Virtual Machine Allocation Policy Research Articles

A virtual machine scheduler based on CPU and I/O-bound features for energy-aware in high performance computing clouds

A Virtual Machine (VM) scheduler for homogeneous High Performance Computing (HPC) cloud environments is presented in this paper. This scheduler considers each VM workload type (CPU or I/O-bound) to decide on its allocation. Scheduler is able to reduce energy consumption, as well as SLA violations on this cloud environment, avoiding performance losses by allocating simultaneously VMs which run different types of tasks.The scheduler method was validated through simulations conducted with the CloudSim framework. Two synthetic benchmarks representing both workload types were run previously in VMs in order to obtain basic data to design the scheduler. Thus, it was possible to implement a scheduling method that employs a VM allocation policy based on features of each application. Results showed that knowing the specifications and characteristics of environment may contribute to a better usage of resources, leading to an increased level of services availability and, finally, reducing problems caused by competition in resource usage.

DataABC: A fast ABC based energy-efficient live VM consolidation policy with data-intensive energy evaluation model

Live Virtual Machine (VM) consolidation is an effective method of improving energy-efficiency level in green data centers. Currently, to evaluate energy consumption in green data centers, energy-efficiency evaluation model with CPU utilization rate has been proposed. However, it is not suitable for data-intensive computing due to great energy consumption by GPU-intensive processing. In this paper, we have proposed a new energy evaluation model with CPU and GPU utilization rates. There are two kinds of policies in live VM consolidation: one for VM selection and the other for VM allocation. Some researchers have proposed their solutions based on VM selection policy or VM allocation policy respectively. However, it will be a better energy-efficiency VM consolidation policy if these two polices are integrated together. Based on these two policies, a fast Artificial Bee Colony (ABC) based energy-efficiency live VM consolidation policy with data-intensive energy model, named as DataABC, is proposed. DataABC adopts the idea of Artificial Bee Colony algorithm to get a fast and global optimized decision of VM consolidation. Compared with two state-of-art policies of PS-ABC and PS-ES, the total energy consumption of DataABC evidently drop by 9.72% and 5.84% respectively. As a result, based on the ESV metric, the DataABC approach has proved that (a) the energy-efficiency evaluation model with data-intensive computing is valid and that (b) DataABC can save energy with a good Quality of Service (QoS) in green data centers.

FP-ABC: Fast and Parallel ABC Based Energy-Efficiency Live VM Allocation Policy in Data Centers

Virtual machine (VM) technology is one of the energy-efficiency approaches to save energy with acceptable quality of service (QoS). In our previous studies, Artificial Bee Colony (ABC) based VM allocation policy can make a good tradeoff between performance and energy consumption. However, there are two problems in state-of-the-art ABC based approaches: (1) how to find global optimized solutions efficiently; (2) how to minimize the decision time of VM allocation. To solve these two problems, the idea of simulated annealing is adopted to get a better global optimum, and the idea of gradient descent is applied to accelerate the speed of finding solution space inΔt. Compared with state-of-the-art ABC based policies, the experimental results show that the proposed algorithm efficiently reduces energy consumption and SLA violation.

Virtual Machine Allocation Policy in Cloud Computing Using CloudSim in Java

Cloud computing is a very powerful concept that can be used to enhance the next generation data center and allow service provider to use data center capability provided by cloud and develop the application based on user requirement. Data center of this cloud computing has huge number of resources and list of applications (with different architecture, configuration and requirement for deployment) want to use those resource. Cloud computing environment uses virtualization concept and provides resources to application by creating and allocating virtual machine to specific application. There for resource allocation policies and load balance policies play very vital role in allocating and managing the resources among various application in clod computing life cycle. CloudSim is an extensible simulation toolkit that enables modeling and simulation of Cloud computing environments. The model proposed by this paper for dynamic load balance policy with considering different attributes and different service level agreements in cloud computing environment helps this environment to utilize their resources and improves performance. The proposed model uses Hungarian algorithm and the result is verified by simulating this model using CloudSim.

Using Virtual Machine Allocation Policies to Defend against Co-resident Attacks in Cloud Computing

Cloud computing enables users to consume various IT resources in an on-demand manner, and with low management overhead. However, customers can face new security risks when they use cloud computing platforms. In this paper, we focus on one such threat-the co-resident attack, where malicious users build side channels and extract private information from virtual machines co-located on the same server. Previous works mainly attempt to address the problem by eliminating side channels. However, most of these methods are not suitable for immediate deployment due to the required modifications to current cloud platforms. We choose to solve the problem from a different perspective, by studying how to improve the virtual machine allocation policy, so that it is difficult for attackers to co-locate with their targets. Specifically, we (1) define security metrics for assessing the attack; (2) model these metrics, and compare the difficulty of achieving co-residence under three commonly used policies; (3) design a new policy that not only mitigates the threat of attack, but also satisfies the requirements for workload balance and low power consumption; and (4) implement, test, and prove the effectiveness of the policy on the popular open-source platform OpenStack.

On the efficiency of several VM provisioning strategies for workflows with multi-threaded tasks on clouds

Cloud computing promises the delivery of on-demand pay-per-use access to unlimited resources. Using these resources requires more than a simple access to them as most clients have certain constraints in terms of cost and time that need to be fulfilled. Therefore certain scheduling heuristics have been devised to optimize the placement of client tasks on allocated virtual machines. The applications can be roughly divided in two categories: independent bag-of-tasks and workflows. In this paper we focus on the latter and investigate a less studied problem, i.e., the effect the virtual machine allocation policy has on the scheduling outcome. For this we look at how workflow structure, execution time, virtual machine instance type affect the efficiency of the provisioning method when cost and makespan are considered. To aid our study we devised a mathematical model for cost and makespan in case single or multiple instance types are used. While the model allows us to determine the boundaries for two of our extreme methods, the complexity of workflow applications calls for a more experimental approach to determine the general relation. For this purpose we considered synthetically generated workflows that cover a wide range of possible cases. Results have shown the need for probabilistic selection methods in case small and heterogeneous execution times are used, while for large homogeneous ones the best algorithm is clearly noticed. Several other conclusions regarding the efficiency of powerful instance types as compared to weaker ones, and of dynamic methods against static ones are also made.

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.

Task scheduling and virtual machine allocation policy in cloud computing environment

Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the computing power of virtual machines(VMs) and network status can greatly affect the completion time of data intensive tasks. However, most of the current resource allocation policies focus only on network conditions and physical hosts. And the computing power of VMs is largely ignored. This paper proposes a comprehensive resource allocation policy which consists of a data intensive task scheduling algorithm that takes account of computing power of VMs and a VM allocation policy that considers bandwidth between storage nodes and hosts. The VM allocation policy includes VM placement and VM migration algorithms. Related simulations show that the proposed algorithms can greatly reduce the task completion time and keep good load balance of physical hosts at the same time.