Dynamic scheduling and analysis of real time systems with multiprocessors

Dynamic scheduling and analysis of real time systems with multiprocessors

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This research work considers Baby Tee 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 Dugouts 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.