Energetic Resource Allocation Framework Using Virtualization in Cloud

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1 Energetic Resource Allocation Framework Using Virtualization in Ms.K.Guna *1, Ms.P.Saranya M.E *2 1 (II M.E(CSE)) Student Department of Computer Science and Engineering, 2 Assistant Professor Department of Computer Science and Engineering, ANNA University Chennai, India. ABSTRACT: Virtualization provides an efficient solution to the objectives of the cloud computing paradigm by facilitating creation of Virtual Machines (VMs) over the underlying physical servers, leading to improved resource utilization and abstraction. Virtualization refers to creating a virtual version of a device or a resource such as a server, a storage device, network or even operating system where the mechanism divides the resource into one or more execution environments. To analyse the behaviour of a data centres: utilization, availability, waiting time, and responsiveness. The factors that a cloud must take into account are elasticity, scalability, live migration of VMs and performance isolation. Live migration of VMs, the process of dynamically transferring a virtual machine across different servers on the fly, has proved to represent a new opportunity to enable responsive and dynamic resource management in modern data centres. Through this algorithm to achieve migrations of VMs on the basis of overloading that occurs a physical server in cloud data centres. As indicated the results, the algorithm provides relatively less performance degradation that occurs due to VM migrations. The SLA violation is also less compared to other techniques and this means the cloud will incur less cost from VM migrations. Further, MHOD algorithm performs host selection and VM reallocation quicker than the existing algorithms. Maintenance of physical servers can be efficiently achieved through the algorithm as it leads to efficient consolidation of VMs. Keywords Virtualization, cloud computing, SLA violation, host selection, Live migration, performance degradation. I. INTRODUCTION In cloud computing, Resource Allocation (RA) is the process of assigning available resources to the needed cloud applications over the internet. Resource allocation starves s if the allocation is not managed precisely [1]. Resource provisioning solves that problem by allowing the s to manage the resources for each individual module. Resource Allocation Strategy (RAS) [2] is all about integrating cloud activities for utilizing and allocating scarce resources within the limit of cloud environment so as to meet the needs of the cloud application [3]. It requires the type and amount of resources needed by each application in order to complete a user job. The order and time of allocation of resources are also an input for an optimal RAS [4]. An optimal RAS should avoid the following criteria as follows: Resource Contention - Resource contention arises when two applications try to access the same resource at the same time. Over Provisioning - Over provisioning arises when the ISSN: Page 171

2 application gets surplus resources than the demanded one. Under Provisioning - Under provisioning of resources occurs when the application is assigned with fewer numbers of resources than it demanded. From the perspective of a cloud, predicting the dynamic nature of users, user demands, and application demands are impractical [5]. For the cloud users, the job should be completed on time with minimal cost. Hence due to limited resources, resource heterogeneity, locality restrictions, environmental necessities and dynamic nature of resource demand, it needs an efficient resource allocation system that suits cloud environments [6]. resources consist of physical and virtual resources. The physical resources are shared across multiple compute requests through virtualization and provisioning [7]. The request for virtualized resources is described through a set of parameters detailing the processing, memory and disk needs [8]. Provisioning satisfies the request by mapping virtualized resources to physical ones. The hardware and software resources are allocated to the cloud applications on-demand basis[9]. The paper is organized as follows: Section II presents the proposed method for Listening skill. Section III describes diagrammatic view of ie., the system architecture of proposed system. Section IV describes the algorithms to be used in a system. Section V describes the different experiments carried out and the results obtained. Finally in section VI, summarizes the main conclusions and future research. EXISTING SYSTEM: Resource Management is critical in cloud computing. With improper resource management, applications might experience network congestion, long time wait, overused CPU and memory, and security problems. To maximized cloud computing infrastructure utilization and minimize total cost of both the cloud computing infrastructure and running applications, resources need to be managed properly. To overcome this there are kinds of resources in the large-scale computing infrastructure need to be managed, CPU load, network bandwidth, disk quota, and even type of operating systems. To provide better quality of, resources are provisioned to the users or applications, via load balancing mechanism, high availability mechanism and security and authority mechanism. To maximize cloud utilization, the capacity of application requirements shall be calculated so that minimal cloud computing infrastructure devices shall be procured and maintained. Given access to the cloud computing infrastructure, applications shall allocate proper resources to perform the computation with time cost and infrastructure cost minimized. Drawbacks: For all the potential of the cloud storage model, there are still a few disadvantages. By utilizing cloud storage, users relinquish direct control over their data. Although a cloud storage is far less likely to have its data lost or compromised than most individuals or organizations, people are still more comfortable knowing precisely where their critical data is located and who is personally responsible for it. Access to data stored on a distributed network is also constrained by the access to the Internet. Lower Internet bandwidth will result in decreased performance and an Internet outage will completely sever an organization s access to its information. II.PROPOSED SYSTEM: The proposed system considers the process of resource management for a largescale cloud environment. Such an environment includes the physical ISSN: Page 172

3 infrastructure and associated control functionality that enables the provisioning and management of cloud s. The perspective take is that of a cloud, which hosts sites in a cloud environment. The cloud owns and administrates the physical infrastructure, on which cloud s are provided. It offers hosting s to site owners through a middleware that executes on its infrastructure. Site owners provide s to their respective users via sites that are hosted by the cloud. Therefore, the user demands are transformed to this virtual cloud server. Through this efficient method, the user s demands will be satisfied successfully by serving the customer without waiting. Therefore, the resources will be allocated dynamically. This work contributes towards engineering a middleware layer that performs resource allocation in such a cloud environment, with the following design goals: 1) Performance objective: To consider computational and memory resources and the objective is to achieve max-min fairness for computational resources under memory constraints. 2) Adaptability: The resource allocation process must dynamically and efficiently adapt to changes in the demand for cloud s. 3) Scalability: The Resource allocation process must be scalable both in the number of machines in the cloud and the number of sites that the cloud hosts. This means that the resources consumed per machine in order to achieve a given performance objective must increase sub linearly with both the number of machines and the number of sites. balance between the two goals. To make the following contributions: To develop a resource allocation system that can avoid overload in the system effectively while minimizing the number of servers used. To introduce the concept of skewness to measure the uneven utilization of a server. By minimizing skewness, it can improve the overall utilization of servers in the face of multidimensional resource constraints. To design a load prediction algorithm that can capture the future resource usages of applications accurately without looking inside the VMs. The algorithm can capture the rising trend of resource usage patterns and help reduce the placement churn significantly. Advantage computing is a usage of very large scalable and virtualized resources in a dynamic way over the internet. Due to the rapid growth of cloud environment usage many tasks require to be executed by the available resources. At the same time it should be possible to achieve better performance, optimizing the servers, reduce migration, support green computing, better resource utilization etc. So resource allocation using virtual machine plays a most important role in cloud environment because it should allocate proper resource to various machines to get maximum benefit. In this project, to present the design and implementation of an automated resource management system that achieves a good ISSN: Page 173

4 III. SYSTEM ARCHITECHTURE: PM 1 PM2. PM n Web server, remote desktop, DNS, Mail, Map/Reduce, etc., LNM (local node manager) CONTROL ALGORITHM In the control algorithm a different set of technique is used to predict non-stationary workloads of the system. In this two set of process are used Markov Host Overload Detection (MHOD) and Optimal Markov Host Overload Detection (MHOD-OPT). User CTRL VM Scheduler Predicts (future resource allocation demands) Hot spot (Detect resource utilize) Cold spot (Check utilization) Migratio n List TABLE I IV. ALGORITHMS Algorithm Allocation parameter Feature SKEWNESS ALGORITHM Skewness is used to quantify the unevenness in utilization of multiple resources on the server. By minimizing the skewness leads to combine of different combine different workloads and improve utilization of server. Skewness consists of three steps: load prediction, hot spot migration, and green computing. GREEN SCHEDULING ALGORITHM Skewness Control Hotspot, cold spot throughput Cold spot throughput Green scheduling MHOD- OPT,MHOD To measure unevenness in resource utilization Usage of idle servers are reduce To estimate the migration probability Green scheduling can determine which server to be in running state. It will turn on and turnoff servers based on load and virtual machine is allocated. Server must be in four states: OFF, ON, SHUTTING, RUNNING. Based on platform any of the state is triggered. V.RELATED WORKS A. Dynamic resource allocation Resource Allocation (RA) is the process of assigning available resources to the needed cloud applications over the internet. Resource allocation starves s if the allocation is not managed accurately. Resource provisioning solves that problem ISSN: Page 174

5 by allowing the s to manage the resources for each individual module. User Request restrictions or limitations, and obligations that cloud consumers must accept. User Request Service level Service B. The cloud is responsible for maintaining an agreed-on level of and provisions resources accordingly. A CSP, who has significant resources and expertise in building and managing distributed cloud storage servers, owns and operates live Computing system, it is the central entity of cloud. activities for utilizing and allocating scarce resources within the limit of cloud environment so as to meet the needs of the cloud application. It requires the type and amount of resources needed by each application in order to complete a user job. The order and time of allocation of resources are also an input for an optimal resource allocation. Check space Provide resource For user request Provide C. consumer consumer represents a person or organization that maintains a business relationship with, and uses the from, a cloud. Users, who stores data in the cloud and rely on the cloud for data computation, consists of both individual consumers and organizations. consumers use Service-Level Agreements (SLAs) for specifying the technical performance requirements to be fulfilled by a cloud. A cloud may also list in the SLAs a set of D. Virtual machine environment Virtualization provides an efficient solution to the objectives of the cloud computing paradigm by facilitating creation of Virtual Machines (VMs) over the underlying physical servers, leading to improved resource utilization. Virtualization refers to creating a virtual version of a device or a resource such as a server, a storage device, network or even operating system where the mechanism divides the resource into one or more execution environments. Number user When a physical server is considered to be overloaded requiring live migration of one or more VMs from the physical server under consideration. Selection of VMs that should be migrated from an overloaded physical server. VM selection policy (algorithm) has to be applied to carry out the selection process. Finding a new placement of the VMs selected for migration from the overload and physical servers and finding the best physical. Overload in server Waiting time for user Virtual machine E. Resource manager Service management in this context covers all the data centre operations activities. This broad discipline considers the ISSN: Page 175

6 necessary techniques and tools for managing s by both cloud s and the internal data centre managers across these physical, IT and virtual environments. The availability of Service computing clouds gives researchers access to a large set of new resources for running complex scientific applications. However, exploiting cloud resources for large numbers of jobs requires significant effort and expertise. User request Managed by server F. Performance evaluation In cloud paradigm, an effective resource allocation strategy is required for achieving user satisfaction and maximizing the profit for cloud s. Some of the strategies discussed above mainly focus on CPU, memory resources.secured optimal resource allocation algorithms and framework to strengthen the cloud computing paradigm VI.FUTURE WORK: Finally, as the next step in our research will be the future demands comes from the unspecified user or customer is to be measured and concerned with the development of the better allocation algorithm which is in heterogeneous and works in dynamic environment using virtual machines. CONCLUSION: Virtualization is the creation of a virtual i.e., rather than actual version of a storage device or network resources. This project gives about the cloud data management interface by using storage virtualization mechanism. The open cloud computing interface is an emerging standard for interoperable interface management in the cloud. computing can solve complex set of tasks in shorter time by proper resource utilization. To make the cloud to work efficiently, best resource allocation strategies have to be employed. Utilization of resources is one of the most important tasks in cloud computing environment where the user s jobs are scheduled to different machines. The various strategies have been studied and classified. The different features of the algorithms have been studied. References: Figure 1 Figure 2 1) J. Kirch. Virtual machine security guidelines. The center for Internet Security, September CIS-VM-Benchmark- v1.0.pdf. 2) Sugerman, Jeremy; Venkitachalam, Ganesh; Lim, Beng-Hong: Virtualizing I/O Devices on VMware Workstation's Hosted Virtual Machine Monitor. In: Proceedings of the General Track: 2002 USENIX Annual Technical Conference. USENIX Association. - ISBN X, ISSN: Page 176

7 3) Soltesz, Stephen; Poetzl, Herbert; Fiuczynski, Marc E.; Bavier, Andy; Peterson, Larry: Container-based operating system virtualization: a scalable, high-performance alternative to hypervisors. In: EuroSys '07: Proceedings of the 2007 conference on EuroSys. ACM Press. - ISSN , ) Popek, Gerald J.; Goldberg, Robert P.: Formal requirements for virtualizable third generation architectures. In: Commun. ACM 17 (1974), July, Nr. 7, DOI / ISSN ) A. Mann. The pros and cons of virtualization. BTQ, s-cons-virtualization&page= virtview%research. 6) Rosenblum M. and Garfinkel T. Virtual machine monitors: current technology and future trends. Computer, 38(5):39-47, May ) Renato J. Figueiredo, Peter A. Dinda, and J. Fortes. A case for grid computing on virtual machines. In ICDCS '03: Proceedings of the 23rd International Conference on Distributed Computing Systems, page 550, Washington, DC, USA, IEEE Computer Society. 8) G. J. Popek and R. P. Goldberg, "Formal requirements for virtualizable third generation architectures, " Comm. ACM, vol. 17, no. 7, pp , ) K. J. Higgins. Vm's create potential risks. Technical report, darkreading, nt.asp?doc-id= ISSN: Page 177