Maximizing job benefits on multiprocessor systems using a greedy algorithm

This project considers a benefit model for on-line preemptive multiprocessor scheduling. In this model, each job arrives with its own benefit function and execution time. The flow time of a job is the time between its arrival and its completion. The benefit function determines the benefit gained for any given flow time. The goal is to maximize the total benefit gained only by the jobs that meet their deadlines. In order to achieve this goal, a variety of approximation algorithms and their applications in multiprocessor scheduling were studied. A greedy algorithm with 2- approximation ratio is proposed to be added to an existing benefit based scheduling algorithm, in order to reduce the delay of each job, by assigning it to the processor with least utilization so far. This method will decrease the flow time of the jobs, resulting in higher benefits gained by each job. Also, evaluation of this approach shows that it uses the CPU cycles more efficiently by providing more balanced distribution of the jobs between the processors. Therefore, more jobs can meet their deadlines and add their gained benefits to the total benefit. In addition, the proposed method is computationally less expensive than the existing benefit based method.