Virtual Machine Placement Algorithm Research Articles

An integrated optimization method to task scheduling and VM placement for green datacenters

In the realm of cloud computing, effective resource allocation can significantly enhance the energy efficiency of datacenters. Task scheduling and Virtual Machine Placement (VMP) are two pivotal aspects of resource allocation. However, in current research, they are often treated separately, overlooking the potential for integrated optimization. In this paper, we propose an integrated solution for task scheduling and VMP in energy-efficient datacenters, based on queueing theory and Deep Reinforcement Learning (DRL) methods. This novel and comprehensive approach provides an alternative perspective for resource scheduling strategies in datacenters. We construct a queueing theory model for task scheduling, aiming to minimize the number of VMs that need to be instantiated, while ensuring that Service Level Agreement (SLA) violation remains at a low level. Furthermore, we design a VMP algorithm based on DRL for real-time selection of Physical Hosts (PHs) for deploying VMs. Finally, we conduct a simulation evaluation using a small-scale datacenter. The experimental results demonstrate that our method consistently ensures a lower rate of SLA violation. Compared to existing algorithms, the DRL-based VMP algorithm enables a more balanced utilization of the various resources in the PHs and reduces the total power consumption of the datacenter by more than 10% on average.

A hybrid energy-aware algorithm for virtual machine placement in cloud computing

A hybrid energy-aware algorithm for virtual machine placement in cloud computing

A distributed auction-based algorithm for virtual machine placement in multiplayer cloud gaming infrastructures

A distributed auction-based algorithm for virtual machine placement in multiplayer cloud gaming infrastructures

A distributed auction-based algorithm for virtual machine placement in multiplayer cloud gaming infrastructures

A distributed auction-based algorithm for virtual machine placement in multiplayer cloud gaming infrastructures

A Utilization Based Genetic Algorithm for virtual machine placement in cloud systems

Due to the increasing demand for cloud computing and related services, cloud providers need to come up with methods and mechanisms that increase the performance, availability and reliability of data centers and cloud systems. Server virtualization is a key component to achieve this, which enables sharing of resources of a single physical machine among multiple virtual machines in a totally isolated manner. Optimizing virtualization has a very significant effect on the overall performance of a cloud computing system. This requires efficient and effective placement of virtual machines into physical machines. Since this is an optimization problem that involves multiple constraints and objectives, we propose a method based on genetic algorithms to place virtual machines into physical servers of a data center. By considering the utilization of machines and node distances, our method, called Utilization Based Genetic Algorithm (UBGA), aims at reducing resource waste, network load, and energy consumption at the same time. We compared our method against several other placement methods in terms of utilization achieved, networking bandwidth consumed, and energy costs incurred, using an open-source, publicly available CloudSim simulator. The results show that our method provides better performance compared to other placement approaches.

A Novel Virtual Machine Placement Algorithm Using an Energy-Aware Meta-Heuristics Approach

The paramount concern associated with the Virtual Machine Placement (VMP) relates to the mapping of Virtual Machine (VMs) to the Physical Machine’s (PMs). This mapping goal is to utilize the PMs to their maximum potential. In order to attain the full proficiency from the mapping process it is required not to impede already active instances of Virtual machine. Incompetence in mapping process substantially results in resource wastage and increase in energy consumption and consequentially increasing the overall functional cost at the data canter. A meta-heuristic based algorithm for virtual machine placement is recommended to redress the aforementioned issues. A cr-Cuckoo algorithm is proposed which integrates the concept of correlation and Cuckoo Search. The work given here is contrasted with varied familiar algorithm of the domain. The acquired results exhibit a distinguished reduction in the consumption of power and count of migrations by VMs and violation in SLA.

An Energy Efficient Particle Swarm Optimization based VM Allocation for Cloud Data Centre: EEVMPSO

Virtual Machine (VM) allocation are the crucial problems because cloud computing enables the rapid growth of data centres and compute centres. Power consumption and network expenses have increased as cloud computing becomes more and more prevalent. System instability may result from repeated requests for computing resources. One of the most important and difficulties facing virtualization technology is finding the best way to stack virtual machines on top of physical machines in cloud data centres. The host must move virtual machines from overloaded to underloaded hosts as part of load balancing, which has an impact on energy consumption. The proposed energy efficient particle swarm optimization algorithm (EEVMPSO) for Virtual Machine allocation to maximize the load balancing. System resources including CPU, storage, and memory are optimized using EEVMPSO. This research article suggests energy-aware virtual machine migration using the Particle Swarm Optimization Algorithm for dynamic VMs placement, energy efficient cloud data centres as a solution to this issue. The experimental result shown in the proposed method, consumption energy in comparison to the PAPSO, KHA, EALBPSO, and RACC-MDT algorithm by 10.86%, 18.22%, 25.8%, and 31.34% respectively, it demonstrated the improvements in the energy service level agreements violation 5.77%, 15.3%, 26.19%, and 30.4%, as well as the average CPU utilization 2.2%, 24%, 22.6%, and 14.6%.

Investigating, quantifying and controlling the co-location attack’s conditional value at risk of VM placement strategies

Co-location attacks jeopardize data privacy and performance of virtual machines (VMs) in cloud computing, which can result in unexpected monetary losses that are not supported or covered by the investments made by users and providers. The uncertainty about the VM type (attacker, target or neutral) exacerbates the difficulty of the problem. However, existing methods, particularly VM placement strategies, fail to capture the risk of significant losses. The measures of risk have a crucial role in coping with the losses that might be incurred by the involved parties in finance or the insurance industry. In this paper, we introduce the notion of risk to the co-location resistant VM placement strategies. We develop a novel framework that assesses the co-location attack risk of an assignment returned by a specific VM placement strategy. A thorough analysis of multiple existing VM placement algorithms highlights the significance of incorporating this measure as it reveals a discernible correlation pattern with respect to co-location attack coverage and resource consumption. Based on these findings, we propose a new Approximate Co-Location Resistant Placement Strategy with Risk Constraints (ACLRPS-RC) to protect the involved parties from overwhelming security losses. The simulations on CloudSim using real workloads show the ability of the new strategy to meet a given level of risk while maintaining an adequate number of used servers.

Progressive-fidelity computation of the genetic algorithm for energy-efficient virtual machine placement in cloud data centers

Energy efficiency is a critical issue in the management and operation of data centers, which form the backbone of cloud computing. Virtual machine placement has a significant impact on the energy efficiency of virtualized data centers. Among various methods to solve the virtual-machine placement problem, the genetic algorithm has been well accepted for its quality of solution. However, it is computationally demanding largely due to the complex form of fitness function, limiting its applications in data centers. To enhance the computational efficiency of the genetic algorithm while maintaining its quality of solution, a progressive-fidelity approach is developed in this paper for genetic-algorithm computation. It starts with a low-fidelity genetic algorithm with a simple fitness function. Then, for solution refinement, it switches to a medium-fidelity genetic algorithm with a more complicated fitness function. Finally, it progresses to the fine tuning of solution through a high-fidelity genetic algorithm with the energy consumption of data centers as fitness function. Heuristics are presented for the adaptive switching of genetic-algorithm computation from low fidelity to medium fidelity and finally to high fidelity. Experiments show that compared with the standard genetic algorithm of high fidelity, our progressive-fidelity approach of genetic algorithm computation is 50% faster for large-scale data centers while maintaining similar quality of solution in terms of the energy consumption of data centers.

An approximation algorithm for virtual machine placement in cloud data centers

An approximation algorithm for virtual machine placement in cloud data centers

A meta reinforcement learning-based virtual machine placement algorithm in mobile edge computing

A meta reinforcement learning-based virtual machine placement algorithm in mobile edge computing

An energy-efficient load balance strategy based on virtual machine consolidation in cloud environment

In the cloud computing environment, unbalanced utilization of multi-dimensional resources in physical servers generates resource fragmentation, leading to inefficient resource utilization and energy wastage in data centers. The inefficient utilization of resources makes high energy consumption and low quality of service (QoS) in resource management an urgent problem to be solved. We propose a load balancing strategy based on virtual machine consolidation (LBVMC), which aims to reduce the energy consumption and service level agreement (SLA) violation of data centers by balancing the multi-dimensional resource utilization in physical machines (PMs). First, we present a load state classification algorithm for PMs with load abnormality considering current and future loads to reduce unnecessary virtual machine (VM) migrations caused by occasional load fluctuations. Then, we propose a resource-weight based selection model for migratable VMs, which selects appropriate VMs to be migrated based on multi-dimensional resource utilization, and reduces resource fragmentation caused by load imbalance. Finally, we design a VM placement algorithm based on resource fitness and load correlation to deploy VMs on the optimal destination PMs to assure load balancing of the destination PMs after VM placement. We perform simulated experiments under homogeneous, heterogeneous, and bottleneck resource environments. Experimental results show that LBVMC is superior to other strategies in reducing energy consumption and SLA violations and achieves better overall performance.

Energy-efficient virtual machine placement algorithm based on power usage

Energy-efficient virtual machine placement algorithm based on power usage

An improved multi-objective eagle algorithm for virtual machine placement in cloud environment

An improved multi-objective eagle algorithm for virtual machine placement in cloud environment

Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers

Energy efficiency is a critical issue in the management and operation of cloud data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy-efficiency improvement for virtualized data centers. Among various methods to solve the VM-placement problem, the genetic algorithm (GA) has been well accepted for the quality of its solution. However, GA is also computationally demanding, particularly in the computation of its fitness function. This limits its application in large-scale systems or specific scenarios where a fast VM-placement solution of good quality is required. Our analysis in this paper reveals that the execution time of the standard GA is mostly consumed in the computation of its fitness function. Therefore, this paper designs a data structure extended from a previous study to reduce the complexity of the fitness computation from quadratic to linear one with respect to the input size of the VM-placement problem. Incorporating with this data structure, an alternative fitness function is proposed to reduce the number of instructions significantly, further improving the execution-time performance of GA. Experimental studies show that our approach achieves 11 times acceleration of GA computation for energy-efficient VM placement in large-scale data centers with about 1500 physical machines in size.

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

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

Hybrid meta‐heuristic algorithm for optimal virtual machine placement and migration in cloud computing

SummaryData sharing in cloud computing happens with multiple participants to freely distribute the group data, which focuses on advancing the effectiveness of work in cooperative backgrounds and has attained widespread benefits. The main intent of this article is to accomplish a virtual machines (VMs) placement and migration model using a hybrid meta‐heuristic concept. A new meta‐heuristic algorithm named DJ‐HA is developed for optimal VM placement and migration to reduce the count of active servers, and minimization of makespan, and energy consumption with a faster convergence rate in a cloud background. Then, the VM migration is done based on the multi‐objective function concerning energy consumption and makespan using the same hybrid DJ‐HA. From the result analysis, the energy consumption of the DJ‐HA is correspondingly secured at 4.3%, 3.5%, 31%, and 33% more advanced than PSO, GWO, DHOA, and JA, at the 100th iteration for Experiment 1. Accordingly, the cost function of the suggested DJ‐HA is secured at 88.8%, 89.4%, 33.3%, and 50% increased than PSO, GWO, DHOA, and JA at the 100th iteration for Experiment 4. Hence, it is proved that the suggested VM migration using DJ‐HA is enriched than the other conventional algorithms.

An Energy-Efficient Strategy and Secure VM Placement Algorithm in Cloud Computing

One of the important and challenging tasks in cloud computing is to obtain the usefulness of cloud by implementing several specifications for our needs, to meet the present growing demands, and to minimize energy consumption as much as possible and ensure proper utilization of computing resources. An excellent mapping scheme has been derived which maps virtual machines (VMs) to physical machines (PMs), which is also known as virtual machine (VM) placement, and this needs to be implemented. The tremendous diversity of computing resources, tasks, and virtualization processes in the cloud causes the consolidation method to be more complex, tedious, and problematic. An algorithm for reducing energy use and resource allocation is proposed for implementation in this article. This algorithm was developed with the help of a Cloud System Model, which enables mapping between VMs and PMs and among tasks of VMs. The methodology used in this algorithm also supports lowering the number of PMs that are in an active state and optimizes the total time taken to process a set of tasks (also known as makespan time). Using the CloudSim Simulator tool, we evaluated and assessed the energy consumption and makespan time. The results are compiled and then compared graphically with respect to other existing energy-efficient VM placement algorithms.

Confidence interval‐based overload avoidance algorithm for virtual machine placement

AbstractVirtualization plays an essential role in decreasing energy consumption and optimizing resource utilization by enabling the creation of virtual machines (VM) and their consolidation through live migration. Excessive migrations and a lack of required VMs are two critical factors in QoS degradation. The current consolidation approaches impose an intensive time complexity and cannot be used in large data centers with hundreds of hosts. This article proposes a framework for dynamic consolidation divided into a QoS‐aware algorithm for overload avoidance and a power‐aware algorithm for VM placement. To compute a safe zone criterion for any VM, relations were suggested by applying an interval estimate with a confidence level. By employing this criterion, the offered algorithm could guarantee the quality of service (QoS), particularly for specific VMs, while avoiding overhead. The VM placement algorithm is developed based on the maximum utilization of active hosts. It provides the capability to control the number of active hosts for the data center manager. The simulation results with real workloads revealed that the proposed framework could decline the amount of service level agreement violations by 78% and the number of migrations by 74%, and energy consumption by up to 13% in comparison with the best results of the benchmark algorithms. Hence, the application of this framework upgrades the QoS of data centers and declines their energy costs.

An energy and carbon-aware algorithm for renewable energy usage maximization in distributed cloud data centers

The vigorous development and the increasing popularity of cloud computing highlight the necessity of reducing data center energy consumption and the environmental impact of carbon dioxide emissions. For geographically distributed data centers, cloud servers are connected to the conventional power grid and in addition they are supported by an attached renewable energy source. Since the carbon footprint rate of energy consumption has dynamic differences in space, reducing energy consumption does not mean decrease carbon emission, which indicates that energy consumption and carbon footprint need to be synergistically optimized. In this paper, an energy and carbon-aware algorithm for virtual machine placement is proposed. The goal is to obtain a virtual machine allocation scheme that aims to achieve the trade-off between energy consumption and carbon emissions by improving renewable energy utilization. The experimental results show that the proposed approach is more energy-efficient and greener, which can also maximize the renewable energy utilization with 73.11% while ensuring the SLA violation with 0.2% in comparison to the baseline algorithms.