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We study the scheduling decision for an application consisting of dependent tasks, in a generic cloud computing system comprising a network of heterogeneous local processors and a remote cloud server. We formulate an optimization problem to find the offloading decision that minimizes the overall application execution cost, subject to an application completion deadline. Since this problem is NP-hard, we propose a heuristic algorithm termed Individual Time Allocation with Greedy Scheduling (ITAGS) to obtain an efficient solution. ITAGS first uses a binary-relaxed version of the original problem to allocate a completion deadline to each individual task, and then greedily optimizes the scheduling of each task subject to its time allowance. Through trace-based simulation using real applications, as well as various randomly generated task trees, we study the performance of ITAGS, highlighting the effect of the application deadline, communication delay, number of processors, and number of tasks. We further demonstrate the substantial performance advantage of ITAGS over existing alternatives.
Sundar et al. (Sun,) studied this question.