An Energy Efficient VM Allocation using Best Fit Decreasing ... - IJESC

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Research Article

Volume 7 Issue No. 1

An Energy Efficient VM Allocation using Best Fit Decreasing Minimum Migration in Cloud Environment Anurag Shrivastava1 , Vaibhav Patel2 , Su kanya Rajak3 Assistance Professor1, 2 , PG, Scholar3 Depart ment of CSC NIRT, BHOPA L, India Abstract: Infrastructure as a Service (IaaS) has become one of the most dominant features that cloud computing offers nowadays. IaaS enables datacenter’s hardware to get virtualized which allows Cloud providers to create multiple Virtual Machine (VM) instances on a single physical mach ine, thus improv ing resource utilization and decreasing energy consumption. VM allocation includes issues like determine load of host and also determine approach for selection of VMs for migration and placement o f VMs to suitable hosts. VMs need to be migrated fro m over utilized host to guarantee that demand for co mputer resources and performance requirements are accomplished. Besides, they need to be mig rated fro m underutilized host to deactivate that host for saving power consumption. In order to solve the problem of energy and performance, efficient dynamic VM consolidation approach is introduced in literat ure. In this work, proposed multip le redesigned VM allocation algorithms and introduced a technique by clustering VMs to migrate by t aking account both CPU utilization and allocated RAM. We implement and study the performance of our algorithms on a cloud computing simu lation toolkit known as CloudSim using Planet Lab, Bitbrains and Google Cluster workload data. Simulat ion results demonstr ate that our proposed techniques outperform the default VM Placement algorith m designed in CloudSim. Keywords: Cloud Co mputing, Dynamic consolidation, VM Allocation, CloudSim, PlanetLabs, Bit Brains, GoogleCluster. I. INTRODUCTION With the limited amount of physical resources available, resource allocation beco mes a challenging task for the cloud service provider. Since cloud computing is a multi-tenancy model, mu ltip le users‟ requests for the cloud resources. So cloud service provider has to decide on how many virtual resources are to be created based on the cloud users‟ requests. Also which virtual machine (VM) has to be mapped onto which physical mach ine (PM) has to be taken care. That is, VM-PM mapping techniques have to be considered. At what instance VM migrat ion has to be done is also based on identifying heavily loaded node and lightly loaded node. So the ultimate goal of the cloud service provider is to maximize profit and maximize resource utilizat ion and the goal of cloud user is to minimize payment by renting the resources. There are various parameters to be considered while allocating resources. While allocating resources to the cloud user, underutilization (wastage) of resources due to over provisioning and overutilization (due to under provisioning) should be avoided. Allocation of resources should consider various parameters like Quality of Serv ice parameters like response time, performance, availability, reliability, security, throughput etc. Performance : Fo r some application demands, performance is one of the important criteria. The system should perform well to provide service to the cloud user. Response Ti me: For interactive applications, response time is an important factor. The system must respond well for these kinds of applications.

International Journal of Engineering Science and Computing, January 2017

Reliability: The system used should be reliable so that the cloud user has no head ache of changing the system. Availability: Whenever cloud resources are requested the cloud service provider must be able to allocate within short span. Security: Fo r critical applications like online transaction applications, system used has to be secure. Otherwise it is not safe to use such a kind of system. Throughput: No. of applicat ions run per unit time should be more. As an increasing nu mber of co mplex applications leverage the computing power of the cloud for parallel co mputing, it becomes important to efficiently manage computing resources for these applications. Due to the difficulty in realizing parallelism, many parallel applications show a pattern of decreasing resource utilization along with the increase of parallelism. As the main aim o f cloud computing is to provide resources as a service on demand to the user. In this work, there are going carried out two main p roblems. 1. Reallocation of virtual machines. 2. A llocation of v irtual machines. The process of selecting which virtual machines (VMs) should be located (i.e. executed) at each physical machine (PM) of a datacenter is known as Virtual Machine Placement (VMP). The VM P prob lem has been extensively studied in cloud computing literature and several surveys have already been presented. Existing surveys focus on specific issues such as:

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(1) Energy-efficient techniques applied to the problem, (2) Particular arch itectures where the VMP problem is applied specifically federated clouds. (3) Methods for comparing performance algorith ms in large on demand clouds.

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(2.1) The Min imu m M igration Time (MMT) policy mig rates a VM v that requires the minimu m t ime to co mplete a migration relatively to the other VMs allocated to the host. (2.2) The Migrat ion time is estimated as the amount of RAM utilized by the VM divided by the spare network bandwidth available for the host j. Let Vj be a set of VMs currently allocated to the host j.

II. RELATED WORK Ting et al [1], Cloud Co mputing refers to constructed data center or "super computer" by virtualization technology and provides computing and storage resources, as well as the application container environment of software running, to software developers in a manner o f free or h iring. Liu et al [2], p ropose priority-based method to consolidate parallel workloads in the cloud. We leverage v irtualization technologies to partition the co mputing capacity of each node into two tiers, the foreground virtual machine (VM) tier (w ith high CPU priority) and the background VM tier (with low CPU priority).

(2.3) MMT policy finds amount of RAM utilized by VM as per availability of network bandwidth. Step 3: The VMs selected for migrat ion are allocated to the destination hosts. For this purpose, perform following steps: (3.1) Sort VM (1, 2 ...k) in order of their decreasing CPU utilizat ion (3.2) For every Vi in V(1,2,…,k) perfo rm manPower