Recent Advances in Energy Efficient Resource Management Techniques in Cloud Computing Environments

Gholipour, Niloofar; Arianyan, Ehsan; Buyya, Rajkumar

doi:10.48550/arxiv.2107.06005

Cited by 1 publication

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“…Resource allocation problem for heterogeneous systems has been widely studied with the objective of either maximizing the on-time task completion [3] or minimizing the energy consumption [7]. Nonetheless, in a usable IoT-based system, both of these objectives in addition to the fairness are desired on a resource-and energy-constraint system.…”

Section: B Problem Statementmentioning

confidence: 99%

FELARE: Fair Scheduling of Machine Learning Tasks on Heterogeneous Edge Systems

Mokhtari¹,

Hossen²,

Jamshidi³

et al. 2022

Preprint

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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) multiobjective 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.

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Section: B Problem Statementmentioning

confidence: 99%