A Novel-Based Multi-agent Brokering Approach for Job Scheduling in a Cloud Environment

With the recent developments in the field of science and technology, the capabilities of handling complex problems have increased because of the maximum usage and management of computing power. Real resources are allocated are of reality, but the difficult part lies in proper identification and accumulation of resources which are required for solving complex problems. However, to get rid of this issue, the current trend is to use cloud computing effectively by resource sharing. The final objective is to facilitate optimum utilization and computing by aggregating idle network and processing resources like CPU cycle and storage spaces. Therefore, an effective measure has to be implemented so as to meet the job requirements by identifying appropriate service providers for successful execution. The allocation and scheduling of jobs should solve various problems and promote optimum utilization of resources. The key objective of this project is to identify and solve various problems mentioned above with the help of the multi-agent brokering approach and the jumper firefly algorithm (JFA). The multi-agent brokering approach helps in the selection of various service providers in a cloud environment, and jumper firefly helps in reducing the make span time by its status table by recording the behavior of each firefly in detail. The proposed algorithm makes the weaker ones to jump to a new position so as to attain high probability. Hence, this helps to attain a better performance in finding an optimal solution to various complex issues. From various experimental angles, the jumper firefly mechanism is considered more efficient in terms of the make span time than the standard firefly algorithm (SFA) or any other methods.