An optimized cost-based data allocation model for heterogeneous distributed computing systems

<span lang="EN-US">Continuous attempts have been made to improve the flexibility and effectiveness of distributed computing systems. Extensive effort in fields connectivity technologies, network programs, high processing components, storage helps improvise results. However, concerns such as slowness response, long execution time, completion time identified stumbling blocks that hinder performance require additional attention. These defects increased total system cost data allocation procedure for a geographically dispersed setup difficult. The load-based architectural model has strengthened performance. To do this, an abstract job is employed, query file containing input processed on directed acyclic graph. jobs are executed engine with lowest cost, system's calculated. computed by summing costs communication, computation, network. will be reduced using Swarm intelligence algorithm. In heterogeneous systems, suggested approach reduce distribution. According simulation results, technique efficiently lowers optimizes partitioned allocation.</span>