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Smart city article details
| Title | Toward Intelligent Cooperation At The Edge: Improving The Qos Of Workflow Scheduling With The Competitive Cooperation Of Edge Servers |
|---|---|
| ID_Doc | 57704 |
| Authors | Zhu K.; Zhang Z.; Sun F. |
| Year | 2024 |
| Published | Wireless Networks, 30, 6 |
| DOI | http://dx.doi.org/10.1007/s11276-023-03361-1 |
| Abstract | Advances in big data and Internet of Things devices have brought novel service modes, such as smart cities and intelligent transportation, to daily life. With the widespread deployment of smart terminals comes an exponentially increasing amount of data, which, causes conflict due to the intensive resource demand and limited computation capacity. To manage this conflict, edge computing has been introduced as an auxiliary technique to cloud computing. However, the emerging computation-intensive service chains bring high resource demands that may exceed the computation capability of a single edge server. Simply offloading them to cloud servers is hardly time saving and is challenging for typical edge-cloud schemes. In this paper, we address the challenge of coordinating the workflow scheduler from multiple users in a partially observable environment. We first partition the workflow by leveraging graph theory to split the component tasks into clusters based on their dependency constraints. We further model the possible contention on edge servers among multiple users as a Markov game and propose a multiagent reinforcement learning-based edge server coordination algorithm named partially observable multiagent workflow scheduler (POMAWS) as the solution. With fine-trained agents, the proposed scheme can intelligently activate nearby edge nodes to form a temporal workgroup and manage contention when it occurs. The numerical results validate the feasibility of our proposed scheme, as its performance exceeds typical cloud computing and traditional clustering schemes with an improved QoS in terms of processing delay. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. |
| Author Keywords | Edge computing; Edge cooperation; Markov game; Multiagent reinforcement learning |
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