Resource Management In Cloud Computing With Increasing Dataset

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1 Resource Management In Cloud Computing With Increasing Dataset Preeti Agrawal 1, Yogesh Rathore 2 1 CSE Department, CSVTU, RIT, Raipur, Chhattisgarh, INDIA Abstract In this paper we present the cloud computing where resources are available on the temporary basis or in the leased. The resources are in the form or combination of Software and Hardware the customer utilize this resources. Problem area is when there are more users for single cloud resources at instance of time then how we can synchronize the resources scheduling for more than one user. However, managing several resources, potentially with different architectures, is difficult for users. Another difficulty is optimally scheduling applications in such environment. In this paper we are giving the strategy how the resource managed in cloud environment. Here Resource management means single cloud having cluster of functional server, so that we can schedule the cloud resources for number of different user. Than we compare the cloud computing with grid computing, Cloud computing evolves from grid computing and provides ondemand resource provisioning. Grid computing may or may not be in the cloud depending on what type of users are using it. If the users are systems administrators and integrators, users care how things are maintained in the cloud. Cloud Computing refers three services as Iaas, Paas, Saas. Infrastructure as a service refers to the sharing of hardware resources for executing services, typically using virtualization technology. With this so-called Infrastructure as a Service (IaaS) approach, potentially multiple users use existing resources. The resources can easily be scaled up when demand increases, and are typically charged for on a per-pay-use basis. In the Platform as a Service (PaaS) approach, the offering also includes a software execution environment, such as an application server. In the Software as a Service approach (SaaS), complete applications are hosted. on the hat e.g. your word processing software isn t installed locally on your PC anymore but runs on a server in the network and is accessed through a web browser. Keywords Cloud Computing, Resource, Qos, Quality Driven Algorithm, Scheduling. I. INTRODUCTION The topic of Cloud Computing is gaining more and more attention in the service research community. Cloud computing is the delivery of computing as a service rather than a product, whereby shared resources, software, and information are provided to computers and other devices as a utility (like the electricity grid) over a network (typically the Internet). Cloud Computing is such a type of computing environment, where business owners outsource their computing needs including application software services to a third party and when they need to use the computing power or employees need to use the application resources like database, s etc., they access the resources via Internet. Figure 1.Cloud computing over view Figure 2. Cloud Computing II. SCHEDULING Job scheduling (JS) system is one of the core and challenging issues in a Cloud Computing system. Traditional job scheduling systems in Cloud (or Grid) computing only consider how to meet the QoS requirements for the resources users, they seldom consider how to make the maximum profits for the resource providers. Actually, a job scheduling system plays a very important role in how to meet Cloud computing users job QoS requirements and use the Cloud resources efficiently in an economic way. Usually, from the Cloud computing resources users sides (we use CCU stands for Cloud computing user), users always think which Cloud computing resource can meet their job QoS requirements for computing (such as the due time of job finishing, the computing capacity etc.), how much money they must pay for the Cloud Computing resources. 31

2 While, from the Cloud Computing service providers (we use CCSP stand for Cloud Computing Service Provider) side, the CCSP always think how they can gain the maximum profits by offering Cloud Computing resources, apart from meeting the CCU s job QoS requirements. To make these two ends meet, the job scheduling system must take efficient and economic strategies for CCU s differentiated service QoS requirements. Focused on this issue, this paper put forward an optimistic differentiated service job scheduling system for CCSP and CCU. III. RESOURCE MANAGEMENT Cloud computing is becoming one of the most explosively expanding technologies in the computing industry today. It enables users to migrate their data and computation to a remote location with minimal impact on system performance. Typically this provides a number of benefits which could not otherwise be realized. These benefits include: Scalable - Clouds are designed to deliver as much computing power as any user wants. While in practice the underlying infrastructure is not infinite, the cloud resources are projected to ease the developer s dependence on any specific hardware. Quality of Service (QoS) - Unlike standard data centres and advanced computing resources, a well designed Cloud can project a much higher QoS than typically possible. This is due to the lack of dependence on specific hardware, so any physical machine failures can be mitigated without the user s knowledge. Specialized Environment - Within a Cloud, the user can utilize custom tools and services to meet their needs. This can be to use the latest library, toolkit, or to support legacy code within new infrastructure. Cost Effective - Users finds only the hardware required for each project. This greatly reduces the risk for institutions which may be looking to build a scalable system. Thus providing greater flexibility since the user is only paying for needed infrastructure while maintaining the option to increase services as needed in the future. Simplified Interface - Whether using a specific application, a set of tools or Web services, Clouds provide access to a potentially vast amount of computing resources in an easy and user-centric way. We have investigated such an interface within Grid systems [8]. There are a number of underlying technologies, services, and infrastructure-level configurations that make Cloud computing possible. One of the most important technologies is the use of virtualization [3],[4]. Virtualization is a way to abstract the hardware and system resources from a operating system. 32 This is typically performed within a Cloud environment across a large set of servers using a Hypervisor or Virtual Machine Monitor (VMM) which lies in between the hardware and the Operating System (OS). From here, one or more virtualized OSs can be started concurrently as seen in Figure 2, leading to one of the key advantages of Cloud computing. This, along with the advent of multi-core processing capabilities, allows for a consolidation of resources within any data centre. It is the Cloud s job to exploit this capability to its maximum potential while still maintaining a given QoS. Virtualization is not specific to Cloud computing. IBM originally pioneered the concept in the 1960 s with the M44/44X systems. It has only recently been reintroduced for general use on x86 platforms. Today there are a number of Clouds that offer Infrastructure as a Service (IaaS). The Amazon Elastic Compute Cloud (EC2) [9], is probably the most popular of which and is used extensively in the IT industry. Eucalyptus is becoming popular in both the scientific and industry communities. It provides the same interface as EC2 and allows users to build an EC2-like cloud using their own internal resources. Fig.3 Virtual machine abstraction IV. RELATED WORK Job scheduling and resource planning system is a hot and one of core research areas in Cloud and Grid Computing. It plays a similar role in Cloud and Grid Computing. Job scheduling system is responsible to select the best suitable resources in a Cloud or Grid for CCU s jobs, by taking some static and dynamic parameters restrictions of CCU s jobs into consideration. Most research work in Grid Computing can be used directly in Clouding Computing environment. References [1-8] provided a board view for the roles of job scheduling in Cloud computing and Grid computing environment. The presented paper topologies build quality driven algorithm for resource management on cloud computing [1].

3 The paper they build the corresponding non-preemptive priority M/G/1 queuing model for the jobs. They put forward a differential service oriented and selfadaptive job scheduling system in Cloud Computing environment [2]. Cloud framework is used for improving system efficiency in a data centre. To demonstrate the potential of our framework, paper presented new energy efficient scheduling, VM system image, and image management components that explore new ways to conserve power[3].various methods discussion for resource scheduling in cloud computing and there architecture for their resource management.[4-7]. The basic grid model discuss on paper generally composed of a number of hosts, each composed of several computational resources, which may be homogeneous or heterogeneous[8]. V. METHODOLOGY T0; scheduler start time Del T = inter-schedule time while (true) T=T+Del T do until (current time >= T) collect arriving tasks into meta-task Ma end do Ms=Ma schedule-meta (Ms, T+Del T) some tasks in may not have been scheduled they are inserted M;; back Ms into Ma Endwhile Function schedulemeta(meta-task Ms,Tn) Kj = completion time of Tk on Mj D = deadline of Tk Vj = availability time of machine mj Sj = size of task Tk Rj = no. of resources request by task Tk For all task Tk in Mj For all machine mj Sort the each task Tk in Meta-Task(queue) according to size,resources and time Tk by ascending order Do until(all tasks in Meta scheduled in Meta-Task OR queue is empty) Mark all machine as available for each task Tk in Ms find machine Mj to schedule select the machine that gives the highest benefit and lock the resources so that is not available for other task end for update the vector v based on the tasks that were assigned the machines update the matrix for the remaining tasks in Ms Re-compute avg. slack values sort tasks by avg. slack values end do VI. RESULTS Here many pie chart shown in below diagram with table. The Pie- chart describe resource management or scheduling. Below every pie chart,table is shown which shown the complete scheduling time of resource when various number of clients are hitting for the same resource. Fig.4 Resource Management with Cloud Computing(65 Query present in database) TABLE I - (65 QUERY) Client 1 One 2207ms Client 2 Two 116ms Client 3 Three 92ms Client 4 Four 202ms Client 5 Five 76ms 33

4 Fig.5 Resource Management with Cloud Computing(when 85 query on database) TABLE II - (85 QUERY) Client 1 One 3005ms Client 2 Two 110ms Client 3 Three 94ms Client 4 Four 203ms Client 5 Five 62ms Fig.7 Resource Management with Cloud Computing(with 150 query in database) TABLE IV - (150 QUERY) Client 1 One 3668ms Client 2 Two 125ms Client 3 Three 110ms Client 4 Four 187ms Client 5 Five 62ms Fig.6 Resource Management with Cloud Computing(with 120 Query in database) TABLE III - (120 QUERY) Client 1 One 3264ms Client 2 Two 113ms Client 3 Three 133ms Client 4 Four 72ms Client 5 Five 117ms VII. CONCLUSION This paper introduces a novel way of incorporating QoS constraints into a resource management algorithm for cloud computing. The QoS constraints are specified using the abstraction called the benefit functions. In this paper, this abstraction was used with a five QoS metric. However, with various pie chart and their table for the query are shown. As shown on result section,we see the various diagram with different time resource scheduling management on cloud computing. The aim behind the resource management is to provide user the resource in less time through cloud computing. The Table show the resource hitting time of different client, for same resource at same time. Here we use five clients at a time; in future we use eleven clients or more clients to providing them resources. Several future directions are identified for further investigation. Some of them include: (a) relaxing the assumption that accurate estimates of the execution times are known at scheduling time,(b) comparing the performance of the algorithms developed here with other scheduling algorithms, and (c) developing schemes for incorporating multiple QoS constraints into the resource management problem. 34

5 REFERENCES International Journal of Emerging Technology and Advanced Engineering [1 ] An Approach For Effective Resource Management in Cloud Computing ( International Journal EnggResearch.net, Issue Dec 2011) [2 ] An Optimistic Differentiated Service Job Scheduling System for Cloud Computing Service Users and Providers by Luqun Li (2009 Third International Conference on Multimedia and Ubiquitous Engineering). [3 ] Efficient Resource Management for Cloud Computing Environments, Andrew J. Younge, Gregor von Laszewski, Lizhe Wang Pervasive Technology Institute Indiana University Bloomington, IN USA. [4 ] Resource Provisioning for Cloud Computing by Ye Hu, Johnny Wong, Gabriel Iszlai and Marin Litoiu by IBM and Canada limited. [5 ] Quality of Service Driven Resource Management Algorithms for network computing Muthucumaru Maheswaran This research was supported by the Natural Sciences and Engineering Research Council of Canada. [6 ] Multi-Objective Problem Solving With Offspring on Enterprise Clouds Christian Vecchiola, Michael Kirley, and Rajkumar Buyya, The University of Melbourne, 3053, Carlton, Victoria, Australia. [7 ] What Networking of Information Can Do for Cloud Computing. Börje Ohlman, Anders Eriksson, Stockho lm, Sweden Ericsson research, René Rembarz, Ericsson Research Ericsson. [8 ] Implementation Issues of A Cloud Computing Platform Bo Peng, Bin Cui and Xiaoming Li 2009 Bulletin of the IEEE Computer Society Technical Committee on Data Engineering. [9 ] A Survey of Job Scheduling and Resource Management in Grid Computing World Academy of Science, Engineering and Technology [10 ] Cloud Computing Wikipedia. [11 ] Grid computing Wikipedia. [12 ] Resource management e-book. [13 ] Apache web server Wikipedia. 35