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14 March 2022 A learning task scheduling for dispersed computing
Sen Yuan, Geming Xia
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Proceedings Volume 12165, International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2021); 121651X (2022) https://doi.org/10.1117/12.2628177
Event: 2021 International Conference on Intelligent Traffic Systems and Smart City, 2021, Zhengzhou, China
Abstract
The increasing computing power emerged in the network drives the further explosion of data. However, the in-network computing resources are not well utilized efficiently by cloud or edge computing to process the big data. Dispersed computing, as a promising complementary paradigm, can gather all the in-network dispersed computing resources to build a near real-time, location-aware computing paradigm. Task scheduling for dispersed computing faces the problems of resource heterogeneity and dynamics. Traditional scheduling algorithms cannot be well adapted to the dispersed computing environment due to the lack of learning. In this paper, we model the task scheduling process as a Markov Decision Process (MDP) and propose a Q-Learning-based task scheduling algorithm for dispersed computing. The simulation results display the feasibility and effectiveness of the algorithm, which can effectively reduce makespan and latency compared with the baseline algorithm and can effectively sense and utilize the dispersed resources.
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Sen Yuan and Geming Xia "A learning task scheduling for dispersed computing", Proc. SPIE 12165, International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2021), 121651X (14 March 2022); https://doi.org/10.1117/12.2628177
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KEYWORDS
Clouds
Computer networks
Internet
Network architectures
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