Virtual Machine Allocation Algorithm Research Articles

Virtual Machine Allocation Strategy Based on Statistical Machine Learning

At present, big data cloud computing has been widely used in many enterprises, and it serves tens of millions of users. One of the core technologies of big data cloud service is computer virtualization technology. The reasonable allocation of virtual machines on available hosts is of great significance to the performance optimization of cloud computing. We know that with the continuous development of information technology and the increasing number of computer users, different virtualization technologies and the increasing number of virtual machines in the network make the effective allocation of virtualization resources more and more difficult. In order to solve and optimize this problem, we propose a virtual machine allocation algorithm based on statistical machine learning. According to the resource requirements of each virtual machine in cloud service, the corresponding comprehensive performance analysis model is established, and the reasonable virtual machine allocation algorithm description of the host in the resource pool is realized according to the virtualization technology type or mode provided by the model. Experiments show that this method has the advantages of overall performance, load balancing, and supporting different types of virtualization.

VM Consolidation Plan for Improving the Energy Efficiency of Cloud

Abstract Achieving energy-efficiency with minimal Service Level Agreement (SLA) violation constraint is a major challenge in cloud datacenters owing to financial and environmental concerns. The static consolidation of Virtual Machines (VMs) is not much significant in recent time and has become outdated because of the unpredicted workload of cloud users. In this paper, a dynamic consolidation plan is proposed to optimize the energy consumption of the cloud datacenter. The proposed plan encompasses algorithms for VM selection and VM placement. The VM selection algorithm estimates power consumption of each VM to select the required VMs for migration from the overloaded Physical Machine (PM). The proposed VM allocation algorithm estimates the net increase in Imbalance Utilization Value (IUV) and power consumption of a PM, in advance before allocating the VM. The analysis of simulation results suggests that the proposed dynamic consolidation plan outperforms other state of arts.

EnTruVe: ENergy and TRUst-aware Virtual Machine allocation in VEhicle fog computing for catering applications in 5G

It is undoubted that fog computing contributes in catering the latency-stringent applications of 5G, and one of the enabling technologies that fundamentally ensures the success of fog computing is virtualization as it offers isolation and platform independence. Although the emergence of vehicle-based fog (referred to as v-fog) facilities can certainly benefit from these desirable features of virtualization, there are several challenges that need to be addressed in order to realize the full potential that v-fogs can offer. One of the challenges of virtualization in v-fog is Virtual Machine (VM) migration. There are several factors that trigger a VM migration in a v-fog such as vehicle resource depletion. VM migrations would not only lead to nonessential usage of valuable resources (e.g. energy, bandwidth, memory) in the v-fogs, but also incur various overheads and performance degradation throughout the whole network. Thus, minimizing VM migrations is necessary. Furthermore, to ensure the seamless VM migrations between v-fogs, trust of v-fogs is required. While there exists studies of trust in the virtualization of cloud, they are irrelevant to v-fogs as v-fogs are different in nature (i.e. heterogeneous, mobile) from the cloud. Additionally, trust is not included in the decision making mechanisms of VM allocation for vehicular environments in the existing works. Moreover, as vehicle resources are constrained, their energy has to be utilized efficiently. In this paper, we propose EnTruVe, an ENergy and TRUst-aware VM allocation in VEhicle fog computing solution that aims to minimize the number of VM migrations while reducing VM processing associated energy consumption as much as possible. The VM allocation algorithm in EnTruVe provides a larger selection pool of v-fogs that meets the VMs requirements (e.g. trust, latency), thereby ensuring higher chances of success of VM allocation. Using Analytic Hierarchy Process (AHP), the proposed EnTruVe solution evaluates the v-fogs based on a set of metrics (e.g. energy consumption and end-to-end latency) to select the optimal v-fog for a VM allocation. Results obtained demonstrate that EnTruVe has the least number of VM migrations and it is the most energy efficient solution. Additionally, it shows that EnTruVe provides the highest utilization of v-fogs of up to 57.6% in comparison to other solutions as the number of incoming requests increases.

Adaptive virtual machine migration based on performance-to-power ratio in fog-enabled cloud data centers

Fog computing as a complementary paradigm to cloud computing is a heuristic shift in service delivery that promises a leap in efficiency and flexibility for cloud-based Internet of Things applications. The performance characteristics of cloud/fog computing attract significant attention from researchers lately. One of the critical challenges in this field is controlling and reducing the massive amount of energy consumption in the cloudlets while still maintaining the Service Level Agreement’s performance requirements. Many virtual machine (VM) allocation and consolidation strategies are investigated to address the challenges mentioned earlier. However, many of the solutions save energy at the cost of performance degradation. This paper proposes a novel multi-step VM allocation algorithm called enhanced performance-to-power ratio for workflow applications ”E-PRWA” in cloud/fog environment. The proposed heuristic algorithm strives to achieve a trade-off between node performance and power consumption. Operating machine hosts at the highest performance-to-power ratio can save a tremendous amount of energy without degrading system performance. The proposed model consists of four stages: (a) detecting overutilized or underutilized nodes based on the preferred utilization (PU); (b) VM selection for migration from the overutilized nodes to underutilized nodes; (c) switching off selected underutilized nodes; (d) deploying the migration VMs based on the modified best-fit decreasing algorithm with PPR, latency overhead, and computational cost consideration. Extensive simulation results illustrate that compared with three baseline energy-efficient VM allocation and selection algorithms, E-PRWA can achieve an average of up to 65.41% of energy-saving with fewer migration number in fog computing.

HomeCloud Framework for Priority aware VM Allocation and Network Bandwidth Provisioning

Various applications in a multi-tenant architecture based cloud computing comprises various requirements and priorities. Severe failure of a critical application may result if the network is congested in a data center. Hence, it is an important to use an efficient resource provisioning technique for ensuring transfer of high priority data to the other traffics still when the network is congested. Therefore, a new framework of Home Cloud architecture is proposed in this paper to provide an effective and flexible delivery of new applications in an edge-cloud architecture which integrates the two complementary technologies of Software-Defined Networking (SDN) and Network Function Virtualization (NFV). In SDN infrastructure, this presents a priority aware resource placement algorithms by taking into consideration of both host and network resources. The Virtual Machines (VMs) of high priority applications are placed at the strongly connected hosts by introducing the priority aware VM allocation (PAVA) algorithm for reducing the possibility of causing network congestion by other tenants. It also guarantees the critical applications for a required bandwidth by means of allocating bandwidth through the setting of priority queues for each network connection within the network of data center handled by SDN controller. The simulation result of the proposed system shows that the combination of proposed approaches will allocate sufficient resources for high-priority applications within a Home Cloud data centre to meet the QoS requirements.

An extended intelligent water drop approach for efficient VM allocation in secure cloud computing framework

Cloud computing provides many advantages services to its users like online resources accessibility, better cost management, dynamic resource pooling, and efficient Virtual Machine (VM) allocation. With so many useful services, cloud computing also introduces several security risks of cloud user information privacy as well as select the minimum number of VM to execute task load and improve resource utilization are some challenging issues. The objective of this work is to achieve higher resource utilization rate while considering the security of the cloud data. We extended the natural phenomena based Intelligent Water Drop (IWD) algorithm and proposed a VM allocation algorithm which optimizes the task execution in a secure cloud environment. The proposed work is implemented on the CloudSim simulation toolkit, and to test the validity of the algorithm, a comparison is performed with other well-known VM allocation policies in the cloud computing. The experimental simulation results showed that the proposed VM allocation policy performed better than exiting VM allocation approaches.

VM Selection and Allocation Policy to Optimize VM Migration in Cloud Environment

Cloud computing, a metered based technology provides the services using virtualized technology over the internet. In the cloud environment, to improve the performance (such as utilization of the resources, energy minimization) extreme number of virtual machines (VMs) can be installed on the servers as per their resource capacity. In this way, servers can be overloaded. Overloaded servers consume more energythan normal status servers. VM migration (VMM) is an efficient technique to become a server in a normal state. VMM technique is used to consolidate the resources to increase resource utilization (RU) and reduceenergy usage. In the VMM technique, selection of VM such as which VM is migrated from one server to another server and allocation of VM on servers is an important aspect. Appropriate VM selection declines the numeral of VMMs and increasesenergy efficiency. Appropriate VM allocation declines the server to become overloaded. In this paper, the VM selection and allocation strategy is presented. CloudSim toolkit is used to verify the strength of proposed VM selection and allocation algorithm. Proposed VM Selection algorithm (MaMT) performs better than existing MiMT algorithm in terms of total energy consumption, number of hosts shut down, number of VMM, and average Service Level Agreement (SLA) violation rate. MaMT algorithm with resource aware provisioning (RAP) and MiMT+RAP algorithm combines both VM selection and allocation policies. RAP algorithm used both energy and RU parameters while allocating VM to the server.MaMTreduces the energy consumption up to 7.25% and reduces the SLA violation rate up-to 2.6% in comparison to MiMT algorithm. When VM selection and allocation policies combines together than more system performance is improved. MaMT+RAPreduces the energy consumption up to6.76% and reduces the SLA violation rate up-to 0.22% in comparison to MaMT algorithm.MiMT+RAPreduces the energy consumption up to15.23% and reduces the SLA violation rate up-to 0.95% in comparison to MiMT algorithm.

Virtual machine allocation and migration based on performance-to-power ratio in energy-efficient clouds

The last decade witnessed a dramatic advance in cloud computing research and techniques. One of the key challenges in this field is reducing the massive amount of energy consumption in cloud computing data centers. Many power-aware virtual machine (VM) allocation and consolidation approaches were proposed to reduce energy consumption efficiently. However, most of the existing efficient cloud solutions save energy at the cost of significant performance degradation. In this paper, we propose a strategy to calculate the optimized working utilization levels for host computers. As the performance and power data need to be measured on real platforms, to make our design practical, we propose a strategy named PPRGear'' which is based on the sampling of utilization levels with distinct Performance-to-Power Ratios (PPR) calculated as the number of Server Side Java operations completed during a certain time period divided by the average active power consumption in that period. In addition, we present a framework for virtual machine allocation and migration which leverages the PPR for various host types. By achieving the optimal balance between host utilization and energy consumption, our framework is able to ensure that host computers run at the most power-efficient utilization levels, i.e., the levels with the highest PPR, thus tremendously reducing energy consumption with ignorable sacrifice of performance. Our extensive experiments with real world traces show that compared with three baseline energy-efficient VM allocation and selection algorithms, IqrMc, MadMmt, and ThrRs, our framework is able to reduce the energy consumption up to 69.31% for various host computer types with fewer migration times, shutdown times, and little performance degradation for cloud computing data centers. (C) 2019 Elsevier B.V. All rights reserved.

Energy-Aware Virtual Machine Clustering for Consolidation in Multi-tenant IaaS Public Clouds

Cloud computing has gained a lot of interest from both small and big academic and commercial organizations because of its success in delivering service on a pay-as-you-go basis. Moreover, many users (organizations) can share server computing resources, which is made possible by virtualization. However, the amount of energy consumed by cloud data centres is a major concern. One of the major causes of energy wastage is the inefficient utilization of resources. For instance, in IaaS public clouds, users select Virtual Machine (VM) sizes set beforehand by the Cloud Service Providers (CSPs) without the knowledge of the kind of workloads to be executed in the VM. More often, the users overprovision the resources, which go to waste. Additionally, the CSPs do not have control over the types of applications that are executed and thus VM consolidation is performed blindly. There have been efforts to address the problem of energy consumption by efficient resource utilization through VM allocation and migration. However, these techniques lack collection and analysis of active real cloud traces from the IaaS cloud. This paper proposes an architecture for VM consolidation through VM profiling and analysis of VM resource usage and resource usage patterns, and a VM allocation policy. We have implemented our policy on CloudSim Plus cloud simulator and results show that it outperforms Worst Fit, Best Fit and First Fit VM allocation algorithms. Energy consumption is reduced through efficient consolidation that is informed by VM resource consumption.

An efficient virtual machine allocation algorithm for parallel and distributed simulation applications

SummaryAllocating appropriate resource for parallel and distributed simulation (PADS) applications in clouds is an intuitive way to improve their execution efficiency. However, the heterogeneity of virtual machine (VMs) in clouds with respect to both their computing power and network latency influences the execution efficiency of PADS applications on different combinations of VMs. Besides, frequent synchronization is one of the characteristics during the execution of PADS applications, which seriously challenges the prediction of the influence of VMs' computing power and network latency on their execution efficiency, and makes allocating appropriate VMs difficult as a result. This paper first proposes a revivification‐based prediction model (ERP), which revives the execution based on statistical data from actual execution of PADS applications to predict the running time of PADS applications on different combinations of VMs. Then, an ERP‐based Allocation algorithm, namely, ERPA, is raised to optimize VMs allocation to minimize the running time of PADS applications in clouds. A series of experiments are conducted to compare the proposed ERPA with three resource allocation algorithms, ie, Gang‐scheduling‐based, Makespan‐based, and Max‐Min‐based algorithms, and the experimental results demonstrate the advantage of ERPA in improving execution efficiency of PADS applications in clouds. In particular, for communication‐sensitive PADS applications, the advantage of ERPA is more significant.

A Management System for Servicing Multi-Organizations on Community Cloud Model in Secure Cloud Environment

The emergence of cloud computing has been growing rapidly in the last decades especially for workflow scheduling. Organizations with the same requirements and needs go to use the community cloud for saving costs. One of the important challenges of using the community cloud is resource allocation and task scheduling. In this paper, we propose a new Management System for servicing Multi-organizations in a Community cloud (MSMC) in a secure cloud environment. The MSMC employs a virtual machine allocation algorithm to organize the community cloud usage among the organizations, where it allocates the available virtual machines according to the use of each organization in an efficient and fair way to execute the submitted applications. Moreover, the MSMC system proposes a new scheduling algorithm, called Ideal Distribution Algorithm (IDA), to schedule the workflow tasks to the virtual machines of the cloud considering both the deadline and cost constraints. Additionally, an enhanced version of the IDA, called Enhanced IDA (EIDA) is proposed to provide load balancing required by the cloud. The simulation experiments show that the system can improve the system ability under deadline constraints and improve the monetary cost.

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

SummaryWith 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.

Cloud simulators in the implementation and evaluation of virtual machine placement algorithms

SummaryIn recent years, many algorithms have been proposed for the optimized allocation of virtual machines in cloud data centers. Such algorithms are usually implemented and evaluated in a cloud simulator. This paper investigates the impact of the choice of cloud simulator on the implementation of the algorithms and on the evaluation results. In particular, we report our experiences with porting an algorithm and its evaluation framework from one simulator (CloudSim) to another (DISSECT‐CF). Our findings include limitations in the design of the simulators and in existing algorithm implementations. Based on this experience, we propose architectural guidelines for the integration of virtual machine allocation algorithms into cloud simulators.

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.

Towards Efficient Resource Allocation for Heterogeneous Workloads in IaaS Clouds

Infrastructure-as-a-service (IaaS) cloud technology has attracted much attention from users who have demands on large amounts of computing resources. Current IaaS clouds provision resources in terms of virtual machines (VMs) with homogeneous resource configurations where different types of resources in VMs have similar share of the capacity in a physical machine (PM). However, most user jobs demand different amounts for different resources. For instance,high-performance-computing jobs require more CPU cores while big data processing applications require more memory. The existing homogeneous resource allocation mechanisms cause resource starvation where dominant resources are starved while non-dominant resources are wasted. To overcome this issue, we propose a heterogeneous resource allocation approach, called skewness-avoidance multi-resource allocation (SAMR), to allocate resource according to diversified requirements on different types of resources. Our solution includes a VM allocation algorithm to ensure heterogeneous workloads are allocated appropriately to avoid skewed resource utilization in PMs, and a model-based approach to estimate the appropriate number of active PMs to operate SAMR. We show relatively low complexity for our model-based approach for practical operation and accurate estimation. Extensive simulation results show the effectiveness of SAMR and the performance advantages over its counterparts.

Heuristic virtual machine allocation for multi-tier Ambient Assisted Living applications in a cloud data center

Cloud computing provides the essential infrastructure for multi-tier Ambient Assisted Living (AAL) applications that facilitate people's lives. Resource provisioning is a critically important problem for AAL applications in cloud data centers (CDCs). This paper focuses on modeling and analysis of multi-tier AAL applications, and aims to optimize resource provisioning while meeting requests' response time constraint. This paper models a multi-tier AAL application as a hybrid multi-tier queueing model consisting of an M/M/c queueing model and multiple M/M/1 queueing models. Then, virtual machine (VM) allocation is formulated as a constrained optimization problem in a CDC, and is further solved with the proposed heuristic VM allocation algorithm (HVMA). The results demonstrate that the proposed model and algorithm can effectively achieve dynamic resource provisioning while meeting the performance constraint.

A Meta-Heuristic based Virtual Machine Placement and Migration for Optimal Resource Utilization in the Cloud Computing Environment

This paper focuses on the Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate hosts available in a Cloud. It is necessary to develop efficient scheduling algorithm to allocate VMs to physical resources. The proposed algorithm is based on Particle Swarm Optimization (PSO) which evaluates a cloud scheduler with an objective of optimal resource utilization in the data centers. The resource wastage model for optimal resource utilization is discussed. The proposed algorithm utilizes the cloud resources effectively by schedule or co-locates dissimilar application specific virtual machines on the host in an intelligent manner. The proposed algorithm is compared against the existing VM allocation algorithm. The results exhibit the advantages of using the proposed algorithm. VM migration is done to meet out the Service Level Agreements (SLAs) based on the proposed model. It is shown that the proposed algorithm achieved the better resource utilization and minimizes the number of VM migration.

Guaranteeing Heterogeneous Bandwidth Demand in Multitenant Data Center Networks

The ability to provide guaranteed network bandwidth for tenants is essential to the prosperity of cloud computing platforms, as it is a critical step for offering predictable performance to applications. Despite its importance, it is still an open problem for efficient network bandwidth sharing in a multitenant environment, especially when applications have diverse bandwidth requirements. More precisely, it is not only that different tenants have distinct demands, but also that one tenant may want to assign bandwidth differently across her virtual machines (VMs), i.e., the heterogeneous bandwidth requirements. In this paper, we tackle the problem of VM allocation with bandwidth guarantee in multitenant data center networks. We first propose an online VM allocation algorithm that improves on the accuracy of the existing work. Next, we develop a VM allocation algorithm under heterogeneous bandwidth demands. We conduct extensive simulations to demonstrate the efficiency of our method.

Support vector machine approach for virtual machine migration in cloud data center

The social media services are popular with Internet services today, such as Facebook, YouTube, Plurk and Twitter. However, the enormous interactions among human beings also result in highly computational costs. The requested resources and demands of some specific social media services are changing severely, and the virtual machines (VMs) exhaust the computing resource of physical machine (PM). Thus this will lead to VM migration. Many researchers investigate how to stabilize the average utilization of virtual machines and physical machines in cloud data center. In this paper, we formulated the VM migration problem in cloud data center based on mixed integer linear programming (MILP). Then, the VM allocation algorithm was proposed to allocate the VMs among the PMs, which is based on the Support Vector Machine (SVM). According to the training process during a specific time, the minimum numbers of VM migration and maximum resource utilization of PMs were accomplished. As the allocation case and simulation results showed, we achieved the stable and low-cost for social media services in cloud data center.

Energy Efficient Multiresource Allocation of Virtual Machine Based on PSO in Cloud Data Center

Presently, massive energy consumption in cloud data center tends to be an escalating threat to the environment. To reduce energy consumption in cloud data center, an energy efficient virtual machine allocation algorithm is proposed in this paper based on a proposed energy efficient multiresource allocation model and the particle swarm optimization (PSO) method. In this algorithm, the fitness function of PSO is defined as the total Euclidean distance to determine the optimal point between resource utilization and energy consumption. This algorithm can avoid falling into local optima which is common in traditional heuristic algorithms. Compared to traditional heuristic algorithms MBFD and MBFH, our algorithm shows significantly energy savings in cloud data center and also makes the utilization of system resources reasonable at the same time.