Abstract
Grid systems are large-scale platforms which consume a considerable amount of energy. Several efficient resource/power management strategies were proposed by the specialized literature. However, most of the proposed strategies are rule-based policies which do not exploit workload patterns. Deploying the same set of rules on systems using different usage patterns, and platform settings, may lead to a sub-optimized setup. Due to the complex nature of grid systems, tailoring such a system-specific policy is not a straightforward task. In this paper, we explore a Deep Reinforcement Learning (DRL) method to build an adaptive energy-aware scheduling policy. We trained our algorithm using real workload traces from Grid’5000 platform. Our experiments pointed out an energy setup saving up to 7%, as well as average requests waiting time reduction of 27%. Finally, the resuslts clarify the importance of explore the workload to build system-specific policies.
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Notes
- 1.
Information available on https://github.com/lccasagrande/GridGym.
- 2.
Information available on https://www.grid5000.fr/w/Hardware.
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Acknowledgements
This study was supported by FAPESC, UDESC, and LabP2D. Experiments were carried out on the GRID’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations.
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Casagrande, L.C., Koslovski, G.P., Miers, C.C., Pillon, M.A. (2020). DeepScheduling: Grid Computing Job Scheduler Based on Deep Reinforcement Learning. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Advanced Information Networking and Applications. AINA 2020. Advances in Intelligent Systems and Computing, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-44041-1_89
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