Abstract

The increase in demands of information services, cloud computing proves itself to enhance scaling, agility, availability and flexibility. But cloud computing has very critical issues as load balancing, security and fault tolerance. As number of users is increasing day by day, the crucial task of cloud computing is to adjust loads of memory, CPU and network to fulfill demands of all clients. For that a number of static and dynamic load balancing algorithms are proposed. Here we are proposing a load balancing algorithm based on clusters in which cluster are formed on geographical bases. All cloud servers arranged in distributed manner. Each Cloud server have a queue length (for ex. 100) of jobs allocated to it. A server can serve up to 100 requests. As 101 request came cluster apply its load balancing algorithm. Along with Dynamic distributed load balancing algorithm we have proposed a security algorithm to secure data transmission between client and Cloud service provider. According to this security algorithm client initiates a key generation process to generate encryption and decryption key pairs that will be valid for a particular message. Likewise, a number of key pairs generated. Client can use any key pair for any data encryption/decryption which will be stored on Cloud. Also Client have to keep a record of Key Pairs with Password/PIN. This algorithm performs better in terms of Throughput, Overhead, Fault Tolerance, Resource Utilization, Response Time, and Scalability.

Highlights

  • Cloud Computing is not a single term it is everything in one word

  • Cloud computing is totally different from its name, basically services refer to infrastructure, platform, software over the single network i.e. internet

  • In this paper we are proposing a load balancing algorithm based on clusters in which cluster are formed on geographical basis

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Summary

INTRODUCTION

Load balancing is the mechanism to balance the load to the cloud nodes in a manner that Computing Communication and Signal Processing each & every node effectively utilizes the resources and minimize the response time. Rahul Rathore et al, International Journal of Advanced Research in Computer Science, 9 (1), Jan-Feb 2018,415-418

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