Distributed Task Scheduling in Serverless Edge Computing Networks for the Internet of Things: A Learning Approach

By delegating the infrastructure management, such as provisioning or scaling to third-party providers, serverless edge computing has recently been widely adopted in several applications, especially Internet of Things (IoT) applications. Task scheduling is a critical issue in serverless edge computin...

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Vydáno v:IEEE internet of things journal Ročník 9; číslo 20; s. 19634 - 19648
Hlavní autoři: Tang, Qinqin, Xie, Renchao, Yu, Fei Richard, Chen, Tianjiao, Zhang, Ran, Huang, Tao, Liu, Yunjie
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 15.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Cloud computing
Computational modeling
Deep reinforcement learning (DRL)
distributed task scheduling
Edge computing
Internet of Things
Internet of Things (IoT)
Job shop scheduling
Multiagent systems
Nodes
Optimization
Processor scheduling
Provisioning
Resource allocation
Resource scheduling
Scheduling
Serverless computing
serverless edge computing
stochastic game
Task analysis
Task scheduling
User experience
ISSN:2327-4662, 2327-4662
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Shrnutí:By delegating the infrastructure management, such as provisioning or scaling to third-party providers, serverless edge computing has recently been widely adopted in several applications, especially Internet of Things (IoT) applications. Task scheduling is a critical issue in serverless edge computing as it significantly impacts the quality of user experience. In contrast to the centralized scheduling in the cloud center, serverless edge task scheduling is more challenging due to the heterogeneous and resource-constrained nature of edge resources. This article aims to study the distributed task scheduling for the IoT in serverless edge computing networks, in which heterogeneous serverless edge computing nodes are rational individuals with interests to optimize their own scheduling utility while the nodes only have access to local observations. The task scheduling competition process is formulated as a partially observable stochastic game (POSG) to enable serverless edge computing nodes to noncooperatively schedule tasks and allocate computing resources depending on their locally observed system state, which takes into account the associated task generation state, data queue state, communication channel state, and previous computing resource allocation state. To solve the proposed POSG and deal with the partial observability, a multiagent task scheduling algorithm based on the dueling double deep recurrent <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula>-network (D3RQN) method is developed to approximate the optimal task scheduling and resource allocation solution. Finally, extensive simulation experiments are conducted to validate the effectiveness and superiority of the proposed scheme.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2022.3167417