Optimizing BOINC scheduling using genetic algorithm based on thermal profile

Abstract

Berkeley Open Infrastructure for Network Computing (BOINC) is an open source middleware for volunteer and grid computing. Main function of BOINC is to use the idle time of computer to run some computation at background. Universiti Teknologi Petronas (UTP) campus grid used BOINC as middleware in computer labs. However, computer can only process jobs during weekday and office hour because they want to reduce energy used for cooling power. In order to fully utilize the computer in labs, we proposed new jobs scheduling algorithm can run based on thermal constraint. The proposed algorithm is combination of thermal profile and heuristic approach. We use genetic algorithm to find the best combination of clients and jobs based on clients order and least execution time. Then we compare our algorithm with brute force method. Result from simulation it shows that proposed algorithm successfully distribute and execute job based on thermal constraints in an effective and efficient way compare to brute force method.