Efficient Virtual Machine Allocation Research Articles

VMP-A3C: Virtual machines placement in cloud computing based on asynchronous advantage actor-critic algorithm

Virtualization technology represented through Virtual Machines (VMs) is recognized as a key infrastructure in cloud computing. This technology is developing rapidly and cloud data centers face challenges such as Virtual Machine Placement (VMP) for energy efficiency. VMP is defined as the efficient allocation of VMs to Host Machines (HMs) to achieve various objectives such as reducing energy consumption, load balancing and avoid Service Level Agreement Violations (SLAV). In this paper, VMP is addressed using a Deep Reinforcement Learning (DRL) based strategy to determine the best mapping between VMs and HMs. We present VMP-A3C, an effective strategy to solve VMP using Asynchronous Advantage Actor-Critic (A3C) algorithm as a new DRL approach. VMP-A3C aims at load balancing in HMs without SLAV, where energy consumption is reduced as much as possible. VMP-A3C learns to dynamically consolidate VMs using migration techniques to a minimum number of HMs. We believe that there is scope for improvements in shutting down little-workload HMs through VMs migration. The effectiveness of the proposed algorithm has been evaluated from various aspects such as the deployment rate, energy consumption, SLAV, the number of shutdown HMs and the number of migrated VMs. The main difference in terms of energy consumption and the number of required HMs between VMP-A3C and the best existing state-of-the-art method is 2.54% and 7.14%, respectively.

Multi-Objective Binary Whale Optimization-Based Virtual Machine Allocation in Cloud Environments

With the rising demands for the services provided by cloud computing, virtual machine allocation (VMA) has become a tedious task due to the dynamic nature of the cloud. Millions of virtual machines (VMs) are allocated and de-allocated at every instant, so an efficient VMA has been a significant concern to enhance resource utilization and depreciate its wastage. Encouraged by the prodigious performance of the nature-inspired algorithm, the binary whale optimization approach has been eventuated to get to grips with the VMA issue with the focus on minimizing the resource waste and volume of servers working actively. The deliberate approach's accomplishment is assessed against the literature's well-known algorithms for VMA issues. The comparison results showed that the least resource wastage fitness of 15.68, minimum active servers of 216, and effective CPU and memory utilization of 88.31% and 88.79%, respectively, have been achieved.

A Bio-inspired VM Migration using Re-initialization and Decomposition Based-Whale Optimization

Virtual Machine (VM) migration has become quite prevalent in cloud computing. Numerous VM migration-oriented techniques are required for efficient VM allocation but continue to struggle due to incorrect energy usage in the cloud model. Bio-inspired R&D-WOA (Re-initialization and Decomposition-Whale Optimization Algorithm) is proposed by integrating WOA with NSGA-II algorithm to discover Pareto-optimal solutions. The proposed algorithm optimizes the allocation of the task to VM which diminishes the number of VM migrations, thereby lowering the migration cost and energy consumption. The efficiency of the Bio-inspired R&D-WOA is 67%, 7%, 1%, and 1.98% improved than the performance of existing WOA with regard to load, migration cost, energy utilized, and resource abundance respectively. At a similar level of load, it provided 24.80% efficiency in energy, 31.77% reduction in migration cost with maximal resource availability of 0.9917 compared to the ChicWhale algorithm.

Optimal Allocation of IaaS Cloud Resources through Enhanced Moth Flame Optimization (EMFO) Algorithm

A new generation of computing resources is available to customers via IaaS, PaaS, and SaaS administrations, making cloud computing the most significant innovation in recent history for the general public. A virtual machine (VM) is configured, started, and maintained across numerous physical hosts using IaaS. In many cases, cloud providers (CPs) charge utility customers who have registered their premises with the utility registration authorities. Given the opposing aims of increasing customer demand fulfillment while decreasing costs and optimizing asset efficiency, efficient VM allocation is generally considered as one of the most difficult tasks for CPs to overcome. This paper proposes the Enhanced Moth Flame Optimization (EMFO) algorithm to provide a unique strategy for assigning virtual machines to suit customer requirements. The recommended approach is applied on Amazon’s EC2 after three distinct experiments are assumed. The utility of the proposed method is further shown by the use of well-known optimization techniques for effective VM allocation. The app was created using a Java-based programming language and then run on the Netbeans IDE 12.4 platform.

Look-ahead energy efficient VM allocation approach for data centers

Energy efficiency is an important issue for reducing environmental dissipation. Energy efficient resource provisioning in cloud environments is a challenging problem because of its dynamic nature and varied application workload characteristics. In the literature, live migration of virtual machines (VMs) among servers is commonly proposed to reduce energy consumption and to optimize resource usage, although it comes with essential drawbacks, such as migration cost and performance degradation. Energy efficient provisioning is addressed at the data center level in this research. A novel efficient resource management algorithm for virtualized data centers that optimizes the number of servers to meet the requirements of dynamic workloads without migration is proposed in this paper. The proposed approach, named Look-ahead Energy Efficient VM Allocation (LAA), contains a Holt Winters-based prediction module. Energy efficiency and performance are inversely proportional. The energy-performance trade-off relies on periodic comparisons of the predicted and active numbers of servers. To evaluate the proposed algorithm, experiments are conducted with real-world workload traces from Google Cluster. LAA is compared with the best approach provided by CloudSim based on VM migration called Local Regression-Minimum Migration Time (LR-MMT). The experimental results show that the proposed algorithm leads to a consumption reduction of up to 45% to complete one workload compared with the LR-MMT.

An efficient virtual machine allocation using single stage weapon target assignment model in cloud software‐defined network environment

SummaryWith the rapid growth of the Internet, technological advancements, and the increasing number of cloud users, a paradigm transformation towards separate data and control planes with a centralized control point is taking place. As a result of this separation, software‐defined networks (SDNs) have distinct software controllers for routers and the capacity to manage network behavior. Virtual machine placement (VMP) and virtual machine migration (VMM) are two examples of SDN usage in data centers. The issue of VMP in cloud environments is a significant challenge that has been widely researched but not fully resolved. In this research, we used the single stage weapon target assignment (SSWTA) to model the virtual machine allocation (VMA) for jobs in order to maximize the resource utilization of the physical machines (PMs) by reducing idle time. Furthermore, this research proposes three algorithms: job assignment algorithms, PM allocation (PMA) using the least recently used (LRU) policy, and optimal VMA. The proposed VMA's performance is compared to other related VMA models such as first fit decreasing, and it is determined to be efficient in terms of resource utilization.

An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures

Cloud computing systems are rapidly evolving toward multicloud architectures supported on heterogeneous hardware. Cloud service providers are widely offering different types of storage infrastructures and multi-NUMA architecture servers. Existing cloud resource allocation solutions do not comprehensively consider this heterogeneous infrastructure. In this study, we present a novel approach comprised of a hierarchical framework based on genetic programming to solve problems related to data placement and virtual machine allocation for analytics applications running on heterogeneous hardware with a variety of storage types and nonuniform memory access. Our approach optimizes data placement using the Hadoop File System on heterogeneous storage devices on multicloud systems. It guarantees the efficient allocation of virtual machines on physical machines with multiple NUMA (nonuniform memory access) domains by minimizing contention between workloads. We prove that our solutions for data placement and virtual machine allocation outperform other state-of-the-art approaches.

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.

CloudBench: an integrated evaluation of VM placement algorithms in clouds

A complex and important task in the cloud resource management is the efficient allocation of virtual machines (VMs), or containers, in physical machines (PMs). The evaluation of VM placement techniques in real-world clouds can be tedious, complex and time-consuming. This situation has motivated an increasing use of cloud simulators that facilitate this type of evaluations. However, most of the reported VM placement techniques based on simulations have been evaluated taking into account one specific cloud resource (e.g., CPU), whereas values often unrealistic are assumed for other resources (e.g., RAM, awaiting times, application workloads, etc.). This situation generates uncertainty, discouraging their implementations in real-world clouds. This paper introduces CloudBench, a methodology to facilitate the evaluation and deployment of VM placement strategies in private clouds. CloudBench considers the integration of a cloud simulator with a real-world private cloud. Two main tools were developed to support this methodology, a specialized multi-resource cloud simulator (CloudBalanSim), which is in charge of evaluating VM placement techniques, and a distributed resource manager (Balancer), which deploys and tests in a real-world private cloud the best VM placement configurations that satisfied user requirements defined in the simulator. Both tools generate feedback information, from the evaluation scenarios and their obtained results, which is used as a learning asset to carry out intelligent and faster evaluations. The experiments implemented with the CloudBench methodology showed encouraging results as a new strategy to evaluate and deploy VM placement algorithms in the cloud.

Multicriteria decision‐making techniques for avoiding similar task scheduling conflict in cloud computing

SummaryAn efficient task scheduling approach shows promising way to achieve better resource utilization in cloud computing. Various task scheduling approaches with optimization and decision‐making techniques have been discussed up to now. These approaches ignored scheduling conflict among the similar tasks. The conflict often leads to miss the deadlines of the tasks. The work studies the implementation of the MCDM (multicriteria decision‐making) techniques in backfilling algorithm to execute deadline‐based tasks in cloud computing. In general, the tasks are selected as backfill tasks, whose role is to provide ideal resources to other tasks in the backfilling approach. The selection of the backfill task is challenging one, when there are similar tasks. It creates conflict in the scheduling. In cloud computing, the deadline‐based tasks have multiple parameters such as arrival time, number of VMs (virtual machines), start time, duration of execution, and deadline. In this work, we present the deadline‐based task scheduling algorithm as an MCDM problem and discuss the MCDM techniques: AHP (Analytical Hierarchy Process), VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to avoid similar task scheduling conflicts. We simulate the backfilling algorithm along with three MCDM mechanisms to avoid scheduling conflicts among the similar tasks. The synthetic workloads are considered to study the performance of the proposed scheduling algorithm. The mechanism suggests an efficient VM allocation and its utilization for deadline‐based tasks in the cloud environment.

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.

Energy-aware virtual machine allocation for cloud with resource reservation

To reduce the price of pay-as-you-go style cloud applications, an increasing number of cloud service providers offer resource reservation-based services that allow tenants to customize their virtual machines (VMs) with specific time windows and physical resources. However, due to the lack of efficient management of reserved services, the energy efficiency of host physical machines cannot be guaranteed. In today’s highly competitive cloud computing market, such low energy efficiency will significantly reduce the profit margin of cloud service providers. Therefore, how to explore energy efficient VM allocation solutions for reserved services to achieve maximum profit is becoming a key issue for the operation and maintenance of cloud computing. To address this problem, this paper proposes a novel and effective evolutionary approach for VM allocation that can maximize the energy efficiency of a cloud data center while incorporating more reserved VMs. Aiming at accurate energy consumption estimation, our approach needs to simulate all the VM allocation updates, which is time-consuming using traditional cloud simulators. To overcome this, we have designed a simplified simulation engine for CloudSim that can accelerate the process of our evolutionary approach. Comprehensive experimental results obtained from both simulation on CloudSim and real cloud environments show that our approach not only can quickly achieve an optimized allocation solution for a batch of reserved VMs, but also can consolidate more VMs with fewer physical machines to achieve better energy efficiency than existing methods. To be specific, the overall profit improvement and energy savings achieved by our approach can be up to 24% and 41% as compared to state-of-the-art methods, respectively. Moreover, our approach could enable the cloud data center to serve more tenant requests.

Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

Modern cloud data centers (DCs) need to tackle efficiently the increasing demand for computing resources and address the energy efficiency challenge. Therefore, it is essential to develop resource provisioning policies that are aware of virtual machine (VM) characteristics, such as CPU utilization and data communication, and applicable in dynamic scenarios. Traditional approaches fall short in terms of flexibility and applicability for large-scale DC scenarios. In this paper, we propose a heuristic- and a machine learning (ML)-based VM allocation method and compare them in terms of energy, quality of service (QoS), network traffic, migrations, and scalability for various DC scenarios. Then, we present a novel hyper-heuristic algorithm that exploits the benefits of both methods by dynamically finding the best algorithm, according to a user-defined metric. For optimality assessment, we formulate an integer linear programming (ILP)-based VM allocation method to minimize energy consumption and data communication, which obtains optimal results, but is impractical at runtime. Our results demonstrate that the ML approach provides up to 24% server-to-server network traffic improvement and reduces execution time by up to $480{\times }$ compared to conventional approaches, for large-scale scenarios. On the contrary, the heuristic outperforms the ML method in terms of energy and network traffic for reduced scenarios. We also show that the heuristic and ML approaches have up to 6% energy consumption overhead compared to ILP-based optimal solution. Our hyper-heuristic integrates the strengths of both the heuristic and the ML methods by selecting the best one during runtime.

Towards Bandwidth Guarantee for Virtual Clusters Under Demand Uncertainty in Multi-Tenant Clouds

In the cloud, multiple tenants share the resource of datacenters and their applications compete with each other for scarce network bandwidth. Current studies have shown that the lack of bandwidth guarantee causes unpredictable network performance, leading to poor application performance. To address this issue, several virtual network abstractions have been proposed which allow the tenants to reserve virtual clusters with specified bandwidth between the Virtual Machines (VMs) in the datacenters. However, all these existing proposals require the tenants to deterministically characterize the bandwidth demands in the abstractions, which can be difficult and result in inefficient bandwidth reservation due to the demand uncertainty. In this paper, we explore a virtual cluster abstraction with stochastic bandwidth characterization to address the bandwidth demand uncertainty. We propose Stochastic Virtual Cluster (SVC), which models the bandwidth demand between VMs in a probabilistic way. Based on SVC, we develop a stochastic framework for virtual cluster allocation, in which the admitted virtual cluster's bandwidth demands are satisfied with a high probability. Efficient VM allocation algorithms are proposed to implement the framework while reducing the possibility of link congestion through minimizing the maximum bandwidth occupancy of a virtual cluster on physical links. Using simulations, we show that SVC achieves the trade-off between the job concurrency and the average job running time, and demonstrate its effectiveness for accommodating cloud application workloads with highly volatile bandwidth demands and its improvement to work-conserving bandwidth enforcement.

An Approach Towards Amelioration of an Efficient VM Allocation Policy in Cloud Computing Domain

Cloud computing is on the horizon of the domain of information technology over the recent few years, giving different remotely accessible services to the cloud users. The quality-of-service (QoS) maintaining of a cloud service provider is the most dominating research issue today. The QoS embraces with different issues like virtual machine (VM) allocation, optimization of response time and throughput, utilizing processing capability, load balancing etc. VM allocation policy deals with the allocation of VMs to the hosts in different datacenters. This paper highlights a new VM allocation policy that distributes the load of VMs among hosts which improves the utilization of hosts’ processing capability as well as makespan and throughput of cloud system. The experimental results are obtained by utilizing trace based simulation in CloudSim 3.0.3 and compared with existing VM allocation policies.

Energy and QoS aware resource allocation for heterogeneous sustainable cloud datacenters

As the demand on Internet services such as cloud and mobile cloud services drastically increases recently, the energy consumption consumed by the cloud datacenters has become a burning topic. The deployment of renewable energy generators such as PhotoVoltaic (PV) and wind farms is an attractive candidate to reduce the carbon footprint and, achieve the sustainable cloud datacenters. However, current studies have focused on geographical load balancing of Virtual Machine (VM) requests to reduce the cost of brown energy usage, while most of them have ignored the heterogeneity of power consumption of each cloud datacenter and the incurred performance degradation by VM co-location. In this paper, we propose Evolutionary Energy Efficient Virtual Machine Allocation (EEE-VMA), a Genetic Algorithm (GA) based metaheuristic which supports a power heterogeneity aware VM request allocation of multiple sustainable cloud datacenters. This approach provides a novel metric called powerMark which diagnoses the power efficiency of each cloud datacenter in order to reduce the energy consumption of cloud datacenters more efficiently. Furthermore, performance degradation caused by VM co-location and bandwidth cost between cloud service users and cloud datacenters are considered to avoid the deterioration of Quality-of-Service (QoS) required by cloud service users by using our proposed cost model. Extensive experiments including real-world traces based simulation and the implementation of cloud testbed with a power measuring device are conducted to demonstrate the energy efficiency and performance assurance of the proposed EEE-VMA approach compared to the existing VM request allocation strategies.

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.

Energy Efficient Virtual Machine Allocation in Cloud

Cloud data centers hosting cloud applications consume huge amounts of electrical energy, contributing to high operational costs and carbon footprints to the environment. When virtualized resources of cloud data centers are allocated, the energy consumption is a problem to be considered necessarily. In this paper, three energy efficient virtual machine allocation algorithms are carried on experimental analysis, including MAX, DVFS and dynamic deployment algorithm (DDA). We have evaluated these algorithms by conducting a performance evaluation study using the CloudSim toolkit. The experimental results show that the dynamic virtual machine allocation algorithm like as DDA is more energy-efficient and the virtual machine migration is an efficient saving energy method.

Performance bounded energy efficient virtual machine allocation in the global cloud

Reducing energy consumption is a critical step in lowering data center operating costs for various institutions. As such, with the growing popularity of cloud computing, it is necessary to examine various methods by which energy consumption in cloud environments can be reduced. We analyze the effects of global virtual machine allocation on energy consumption, using a variety of real-world policies and a realistic testing scenario. We found that by using an allocation policy designed to minimize energy, total energy consumption could be reduced by up to 14%, and total monetary energy costs could be reduced by up to 26%. Further, we have begun performance qualification of our energy cost driven allocation policies through network capability tests. Our results indicate that performance and IaaS provider implementation costs have a significant influence on selection of optimal virtual machine allocation policies.

Efficient Virtual Machine Placement for On-Demand Access to Infrastructure Resourcesin Cloud Computing

computing is the utility computing that provides virtualized resources, applications, and services using distributed network and Internet. Cloud computing service offers the ability to scale up and scale down your computing requirements and most importantly to reduce the cost of deployment. Many organizations are migrating to cloud computing services to lower the risk and for better business continuity.In case of on-demand access user requests infrastructure services for immediate access and for a very short interval of time, they have to pay certain charge depending upon that duration.In cloud computing, infrastructure requests are served by the allocation of virtual machines to those requests; these virtual machines should be placed on the underlying hardware infrastructure called datacenter. In this paper we have proposed a model for the efficient allocation of virtual machines on the cloud infrastructure to reduce the allocation time and to optimize the resource utilization. The proposed model is simulated and its performance is compared with two other existing models.