FELARE: Fair Scheduling of Machine Learning Tasks on Heterogeneous Edge Systems
FELARE: Fair Scheduling of Machine Learning Tasks on Heterogeneous Edge Systems
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DOI：
10.1109/cloud55607.2022.00069
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发表时间：
2022-05
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影响因子：
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通讯作者：
Ali Mokhtari;Pooyan Jamshidi;M. Salehi
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作者：
Ali Mokhtari;Pooyan Jamshidi;M. Salehi
边缘计算通过并发和连续执行延迟敏感的机器学习（ML）应用程序来实现基于IoT的智能系统。这些基于边缘的机器学习系统通常是电池供电的（即，能量有限）。它们使用具有不同计算性能的异构资源（例如，CPU、GPU和/或FPGA）来满足ML应用的延迟约束。面临的挑战是在异构边缘计算系统（HEC）上针对这些系统的能量和延迟约束为不同的ML应用分配用户请求。为此，我们研究和分析的资源分配解决方案，可以提高按时任务完成率，同时考虑能源约束。重要的是，我们研究了边缘友好（轻量级）多目标映射算法，这些算法不会偏向于特定的应用程序类型来实现目标;相反，算法在映射决策中考虑并发ML应用程序的“公平性”。性能评估表明，所提出的启发式算法在延迟和能量目标方面优于异构系统中广泛使用的启发式算法，特别是在低到中等的请求到达率下。我们观察到按时任务完成率提高了8.9%，节能率提高了12.6%，而没有对边缘系统施加任何重大开销。
Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. These edge-based machine learning systems are often battery-powered (i.e., energy-limited). They use heterogeneous resources with diverse computing performance (e.g., CPU, GPU, and/or FPGA) to fulfill the latency constraints of ML applications. The challenge is to allocate user requests for different ML applications on the Heterogeneous Edge Computing Systems (HEC) with respect to both the energy and latency constraints of these systems. To this end, we study and analyze resource allocation solutions that can increase the on-time task completion rate while considering the energy constraint. Importantly, we investigate edge-friendly (lightweight) multi-objective mapping heuristics that do not become biased toward a particular application type to achieve the objectives; instead, the heuristics consider "fairness" across the concurrent ML applications in their mapping decisions. Performance evaluations demonstrate that the proposed heuristic outperforms widely-used heuristics in heterogeneous systems in terms of the latency and energy objectives, particularly, at low to moderate request arrival rates. We observed 8.9% improvement in on-time task completion rate and 12.6% in energy-saving without imposing any significant overhead on the edge system.