Detection of False Data Injection Attacks and Genetic Algorithm Based Dynamic Jobs Scheduling in Grid Computing

Shruti B Patel; Prof. Manikandan K

Abstract

A computational grid is a large scale, heterogeneous collection of autonomous systems, geographically distributed and interconnected by low latency and high bandwidth networks. The sharing of computational resources is a major aspect of grids. Scheduling is a key problem in emergent computational systems, such as Grid and P2P, in order to benefit from the large computing capacity of such systems. Our approach is to dynamically generate an optimal schedule to complete the different tasks in a minimum period of time as well as utilizing the resources in an efficient way. There are so many approaches for scheduling like Genetic Algorithm (GA), Simulated Annealing (SA) and Ant Colony optimization (ACO). In this paper, we would like to present Genetic Algorithms (GAs) based schedulers for efficiently allocating jobs to resources in a Grid system. We would also like to implement GAs for designing efficient Grid schedulers when makespan is minimized. Our GA-based schedulers are very fast and hence they can be used schedule jobs arrived in the Grid system. Adding to this, increased connectivity of grid helps for bidirectional communications presents extreme security vulnerabilities. The proposed system also provides approach for false data detection in smart grids, like MD5 message-digest algorithm used as cryptographic hash function for message authentication and to verify the content of the message.

Keywords

False data detection, Genetic Algorithm, Makespan, Minimum completion time, Fitness.

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Cites this article as

S. B. P. . , P. M. K. . , "Detection of False Data Injection Attacks and Genetic Algorithm Based Dynamic Jobs Scheduling in Grid Computing", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-6, Issue-2, Page No-3-8, 2019. Available from: