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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Veröffentlicht in:	IEEE internet of things journal Jg. 9; H. 20; S. 19634 - 19648
Hauptverfasser:	Tang, Qinqin, Xie, Renchao, Yu, Fei Richard, Chen, Tianjiao, Zhang, Ran, Huang, Tao, Liu, Yunjie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 15.10.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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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Abstract	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.
AbstractList	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 [Formula Omitted]-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. 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.
Author	Tang, Qinqin Zhang, Ran Xie, Renchao Chen, Tianjiao Yu, Fei Richard Liu, Yunjie Huang, Tao
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SubjectTerms	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
Title	Distributed Task Scheduling in Serverless Edge Computing Networks for the Internet of Things: A Learning Approach
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