Indian Journal of Science and Technology

Abstract

Cloud computing has been recently emerged on internet as a model for hosting and providing services. Such model is impressive for business owners due to that there is no more need the users to declare their requirements to prepare a plan for answering them, therefore, an organization can start its work in small size and add more resources only when demand for services is increased. In addition to it, Cloud computing provides more advantages for organizations and individuals. For this reason it has become a hot topic in academic and business environments and as it was expected, many are attracted and nowadays we are facing with many cloud service providers. Generally, resources management within cloud environment, due to their heterogeneity, is a complicated issue. Timing algorithms within parallel distributed systems are playing important role for tasks timing and sending them to the appropriate resources. Timing issue is a technique for equitable distribution of resources to clients and it is for achieving optimal productivity of resources with least responding time and more importantly is to avoid extra overhead on resources. Timing issue in its kind is a NP issue. Therefore its solution through multi-purpose developmental algorithms is an appropriate method. Two purposes of time minimization of task completion and finishing cost to the customer in task timing within cloud environment has conflict and inconsistency that current optimal single-purpose methods cannot resolve them. Therefore, use of new methods by multi-purpose optimization capability provides optimized response according to optimization of all purposes approach. In this research the issue of independent task timing by the purpose of lowest price, least completion time consumption and maximum load balancing within system by using optimized multi-purpose particles congestion algorithm has been studied and it is concluded that the recommended method in comparison with multi-purpose genetic algorithm has better results.
Keywords: Cloud Computing, Multi-Purpose Optimization, PSO Algorithm, Timing