Subset Selection for Hybrid Task Scheduling with General Cost Constraints

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Subset selection problem for task scheduling with general cost constraints exists widely in IoT applications. Its objective is to select several profitable tasks to execute under routing and cost constraints such that the total profit is maximized. Most prior arts only focus on either online tasks or offline tasks, which are usually inapplicable in practical applications where online tasks and offline tasks co-exist. In this paper, we study the subset selection problem for HybrId Task Scheduling with general cost constraints (HITS), in which both online and offline tasks are scheduled to maximize the overall profit. We first divide the HITS problem into online and offline subproblems and propose two algorithms to solve them with bounded approximation ratios. Furthermore, we propose an approximation algorithm for the hybrid scenario where both online and offline tasks are considered. Extensive simulations show that our proposed algorithm outperforms baseline algorithms by 21.5% averagely in profit and also performs well in pure online/offline scenarios. We further demonstrate the feasibility of our algorithm through test-bed experiments in a realistic scene.