Binary Search-Based Fast Scheduling Algorithms for Reliability-Aware Energy-Efficient Task Graph Scheduling With Fault Tolerance

Among the available processor-level energy savings schemes, dynamic voltage and frequency scaling (DVFS) is very popular and effective due to its widespread cross-platform use in designing energy-efficient scheduling algorithms. However, rapid frequency switching by DVFS based algorithms while minimizing the energy consumptions may result transient failures in the system. To avoid such failures and their catastrophic consequences, energy-efficient scheduling algorithms with the capabilities to provide more reliable task schedules are always in demand. Therefore, this paper introduces two novel low complexity energy-efficient task scheduling algorithms for heterogeneous computing environments. We term the first algorithm as 'binary search-based energy-efficient scheduling with reliability goal (BSESRG)' for running parallel task graphs in heterogeneous computing systems. We show that the proposed BSESRG has the capability to reduce energy consumption, and shorten the total schedule length by meeting the reliability goals up to a certain threshold. Then, we present our second algorithm, the 'binary search-based energy-efficient fault-tolerant scheduling with reliability goal (BSESRG-FT), which ensures meeting the reliability goals with simultaneous consideration of fault tolerance. The proposed BSESRG-FT is able to reach higher reliability goals, reduce energy consumption, and shorten the total schedule length of a parallel task graph on heterogeneous platforms. We demonstrate the working of both BSESRG and BSESRG-FT through simulation experiments considering real-world task graphs, and show the supremacy of the two proposed algorithms over their respective peers (viz., ESRG and EFSRG) in terms of energy savings, schedule lengths, run times and reliability goals. The superiority of the proposed BSESRG and BSESRG-FT over their respective competitors are also validated on the real benchmark MiBench. Moreover, from the complexity analysis, we respectively find the time complexities of BSESRG and BSESRG-FT as O(|X| x |P| x log(2)|F|) and O (|X| x |P|(2 )x log(2)|F|) confirming their better computational efficiency than the respective peers.