Load balancing across a networked environment is a monotonous job. Moreover, if the job to be distributed is a constraint satisfying one, the distribution of load demands core intelligence. This paper proposes parallel processing through Global Evalu
ation Function by means of randomly initialized agents for solving Constraint Satisfaction Problems. A potential issue about the number of agents in a machine under the invocation of distribution is discussed here for securing the maximum benefit from Global Evaluation and parallel processing. The proposed system is compared with typical solution that shows an exclusive outcome supporting the nobility of parallel implementation of Global Evaluation Function with certain number of agents in each invoked machine.
Distributed processing across a networked environment suffers from unpredictable behavior of speedup due to heterogeneous nature of the hardware and software in the remote machines. It is challenging to get a better performance from a distributed sys
tem by distributing task in an intelligent manner such that the heterogeneous nature of the system do not have any effect on the speedup ratio. This paper introduces homogenization, a technique that distributes and balances the workload in such a manner that the user gets the highest speedup possible from a distributed environment. Along with providing better performance, homogenization is totally transparent to the user and requires no interaction with the system.
