An Entropic Optimization Technique in Heterogeneous Grid Computing Using Bionic Algorithms

The wide usage of the Internet and the availability of powerful computers and high-speed networks as lowcost commodity components have a deep impact on the way we use computers today, in such a way that these technologies facilitated the usage of multi-owner and geographically distributed resources to address large-scale problems in many areas such as science, engineering, and commerce. The new paradigm of Grid computing has evolved from these researches on these topics. Performance and utilization of the grid depends on a complex and excessively dynamic procedure of optimally balancing the load among the available nodes. In this paper, we suggest a novel two-dimensional figure of merit that depict the network effects on load balance and fault tolerance estimation to improve the performance of the network utilizations. The enhancement of fault tolerance is obtained by adaptively decrease replication time and message cost. On the other hand, load balance is improved by adaptively decrease mean job response time. Finally, analysis of Genetic Algorithm, Ant Colony Optimization, and Particle Swarm Optimization is conducted with regards to their solutions, issues and improvements concerning load balancing in computational grid. Consequently, a significant system utilization improvement was attained. Experimental results eventually demonstrate that the proposed method's performance surpasses other methods.