Workflow Scheduling in Serverless Edge Computing for the Industrial Internet of Things: A Learning Approach
Renchao Xie, Dier Gu, Qinqin Tang, Tao Huang, Fei Richard Yu
Beijing University of Posts and Telecommunications Purple Mountain Laboratories State Key Laboratory of Networking and Switching Technology Shenzhen University
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摘要与影响
Serverless edge computing is seen as a promising enabler to execute differentiated Industrial Internet of Things (IIoT) applications without managing the underlying servers and clusters. In IIoT serverless edge computing, IIoT workflow scheduling for cloud-edge collaborative processing is closely related to the service quality of users. However, serverless functions decomposed by IIoT applications are limited in their deployment at the edge due to the resource-constrained nature of edge infrastructures. In addition, the scheduling of complex IIoT applications supported by serverless computing is more challenging. Therefore, considering the limited function deployment and the complex dependencies of serverless workflows, we model the workflow application as directed acyclic graph and formulate the scheduling problem as a multiobjective optimization problem. A dueling double deep Q-network-based solution is proposed to make scheduling decisions under dynamically changing systems. Extensive simulation experiments are conducted to validate the superiority of the proposed scheme.
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计算机 / AIIoT and Edge/Fog Computing
Cloud Computing and Resource Management · Age of Information Optimization
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