2019 Fiscal Year Annual Research Report
Cotask-Aware Offloading and Scheduling in Mobile-Edge Computing Systems
| Project/Area Number |
19K21539
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| Allocation Type | Multi-year Fund |
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| Research Institution | National Institute of Informatics |
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Principal Investigator |
江 易翰 国立情報学研究所, アーキテクチャ科学研究系, 特任助教 (10824196)
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| Project Period (FY) |
2019-04-01 – 2020-03-31
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| Keywords | Mobile Edge Computing / Task Offloading / Delay Sensitivities / Actor-Critic Method / Reinforcement Learning |
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| Outline of Annual Research Achievements |
This research investigates the problem of task offloading in mobile edge computing systems while considering the diverse delay sensitivities of tasks. The applicant proposed an actor-critic based deep reinforcement learning (ADRL) model to minimize the total penalty that is attributed to the deadline misses of deadline-constrained tasks and the lateness of delay-sensitive tasks. The ADRL model was trained by a real data set, where the diverse delay sensitivities of tasks can be observed. The simulation results show that the ADRL model outperforms several heuristic algorithms in terms of total penalty. Even if the system is heavily loaded or it does not have a lot of computing power at the edge, the ADRL model can still reach low total penalty as it can learn well from the environment.
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