In this paper, we consider the widespread multi-stage job scheduling problem (e.g., in Big Data processed by MapReduce) in which jobs arrive at hybrid cloud systems stochastically. The objective is to minimize the number of elastic computing instances. Along with hard deadlines of jobs, the problem under study is NP-hard in strong sense. In terms of initial job priorities, timetables are constructed by adjusting job priorities adaptively and generating feasible schedules iteratively. Job sequences are generated by two simple dispatching rules. A fast local search heuristic and a rescheduling process are developed for improving the obtained sequences. Experimental results show that the proposed heuristics improve the utilization of computing resources effectively while meeting the cloud service quality requirements.
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