© 2014, IJARCSSE All Rights Reserved Page | 807 Volume 4, Issue 2, February 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Analysis of Public Cloud Load Balancing using Partitioning Method and Game Theory Mangal Nath Tiwari * Kamalendra Kumar Gautam Dr Rakesh Kumar Katare Dept. of CS, APS University Dept. of CS, APS University Dept. of CS, APS University Rewa, MP, India Rewa, MP, India Rewa, MP, India Abstract—Cloud computing is a provision of providing networked, on-line, on-demand services pay per use basis. Several issues as scalability, security, performance etc are discussed so far by many researchers for the cloud computing. Cloud partitioning is an optimal approach for public cloud. In public cloud environment various nodes are used with required computing resources situated in different geographic locations, so this strategy simplifies the load distribution across the multiple nodes, but fault tolerance and load balancing are most important problems obtaining high performance in the system. Load balancing is the process of distribution of workload among different nodes or processor. The purpose of load balancing is to improve the performance of a cloud environment through an appropriate distribution strategy. Game theory is the formal study of conflict and cooperation. Game theoretic concepts apply whenever the actions of several agents are interdependent. The game theoretic algorithms help to obtain a user optimal load balancing which ultimately improves overall performance of cloud computing. This paper introduces a better approach for public cloud load distribution using partitioning and game theory concept to increase the performance of the system. Keywords— Cloud computing, Dynamic Load Balancing (DLB), Game Theory, Public Cloud, Cloud Partitioning. I. INTRODUCTION Cloud computing is the use of computing resources that are delivered as a service over a. Load balancing and provisioning in cloud computing systems is really a challenge job. For solving such problem always a distributed and dynamic solution is required. Because it is not always practically feasible or cost efficient to maintain one or more idle services just as to fulfils the required demands Jobs cannot be assigned to appropriate servers and clients individually for efficient load balancing as cloud is a very complex structure and components are present throughout a wide spread area. Here some uncertainty is attached while jobs are assigned. The aim is to provide an evaluation and comparative study of these approaches [13]. Workload distribution problem in cloud computing environment is very crucial and complex task till today, because prediction of user request arrivals on the server is not possible. In cloud environment, each virtual machine has different capabilities, so it becomes more complex to schedule job and balance the work-load among nodes. In this paper we introduce a dynamic strategy to balance workload among nodes. This scheme provides more flexibility and performance in the system. Virtualization is very useful concept in context of cloud systems. Virtualization means “something which isn’t real”, but gives all the facilities of a real. It is the software implementation of a computer which will execute different programs like a real machine. Virtualization is related to cloud, because using virtualization an end user can use different services of cloud Ashish et al. [1]. The remote datacenter will provide different services in a full or partial virtualized manner Public cloud applications, storage, and other resources are made available to the general public by a service provider. These services are free or offered on a pay-per-use model. Generally, public cloud service providers like Amazon AWS, Microsoft and Google own and operate the infrastructure Ashish et al. [1]. Public cloud services may be free or offered on a pay-per-usage model. The term "public cloud" arose to differentiate between the standard model and the private cloud, which is a proprietary network or data canter that uses cloud computing technologies, such as virtualization. Examples of public clouds include Amazon Elastic Compute Cloud (EC2), IBM's Blue Cloud, Sun Cloud, Google Drive, Google Apps, and Windows Azure Services Platform [6]. In this paper we reviewed a dynamic strategy to balance workload among nodes with the help of cloud partitioning and game theory concept. In this work various nodes are used with required computing resources situated in different geographic location. II. LOAD BALANCING “The load balancing technique used to make sure that none of the node is in idle state while other nodes are being utilized”. In order to balance the lode among multiple nodes you can distribute the load to another node which has lightly loaded. Thus distributing the load during runtime is known as Dynamic Load Balancing technique. Load balancing algorithm can be divided into two categories as 1) Static and 2) Dynamic. In static load balancing algorithm, all the information about the system is known in advance, and the load balancing strategy has been made by load balancing algorithm at compile time. This load balancing strategy will be kept constantly during runtime of the system.