A Strategy Game System for QoS-Efficient Dynamic Virtual Machine Consolidation in Data Centers

Virtual Machine (VM) placement is considered as one of the crucial problems in Cloud Computing environments. From the perspective of Cloud Service Providers (CSPs), finding the optimal VM placement strategy is often related to optimal resource utilization, revenue maximization, and energy efficiency. However, to ensure the continuity of customer services, CSPs should also consider the reliability of deployed applications when placing VMs on their infrastructures. Existing research in this area either do not focus on the Cloud reliability evaluation aspect or do not account for the trade-off between reliability and performance in the VM placement process. In this paper, we propose a multi-objective placement model for interdependent VMs in the Cloud that considers both reliability and workload. Reliability in our model is quantitatively evaluated through a set of metrics that we propose. The model involves an Integer Linear Programming problem that aims at maximizing the reliability of the Cloud while minimizing network delay. A multi-objective genetic algorithm is then used to solve the problem heuristically. The proposed model introduces a level of flexibility and its parameters could be adjusted depending on the requirements of the infrastructure and services. The results show that our model achieves high Cloud reliability and allows to effectively control the trade-off between reliability and Quality of Service.

Stochastic scheduling for variation-aware virtual machine placement in a cloud computing CPS

Energy efficiency is one of the important issues in green cloud data centers (DCs). In this context, a virtual machine (VM) placement is one of the important techniques which can be used to achieve energy efficiency in such environments. Bio-inspired optimization algorithms are widely used in the literature to solve the VM placement (VMP) problem and different types of them are benefited to achieve energy efficiency while meeting Quality of Service (QoS) and user-specified constraints such as deadlines and cost. This paper presents a comprehensive survey and taxonomy of the bio-inspired VMP schemes. For this purpose, we first provide the essential concepts regarding the VMP and describe various objectives and factors which can be considered in this process. Then, we provide a taxonomy of VMP schemes regarding their applied optimization algorithms and compare their employed factors in the VMP process as well as simulator environments and the metrics which have been utilized in the verification of the investigated VMP frameworks. Finally, the concluding remarks and future researches directions are provided.

Improving the energy efficiency while guaranteeing quality of services (QoS) is one of the main challenges of efficient resource management of large-scale data centers. Dynamic virtual machine (VM) consolidation is a promising approach that aims to reduce the energy consumption by reallocating VMs to hosts dynamically. Previous works mostly have considered only the current utilization of resources in the dynamic VM consolidation procedure, which imposes unnecessary migrations and host power mode transitions. Moreover, they select the destinations of VM migrations with conservative approaches to keep the service-level agreements , which is not in line with packing VMs on fewer physical hosts. In this paper, we propose a regression-based approach that predicts the resource utilization of the VMs and hosts based on their historical data and uses the predictions in different problems of the whole process. Predicting future utilization provides the opportunity of selecting the host with higher utilization for the destination of a VM migration, which leads to a better VMs placement from the viewpoint of VM consolidation. Results show that our proposed approach reduces the energy consumption of the modeled data center by up to 38% compared to other works in the area, guaranteeing the same QoS. Moreover, the results show a better scalability than all other approaches. Our proposed approach improves the energy efficiency even for the largest simulated benchmarks and takes less than 5% time overhead to execute for a data center with 7600 physical hosts.

Virtual machine (VM) consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data centers. Most existing studies have considered VM consolidation as a bin-packing problem, but the current schemes commonly ignore the long-term relationship between VMs and hosts. In addition, there is a lack of long-term consideration for resource optimization in the VM consolidation, which results in unnecessary VM migration and increased energy consumption. To address these limitations, a VM consolidation method based on multi-step prediction and affinity-aware technique for energy-efficient cloud data centers (MPaAFVMC) is proposed. The proposed method uses an improved linear regression prediction algorithm to predict the next-moment resource utilization of hosts and VMs, and obtains the stage demand of resources in the future period through multi-step prediction, which is realized by iterative prediction. Then, based on the multi-step prediction, an affinity model between the VM and host is designed using the first-order correlation coefficient and Euclidean distance. During the VM consolidation, the affinity value is used to select the migration VM and placement host. The proposed method is compared with the existing consolidation algorithms on the PlanetLab and Google cluster real workload data using the CloudSim simulation platform. Experimental results show that the proposed method can achieve significant improvement in reducing energy consumption, VM migration costs, and service level agreement (SLA) violations.

An overview of virtual machine placement schemes in cloud computing

This paper presents a hybrid optimization model that allows a cloud service provider to establish virtual machine (VM) placement strategies for its data centers in such a way that energy efficiency and network quality of service are jointly optimized. Usually, VM placement is an activity not fully integrated with network operations. As such, the VM placement strategy does not take into account the impact it produces on the network performance in terms of quality of service parameters such as packet losses and traffic delays. The proposed strategy allows cloud providers to reach a balance between the energy efficiency of their infrastructures and the network quality of service they offer to their customers. The proposed strategy has high potential for parallelism making it feasible for the energetic optimization of large cloud infrastructures. In addition, the strategy allows network operations practices such as constraint-based routing be incorporated into the VM placement process.

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.

VM consolidation steps in cloud computing: A perspective review

Based on the convex optimization theory, the energy-efficient and scalability problems of modern data centers are studied. Then a novel application-aware virtual machine (VM) placement algorithm is proposed to cope with these problems. Firstly, the definition of VM placement fairness and utility function are given, and the basic algorithm of VM placement which fulfills server constraints of physical machines is con-ducted. Secondly, we formulate the VM placement as an optimization problem. A two-tier algorithm that efficiently solves the VM placement problem in large scale was designed. Comparing to existing methods, we illustrate the performance improvement such as reducing the number of physical machines efficiently, decreasing communication distance between VMs, improving energy-efficient and scalability of data centers.

As more and more applications migrate into clouds, the placement of virtual machines for these applications has much impact on the performance of cloud systems. A number of virtual machine (VM) placement techniques have been proposed over recent years. However, most of the existing works on VM placement ignore the response latency of the requests from tenants. In this paper, we investigate the techniques of VM placement with stochastic requests from the tenants to minimize the total (average) response latency. We first model the requests for each application from the corresponding tenant as independent Poisson stream. Moreover, the VMs are modeled as simple M/M/1 queueing systems. Then, we define the problem of VM placement for minimizing the total response delay (VMMD) and show it is NP-hard. We propose three heuristic algorithms, namely, Greedy, Local Adjustment (LA) and Simulated Annealing (SA). We conduct abundant simulation experiments to evaluate the performance of our proposed algorithms. The simulation results show that the proposed algorithms are efficient in decreasing the total response latency of the requests from tenants. Especially, the SA heuristic, which decreases the total response latency about 68% at most, shows the best performance on minimizing the total response latency in cloud systems.

A major challenge of IaaS cloud data centers is the placement of a huge number of Virtual Machines (VMs) over a physical infrastructure with a high number of nodes. The VMs placement process must strive to reduce as much as possible the number of physical nodes to improve management efficiency, reduce energy consumption and guarantee economical savings. However, since each VM is considered as a black box with independent characteristics, the VMs placement task presents scalability issues due to the amount of involved data and to the resulting number of constraints in the underlying optimization problem. For large data centers, this condition often leads to the impossibility to reach an optimal solution for VMs placement. Existing solutions typically exploit heuristics or simplified formulations to solve the placement problem, at the price of possibly sub-optimal solutions. We propose an innovative VMs placement technique, namely Class-Based, that takes advantage from existing solutions to automatically group VMs showing similar behavior. The Class-Based technique solves a placement problem that considers only some representatives for each class, and that can be replicated as a building block to solve the global VMs placement problem. Our experiments demonstrate that the proposed technique is viable and can significantly improve the scalability of the VMs placement in IaaS Cloud systems with respect to existing alternatives.

In cloud computing environment there are many strategies used for virtual machine (VM) placement. Virtualization technology can create multiple virtual machines. The mapping method of virtual machines to physical machines is thought as virtual machine placement. Objectives for VM selection can be multi-objective and mono objective. MP’s main aims are to reduce the make span time and speed. Cloud computing plays a vital role in content delivery services for processing data and scheduling the task like smart cities sensors data, healthcare data processing services (HDPS). Cloud computing for HDPS can restore patients’ records, diseases diagnosing and different medical domains in less time and fewer of price. Here we proposed a methodology which is GWO algorithm based on nature inspired concepts. The algorithm will work on optimal selection of virtual machine.

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

With the rapid development of cloud computing, the demand for infrastructure resources in cloud data centers has further increased, which has already led to enormous amounts of energy costs. Virtual machine (VM) consolidation as one of the important techniques in Infrastructure as a Service clouds (IaaS) can help resolve energy consumption by reducing the number of active physical machines (PMs). However, the necessity of considering energy-efficiency and the obligation of providing high quality of service (QoS) to customers is a trade-off, as aggressive consolidation may lead to performance degradation. Moreover, most of the existing works of threshold-based VM consolidation strategy are mainly focused on single CPU utilization, although the resource request on different VMs are very diverse. This paper proposes a novel self-adaptive VM consolidation strategy based on dynamic multi-thresholds (DMT) for PM selection, which can be dynamically adjusted by considering future utilization on multi-dimensional resources of CPU, RAM and Bandwidth. Besides, the VM selection and placement algorithm of VM consolidation are also improved by utilizing each multi-dimensional parameter in DMT. The experiments show that our proposed strategy has a better performance than other strategies, not only in high QoS but also in less energy consumption. In addition, the advantage of its reduction on the number of active hosts is much more obvious, especially when it is under extreme workloads.