Dynamic Placement Of Virtual Machines Research Articles

Multiple objectives dynamic VM placement for application service availability in cloud networks

Ensuring application service availability is a critical aspect of delivering quality cloud computing services. However, placing virtual machines (VMs) on computing servers to provision these services can present significant challenges, particularly in terms of meeting the requirements of application service providers. In this paper, we present a framework that addresses the NP-hard dynamic VM placement problem in order to optimize application availability in cloud computing paradigm. The problem is modeled as an integer nonlinear programming (INLP) optimization with multiple objectives and constraints. The framework comprises three major modules that use optimization methods and algorithms to determine the most effective VM placement strategy in cases of application deployment, failure, and scaling. Our primary goals are to minimize power consumption, resource waste, and server failures while also ensuring that application availability requirements are met. We compare our proposed heuristic VM placement solution with three related algorithms from the literature and find that it outperforms them in several key areas. Our solution is able to admit more applications, reduce power consumption, and increase CPU and RAM utilization of the servers. Moreover, we use a deep learning method that has high accuracy and low error loss to predict application task failures, allowing for proactive protection actions to reduce service outage. Overall, our framework provides a comprehensive solution by optimizing dynamic VM placement. Therefore, the framework can improve the quality of cloud computing services and enhance the experience for users.

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%.

Improving Dynamic Placement of Virtual Machines in Cloud Data Centers Based on Open-Source Development Model Algorithm

Improving Dynamic Placement of Virtual Machines in Cloud Data Centers Based on Open-Source Development Model Algorithm

A Secure and Multiobjective Virtual Machine Placement Framework for Cloud Data Center

To facilitate cost-effective and elastic computing benefits to the cloud users, the energy-efficient and secure allocation of virtual machines (VMs) plays a significant role at the data centre. The inefficient VM Placement (VMP) and sharing of common physical machines among multiple users leads to resource wastage, excessive power consumption, increased inter-communication cost and security breaches. To address the aforementioned challenges, a novel secure and multi-objective virtual machine placement (SM-VMP) framework is proposed with an efficient VM migration. The proposed framework ensures an energy-efficient distribution of physical resources among VMs that emphasizes secure and timely execution of user application by reducing inter-communication delay. The VMP is carried out by applying the proposed Whale Optimization Genetic Algorithm (WOGA), inspired by whale evolutionary optimization and non-dominated sorting based genetic algorithms. The performance evaluation for static and dynamic VMP and comparison with recent state-of-the-arts observed a notable reduction in shared servers, inter-communication cost, power consumption and execution time up to 28.81%, 25.7%, 35.9% and 82.21%, respectively and increased resource utilization up to 30.21%.

Dynamic placement of virtual machines using an improved multi‐objective teaching‐learning based optimization algorithm in cloud

AbstractRecently, cloud computing is growing rapidly and contributing to the realization of other technologies. Cloud data centers use a lot of energy to host applications, which leads to high operating costs and increased carbon dioxide emissions into the environment. Dynamic consolidation of virtual machines (VMs) into the minimum number of physical servers is an efficient approach to managing energy consumption in cloud. Virtual machines placement (VMP) is an important problem in the proper consolidation of VMs and is the most common way to improve resource utilization. In VMP problem, VMs are consolidated to minimize the number of active physical servers. This consolidation is done using migration of VMs. After placement, the VMs execute on selected physical servers and the underloaded physical servers are off to optimize energy consumption. Basically, the purpose of VMP is to allocate a set of VMs to a subset of physical servers, taking into account some objectives such as reducing energy consumption and violating the service level agreement (SLA). In this article, an improved teaching‐learning based optimization (TLBO) algorithm is formulated to solve VMP as a multi‐objective problem, which we call VMP‐TLBO. VMP‐TLBO can perform optimal placement of migrated VMs to physical servers with the purpose of reducing energy consumption and SLA violation. VMP‐TLBO is implemented in MATLAB and the experimental results show that proposed algorithm does not violate SLA and compared to the best equivalence algorithm (ie, PAPSO), it improves energy consumption and SLA violation by 1.8% and 5.6%, respectively. In addition, VMP‐TLBO consolidates migrated VMs into the minimum number of physical servers to minimize the number of overloaded hosts as much as possible.

Improving the Load Balancing and Dynamic Placement of Virtual Machines in Cloud Computing using Particle Swarm Optimization Algorithm

Nowadays, maximizing profits, decreasing operating cost and scheduling tasks are the most important issues of cloud computing with its growing usage. In this regard, one of the challenges in cloud computing is to provide an efficient method to deploy virtual machines on physical machines with the aim of optimizing energy consumption, fair load distribution and task scheduling. The purpose of present study is to provide a method for improving task scheduling through an improved particle swarm optimization algorithm. In the proposed method of present study, selection of a proper objective function has led to balanced workload of virtual machines, decreased time of all tasks as well as maximum utilization of all resources and increased productivity in addition to dynamic placement of virtual machine on physical machine. The results of simulation showed that the proposed method has provided an optimized solution for scheduling tasks, equal allocation of tasks in virtual machines and placement on the appropriate physical machine and less time with an improvement of 0.02 has been spent on the process of outsourcing virtual machines.

Performance of particle swarm optimization bin packing algorithm for dynamic virtual machine placement for the consolidation of cloud server

Infrastructure as a service offered by the cloud computing is one of the most important service. It allows physical machines to get virtualized by creating many instances of virtual machines. Mapping virtual machines on physical machine has become the major challenge in cloud data centres. The dynamic virtual machine placement methods are used to solve this issue with objectives like maximizing the resource utilization, minimizing the energy consumption and maximizing the scalability of data centres. In this paper a virtual machine placement-based bin packaging algorithm is proposed and analysed with four different fitness strategies to obtain the optimal solution. The unimodal (Sphere, Step) and multimodal (Graywang and Rastridge) benchmark functions are used with proposed algorithm for the analysis and obtain the quantitative measurements. The results show that optimizing the mass of particles using the best fitting strategy reduces the energy consumption, resource utilization and improved the scalability of data centres.

A Dynamic Virtual Machine Placement and Migration Scheme for Data Centers

We study the problem of virtual machine (VM) placement and migration in a data center. In the current approaches, VMs are assigned to physical servers using on-demand provisioning. Such an approach is simple but it often results in a poor performance due to resource fragmentation. Additionally, sub-optimal VM placement usually generates unneeded VM migration and unnecessary cross network traffic. The efficiency of a datacenter therefore significantly depends on how VMs are provisioned and where they are placed. A good placement scheme will not only improve the quality of service but also reduce the operation cost of the data center. In this paper, we study the problem of optimal VM placement and migration to minimize resource usage and power consumption in a data center. We formulate the optimization problem as a joint multiple objective function and solve it by leveraging the framework of convex optimization. Due to the intractable nature of the combinatorial optimization, we then propose Multi-level Join VM Placement and Migration (MJPM) algorithms based on the relaxed convex optimization framework to approximate the optimal solution. The theoretical analysis demonstrates the effectiveness of our proposed algorithms that substantially increases data center efficiency. In addition, our extensive simulation results on different practical topologies show significant performance improvement over the existing approaches.

A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers

• Multi-objective algorithm for virtual machine (VM) placement in Cloud data centers. • Approximation of Pareto optimal set of VM placements. • Resource overcommitment, resource wastage and live migration energy tradeoff. • Island-based optimisation heuristic based on genetic NSGA-II algorithm. • Improved evaluation results compared to state-of-the-art heuristics. Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource overcommitment affecting the Quality of Service (QoS) of the running applications. The effective tradeoff between resource wastage and overcommitment is a challenging task in virtualized Clouds and depends on the allocation of virtual machines (VMs) to physical resources. We propose in this paper a multi-objective method for dynamic VM placement, which exploits live migration mechanisms to simultaneously optimize the resource wastage, overcommitment ratio and migration energy. Our optimization algorithm uses a novel evolutionary meta-heuristic based on an island population model to approximate the Pareto optimal set of VM placements with good accuracy and diversity. Simulation results using traces collected from a real Google cluster demonstrate that our method outperforms related approaches by reducing the migration energy by up to 57% with a QoS increase below 6%.

Genetic algorithm-based tabu search for optimal energy-aware allocation of data center resources

Cloud computing delivers practical solutions for long-term image archiving systems. Cloud data centers consume enormous amounts of electrical energy that increases their operational costs. This shows the importance of investing on energy consumption techniques. Dynamic placement of virtual machines to appropriate physical nodes using metaheuristic algorithms is among the methods of reducing energy consumption. In metaheuristic algorithms, there should be a balance between both exploration and exploitation aspects so that they can find better solutions in a search space. Exploration means looking for a solution in a wider area, while exploitation is producing new solutions from existence ones. Artificial bee colony optimization, which is a biological metaheuristic algorithm, is a sign-oriented approach. It has a strong exploration ability, but a relatively weaker exploitation power. On the other hand, tabu search is a popular algorithm that shows better exploitation in comparison with ABC. In this study, cloud computing environments are detailed with an allocation protocol for efficient energy and resource management. The technique of energy-aware allocation splits data centers (DCs) resources among client applications end routes to enhance energy efficacy of DCs and also achieves anticipated quality of service (QoS) for everyone. Heuristic protocols are exercised for optimizing the distribution of resources to upgrade the efficiency of DC. In the current paper, energy-aware resources allotment technique is employed and optimized in clouds via a new approach called Tabu Job Master (JM). Tabu JM claims the benefits of some variables and also rapid convergence speeds. Results are duly achieved for energy consumption—the count of virtual machines (VMs) migration and also makespan. The results shown by Tabu JM are benchmarked by using genetic algorithm (GA), artificial bee colony (ABC), ABC with crossover and technique of mutation, the basic tabu search techniques, and Tabu Job Master.

Unified Provisioning Framework for Dynamic Virtual Machine Placement Optimization in Cloud Data Center

Current techniques for Virtual machine placement in cloud data center is avoiding multiple resources due to its complexity and hardness of the problem. Due to this each Virtual Machine Placement increased overall frequency of server consolidation and migration. In this paper, we have overcome these limitations by providing local search-based unified approximation framework which utilized multiple resources of server and reduced the frequency of server consolidation and migration. The framework is evaluated on Azure cloud data center benchmark data sets and it has surpassed existing methods with improvement by 32% in overall virtual machine placement.

A state based energy optimization framework for dynamic virtual machine placement

The dynamic optimization of virtual machine (VM) placement is to dynamically adjust the placement of VMs on physical machines (PMs) to accomplish some objectives with certain constraints. On one hand, the number of possible combinations of PMs and VMs can be extremely large, which make the optimal solution very hard to get. On the other hand, the optimized solution needs be reachable from the old solution. To solve the problem from both sides, a partitioned optimization framework is proposed. First, four different states of PM, i.e. off, sleeping, ready and running, are introduced with different energy consumption. Running pool, sleeping pool and off pool are set up which partition PMs based on their different states. The classification helps us build the energy consumption model which is needed to evaluate mapping solutions. To decide if a new solution is better than the old one, only three parts of energy need be considered, i.e. energy changes for PMs in different states, energy consumed for changing the states of PMs, and extra energy consumption for migrating VMs. An energy model composed of these three parts is built as the optimization objective. A method is presented to decide the most suitable range to conduct the energy optimization through excluding some PMs in the sleep or off pool if the best solution achieved with those PMs included cannot be better than old solution. A memetic algorithm combining the partheno-genetic algorithm with the multiplayer random evolutionary game theory is proposed to achieve the global optimal solution and generate the executable live migration sequence from old mapping to the target one at the same time. According to our experimental results, our method can decrease the optimization range remarkably. Within the optimized scales, the proposed algorithm performed very well to approach the global optimal solution and guarantee the solution’s feasibility from old solution at the same time.

Energy-aware resource utilization based on particle swarm optimization and artificial bee colony algorithms in cloud computing

Cloud datacenters consume enormous amounts of electrical energy that increases their operational costs. This shows the importance of investing on energy consumption techniques. Dynamic placement of virtual machines to appropriate physical nodes using metaheuristic algorithms is among the methods of reducing energy consumption. In metaheuristic algorithms, there should be a balance between both exploration and exploitation aspects so that they can find better solutions in a search space. Exploration means looking for a solution in a wider area, while exploitation is producing new solutions from existence ones. Artificial bee colony (ABC) optimization, which is a biological metaheuristic algorithm, is a sign-oriented approach. It has a strong exploration ability, but a relatively weaker exploitation power. On the other hand, particle swarm optimization (PSO) is a population-based algorithm that shows better exploitation in comparison with ABC. In this research, a scheduling framework is proposed called HSF.ABC&PSO (hybrid scheduling framework based on ABC&PSO algorithms) that uses the combination of ABC and PSO algorithms. The result of experiments shows that a 4–8% of reduction in energy consumption is obtained in the mode without migration and that 3–12% of reduction is obtained in the mode with migration. In addition, a 5–14% of reduction in the computational energy consumption is obtained in the mode without migration, and 5–28% is obtained in the mode with migration. The total execution time is decreased by up to 15% in mode without migration and is approximately decreased by 27% in mode with migration. Up to 53% throughput is obtained in the mode without migration and 67% obtained with migration. Finally, 9–23% improvement in SLA violation is evaluated as well.

Type-aware virtual machine management for energy efficient cloud data centers

To meet the growing demands on cloud services and applications, a sizeable number of large scale cloud data centers, hosting thousands of heterogeneous servers, is established by cloud service providers. The ever growth in establishing cloud data centers is accompanied by consuming enormous amounts of energy. Thus, proposing efficient management approaches to reduce the energy consumption in cloud data centers becomes a top priority for ensuring the scalability of the cloud computing architecture. In general, the main source of energy overconsumption in today’s data centers is due to the inefficient use of the physical servers’ resources, which results in poor server utilization patterns. So, the key aspect is to utilize the physical resources optimally while serving the cloud user demands. This paper investigates the design and implementation of virtual machine management strategies for energy efficient cloud data centers. Particularly, it considers the processes of virtual machine placement and virtual machine consolidation in enhancing the energy efficiency in cloud infrastructures. While addressing the virtual machine placement problem is important, virtual machine consolidation is even more important to enable continuous reorganization of the already-placed virtual machines on the least number of physical machines. This results in reducing the number of active physical machines by leveraging live virtual machine migration enabled by the virtualization concept. Moreover, since the virtual machine migration operations consume additional energy, the frequency of VM migrations needs to be limited and controlled as well. The paper presents a distributed approach to an energy-efficient dynamic virtual machine consolidation mechanism. This approach determines, based on novel algorithms, which virtual machines to migrate, and when. Then, the placement of the virtual machines selected for migration is achieved based on a generalization of the Knapsack Problem known as the Multiple Choice Knapsack Problem. The placement process suits both static and dynamic virtual machine placement. The results of the performance evaluation demonstrate that the proposed new algorithms are able to enhance the energy efficiency in cloud data centers.

Optimal Dynamic Placement of Virtual Machines in Geographically Distributed Cloud Data Centers

In geo-distributed cloud systems, a key challenge faced by cloud providers is to optimally tune and configure the underlying cloud infrastructure. An important problem in this context, deals with finding an optimal virtual machine (VM) placement, minimizing costs, while at the same time, ensuring good system performance. Moreover, due to the fluctuations of demand and traffic patterns, it is crucial to dynamically adjust the VM placement scheme over time. It should be noted that most of the existing studies, however, dealt with this problem either by ignoring its dynamic aspect or by proposing solutions that are not suitable for a geographically distributed cloud infrastructure. In this paper, exact as well as heuristic solutions based on Integer Linear programming (ILP) formulations are proposed. Our work focuses also on the problem of scheduling the VM migration by finding the best migration sequence of intercommunicating VMs that minimizes the resulting traffic on the backbone network. The proposed algorithms execute within a reasonable time frame to readjust VM placement scheme according to the perceived demand. Our aim is to use VM migration as a tool for dynamically adjusting the VM placement scheme while minimizing the network traffic generated by VM communication and migration. Finally, we demonstrate the effectiveness of our proposed algorithms by performing extensive experiments and simulation.

An Energy Aware Unified Ant Colony System for Dynamic Virtual Machine Placement in Cloud Computing

Energy efficiency is a significant topic in cloud computing. Dynamic consolidation of virtual machines (VMs) with live migration is an important method to reduce energy consumption. However, frequent VM live migration may cause a downtime of service. Therefore, the energy save and VM migration are two conflict objectives. In order to efficiently solve the dynamic VM consolidation, the dynamic VM placement (DVMP) problem is formed as a multiobjective problem in this paper. The goal of DVMP is to find a placement solution that uses the fewest servers to host the VMs, including two typical dynamic conditions of the assignment of new coming VMs and the re-allocation of existing VMs. Therefore, we propose a unified algorithm based on an ant colony system (ACS), termed the unified ACS (UACS), that works on both conditions. The UACS firstly uses sufficient servers to host the VMs and then gradually reduces the number of servers. With each especial number of servers, the UACS tries to find feasible solutions with the fewest VM migrations. Herein, a dynamic pheromone deposition method and a special heuristic information strategy are also designed to reduce the number of VM migrations. Therefore, the feasible solutions under different numbers of servers cover the Pareto front of the multiobjective space. Experiments with large-scale random workloads and real workload traces are conducted to evaluate the performance of the UACS. Compared with traditional heuristic, probabilistic, and other ACS based algorithms, the proposed UACS presents competitive performance in terms of energy consumption, the number of VM migrations, and maintaining quality of services (QoS) requirements.

Dynamic Virtual Machines Placement in a Cloud Environment by Multi-Objective Programming

Dynamic Virtual Machines Placement in a Cloud Environment by Multi-Objective Programming

WAYFINDER: parallel virtual machine reallocation through A* search

Modern virtual machine (VM) management software enables consolidation of VMs for power savings or load-balancing for performance. While existing literature provides various methods for computing a better load-balanced, or consolidated goal state, it fails to adequately suggest the best path from the system’s current state to the desired goal allocation. This paper discusses an approach to efficient path finding in VM placement problems for cloud computing environments of moderate scale with results indicating the solution is reasonable for managing hundreds of VMs. We present an overview of known approaches to dynamic VM placement and discuss their shortcomings with respect to dynamic reallocation. We then describe a novel design and implementation of a heuristic search algorithm to determine optimal sequential migration plans to transition from a given VM-to-host allocation to an arbitrary desired allocation state. We then elaborate nuances of A* application to this domain along with our simulation-based validation approach. Finally, this work demonstrates a novel and highly effective technique for exploiting migration parallelism in order to rapidly achieving VM reallocation convergence suitable for continual workload rebalancing in cloud data centers.

Identifying Overloaded Servers and Managing Dynamic Placement of Virtual machines in Cloud

Cloud computing is becoming one of the most popular commercial infrastructure due to its little maintenance expense and on demand resource utilization. Cloud computing possesses many kinds of technical challenges such as fault tolerance, reliability, availability, integrity etc. due to its complex and distributed nature. But the main problem related to all those is overload incurred by Virtual Machines (VM). So, load balancing is one of the most significant issues that can help to gain rapid performance of cloud infrastructure. This research proposes algorithms for detecting failed servers due to overloaded VMs. The failure detection algorithm checks server status after a predefined time interval. This algorithm gives proactive technique to deal with overloaded VMs. When any failure in the server is found, the resource balancing algorithm migrates its VMs to an adequate healthy Physical Machine (PM). To distribute workload evenly, the resource utilization skew is measured. This VM to PM mapping is done in a way that every PM will do almost equal amount of work. General Terms Cloud computing

A solution of dynamic VMs placement problem for energy consumption optimization based on evolutionary game theory

Power saving of data centers has become an urgent problem in recent years. For a virtualized data center, optimizing the placement of virtual machines (VMs) dynamically is one of the most effective methods for power savings. Based on a deep study on VMs placement, a solution is proposed and described in this paper to solve the problem of dynamic placement of VMs toward optimization of their energy consumptions. A computational model of energy consumption is proposed and built. A novel algorithm based on evolutionary game theory is also presented, which successfully addresses the challenges faced by dynamic placement of VMs. It is proved that the proposed algorithm can reach the optimal solutions theoretically. Experimental results also demonstrate that, by adjusting VMs placement dynamically, the energy consumption can be reduced correspondingly. In comparison with the existing state of the arts, our proposed method outperforms other five algorithms tested and achieves savings of 30–40% on energy consumption.