumption, resource wastage, and the system reliability to meet SLA and QoS requirements. We have validated the effectiveness of our proposed approach by conducting a performance evaluation study using the CloudSim toolkit. Competitive analysis of the experimental results demonstrates that the proposed approach significantly improves energy consumption while avoiding the inefficient VM migrations.

Computation of workflow scheduling using backpropagation neural network in cloud computing: a virtual machine placement approach

For measuring the efficiency of workflow scheduling, determining makespan and execution cost is essential. As estimating makespan and cost is difficult in a Cloud environment, designing an efficient computation of workflow scheduling remains a challenge. The Cloud resources are scaled up and down in accordance with user demand by following a scheduling policy. The scalability of the work environment is achieved through the virtualization process. Based on system experience, this paper proposes the priority-based backfilling backpropagation neural network (PBF-NN) hybrid scheduling algorithm for measuring makespan and execution cost accurately. The backfill algorithm is used to schedule tasks to the available resources. The percentage of migration is reduced when this algorithm is used compared to the First Come First Serve algorithm. Then, the Berger model is used to measure the fairness of resource allocation. The system decides task reallocation based on the fairness value. The backpropagation neural network handles the virtual machine placement process with necessary training and testing. The proposed algorithm dynamically allocates the tasks and reduces the utilization of resources. We use an experimental study to illustrate how the proposed system enables higher efficiency in cost, makespan, and performance.

Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center

In data centers, cloud-based services are usually deployed among multiple virtual machines (VMs), and these VMs have data traffic dependencies on each other. However, traffic dependency between VMs has not been fully considered when the services running in the data center are expanded by creating additional VMs. If highly dependent VMs are placed in different physical machines (PMs), the data traffic increases in the underlying physical network of the data center. To reduce the amount of data traffic in the underlying network and improve the service performance, we propose a traffic-dependency-based strategy for VM placement in software-defined data center (SDDC). The traffic dependencies between the VMs are analyzed by principal component analysis, and highly dependent VMs are grouped by gravity-based clustering. Each group of highly dependent VMs is placed within an appropriate PM based on the Hungarian matching method. This strategy of dependency-based VM placement facilitates reducing data traffic volume of the data center, since the highly dependent VMs are placed within the same PM. The results of the performance evaluation in SDDC testbed indicate that the proposed VM placement method efficiently reduces the amount of data traffic in the underlying network and improves the data center performance.

An energy-efficient VM placement method for cloud data centers using a hybrid genetic algorithm

PurposeThis purpose of this paper is to propose a novel hybrid genetic algorithm based on a virtual machine (VM) placement method to improve energy efficiency in cloud data centers. How to place VMs on physical machines (PMs) to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers. Over the past few years, many approaches for VM placement (VMP) have been proposed; however, existing VM placement approaches only consider energy consumption by PMs, and do not consider the energy consumption of the communication network of a data center.Design/methodology/approachThis paper attempts to solve the energy consumption problem using a VM placement method in cloud data centers. Our approach uses a repairing procedure based on a best-fit decreasing heuristic to resolve violations caused by infeasible solutions that exceed the capacity of the resources during the evolution process.FindingsIn addition, by reducing the energy consumption time with the proposed technique, the number of VM migrations was reduced compared with existing techniques. Moreover, the communication network caused less service level agreement violations (SLAV).Originality/valueThe proposed algorithm aims to minimize energy consumption in both PMs and communication networks of data centers. Our hybrid genetic algorithm is scalable because the computation time increases nearly linearly when the number of VMs increases.

A learning‐based approach for virtual machine placement in cloud data centers

SummaryIn recent years, the increasing use of cloud services has led to the growth and importance of developing cloud data centers. One of the challenging issues in the cloud environments is high energy consumption in data centers, which has been ignored in the corporate competition for developing cloud data centers. The most important problems of using large cloud data centers are high energy costs and greenhouse gas emission. So, researchers are now struggling to find an effective approach to decreasing energy consumption in cloud data centers. One of the preferred techniques for reducing energy consumption is the virtual machines (VMs) placement. In this paper, we present a VM allocation algorithm to reduce energy consumption and Service Level Agreement Violation (SLAV). The proposed algorithm is based on best‐fit decreasing algorithm, which uses learning automata theory, correlation coefficient, and ensemble prediction algorithm to make better decisions in VM allocation. The experimental results indicated improvement regarding energy consumption and SLAV, compared with well‐familiar baseline VM allocation algorithms.

Multi-Objective Optimization Approach for Virtual Machine Placement Based on Particle Swarm Optimization in Cloud Data Centers

Multi-Objective Optimization Approach for Virtual Machine Placement Based on Particle Swarm Optimization in Cloud Data Centers

Network failure–aware redundant virtual machine placement in a cloud data center

SummaryCloud has become a very popular infrastructure for many smart city applications. A growing number of smart city applications from all over the world are deployed on the clouds. However, node failure events from the cloud data center have negative impact on the performance of smart city applications. Survivable virtual machine placement has been proposed by the researchers to enhance the service reliability. Because of the ignorance of switch failure, current survivable virtual machine placement approaches cannot achieve the best effect. In this paper, we study to enhance the service reliability by designing a novel network failure–aware redundant virtual machine placement approach in a cloud data center. Firstly, we formulate the network failure–aware redundant virtual machine placement problem as an integer nonlinear programming problem and prove that the problem is NP‐hard. Secondly, we propose a heuristic algorithm to solve the problem. Finally, extensive simulation results show the effectiveness of our algorithm.

Simulated annealing based VM placement strategy to maximize the profit for Cloud Service Providers

Virtual machine (VM) placement strategies reported in the literature focuses mainly on minimization of power consumption and maximization of placed VMs. The revenue earned by a cloud service provider (CSP) depends on the number of VMs placed. Increasing the number of VMs placed by a CSP not only increases the power consumption but also decreases the profit margin of the CSP. In this paper, we propose a technique called maximum VM placement with minimum power consumption (MVMP) to maximize the profit earned by a CSP. The proposed technique attempts to maximize the revenue and minimize the power budget. It is formulated as a bi-objective optimization problem, and is solved using simulated annealing (SA) technique. To reach a sub-optimal solution more randomness is applied to SA. Our MVMP algorithm is compared to five state of the art algorithms in the realm of strategic VM placement, namely Marotta and Avallone (MA) approach, Hybrid genetic algorithm (HGA), Modified Best-Fit decreasing (MBFD), First-Fit decreasing (FFD) and Random deployment. We observe that MVMP performs better than Marotta and Avallone (MA) approach, HGA, MBFD, FFD and Random placement in terms of number of servers used, energy consumption, profit and execution time. Scalability of MVMP is verified using two different scenarios: (i) fixed number of VMs and, (ii) fixed number of servers. It is observed that MVMP is scalable too.

Energy Efficient Virtual Machine Placement Approach for Balanced Resource Utilization in Cloud Environment

Cloud is a business model that provides on demand computing resources to the clients on rental basis. It is an utility model, so clients need to pay only for that amount of resources which is actually used. Cloud provider want to gain more and more profit that can be possible only by effective resource utilization. For this purpose virtualization technology is used, which allows sharing of physical resources. In virtualization hypervisor provide requested resources to the client in the form of virtual machine. Virtualization increases the resource utilization by creating the number of VM in a single host, but wrong VM placement can increase resource leakage. Resource leakage can be defined as an amount of waste resources that can’t be used. Resources in the cloud are in multi dimensions i.e. CPU, RAM, bandwidth etc. So resource leakage is one of the major problems in the cloud environment. Balanced resource utilization in the cloud is more challenging task as compared to the traditional distributed computing paradigms, due to dynamic behavior of the VM resource requirement. Number of virtual machine placement algorithms has been proposed that run under cloud computing environment. Main goal of these algorithms are either to save energy by shutting down some severs or maximizing the resources utilization, but few of them considered both. This paper proposes a VM placement approach that increases the balanced resources utilization and reduced power consumed by the data centers. For the balanced resources utilization six clusters CMB, CBM, MBC, MCB, BMC and BCM are used, where C, M and B stands for CPU, memory and bandwidth respectively. Initially some host assigned to each cluster according to the remaining capacity. CBM cluster keep the host where remaining CPU > remaining bandwidth > remaining RAM. Host in the CBM cluster can run only that VM, which require more CPU and minimum RAM. This approach is motivated by the concept that VM is assigned to PM such that, resource requirement of the VM are complement with the resource utilization of the PM. CloudSim simulator is used to implement this approach. Experimental result shows that this approach increased the resource utilization, reduced the resource leakage and reduced the energy consumption by minimizing the number of active server.

A Hybrid Genetic Algorithm for the Energy-Efficient Virtual Machine Placement Problem in Data Centers

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation technology. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches consider the energy consumption by physical machines only, but do not consider the energy consumption in communication network, in a data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement. In our preliminary research, we have proposed a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both physical machines and the communication network in a data center. Aiming at improving the performance and efficiency of the genetic algorithm, this paper presents a hybrid genetic algorithm for the energy-efficient virtual machine placement problem. Experimental results show that the hybrid genetic algorithm significantly outperforms the original genetic algorithm, and that the hybrid genetic algorithm is scalable.

CPU Frequency Scaling Algorithm for Energy-saving in Cloud Data Centers

High energy consumption becomes an urgent problem in cloud datacenters. Based on virtualization tech- nologies, the pay-as-you-go resource provision paradigm has become a trend. Specifically, Virtual Machine (VM) is the basic resource unit in data center for resource migration and provisioning. Many researches have been devoted to improve datacenter resource utilization and reduce power consumption by VM placement. As the most important power consumption resource, CPU has a fluctuant frequency range. Based on CPU frequency scaling, a new approach for VMs placement is proposed. The approach is realized in two stages. In the initial stage, we propose a multi- objective heuristic ant colony algorithm, which will find the optimization solution for energy saving as well as service- level agreement (SLA). In the dynamic stage, by using autoregressive prediction and CPU frequency scaling, the proposed approach can adjust the CPU utilization if needed, not depending on whole VM migration. The experiments show that the energy saving algorithms based on CPU frequency scaling are much better than the traditional BFD and FFD algorithms in saving energy and satisfying SLA.

CCAP: A Cache Contention-Aware Virtual Machine Placement Approach for HPC Cloud

Applications in High Performance Computing (HPC) cloud are characterized by large cache resource consumption due to large-scale inputs and intensive communications, which creates serious Shared Last Level cache (SLLC) performance bottleneck. Current system software stacks are not efficient in addressing this issue among virtual machines at the hypervisor level or the threads at the operating system level. In this paper, we investigate performance interference due to contention for SLLC in the HPC cloud. We employ an enhanced reuse distance analysis technique with an accelerated cyclic compression algorithm to identify application's cache interference intensity. Based on reuse distance analysis, we propose a practical Cache Contention-Aware virtual machine Placement approach (CCAP). CCAP dispatches virtual machines according to their cache interference intensities to avoid cache pollution and interference, thus alleviating negative effects of cache contention. We implement CCAP in the Xen hypervisor. Evaluation of NPB workload reveals that CCAP can improve performance of cache sensitive applications when they are co-scheduled with cache pollution programs. For a 2-workload system, it reduces execution time by 12 %, as well as cache miss rate by 13 %, while increasing throughput by 13 %, on average. Moreover, CCAP also improves the average performance of the cache pollution programs by 5 %. For a 4-workload system, CCAP brings more significant performance improvement to cache sensitive applications, an average increase of 20 %.