دانلود رایگان مقاله برنامه ریزی پویا و تحلیل سیستم زمان واقعی چند پردازشگر

Abstract

This research work considers a scenario of cloud computing job-shop scheduling problems. We consider m realtime jobs with various lengths and n machines with different computational speeds and costs. Each job has a deadline to be met, and the profit of processing a packet of a job differs from other jobs. Moreover, considered deadlines are either hard or soft and a penalty is applied if a deadline is missed where the penalty is considered as an exponential function of time. The scheduling problem has been formulated as a mixed integer non-linear programming problem whose objective is to maximize net-profit. The formulated problem is computationally hard and not solvable in deterministic polynomial time. This research work proposes an algorithm named the Tube-tap algorithm as a solution to this scheduling optimization problem. Extensive simulation shows that the proposed algorithm outperforms existing solutions in terms of maximizing net-profit and preserving deadlines.

7. Conclusion

This project work considers a scenario of cloud computing jobshop scheduling where multiple jobs are assigned to a server that possesses multiple processors (i.e., machines). It is considered that each job has a deadline to be met, each job may have a different job length, and the profit of processing a packet of a job can differ from other jobs. It is also considered that each machine may have different processing rates and processing costs. It is also assumed that the deadlines are either hard or soft. A penalty is applied if a job fails to meet deadline. The problem has been formulated as a mixed integer non-linear programming problem. This paper proposes a realistic solution to solve the formulated problem called the Tube-tap algorithm which offers less computational complexity. Extensive simulations are carried out to compare the performance of the proposed algorithm with existing solutions. The simulation results show that the proposed algorithm outperforms the existing solutions in terms of maximizing net profit and preserving deadlines.