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Dynamical Resource ...
Dynamical Resource Allocation in Edge for Trustable Internet-of-Things Systems : A Reinforcement Learning Method
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- Deng, Shuiguang (författare)
- Zhejiang University School of Medicine, CHN
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- Xiang, Zhengzhe (författare)
- Zhejiang University, CHN
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- Zhao, Peng (författare)
- Zhejiang University School of Medicine, CHN
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- Taheri, Javid (författare)
- Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013)
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- Gao, Honghao (författare)
- Computing Center Shanghai University, CHN
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- Yin, Jianwei (författare)
- Zhejiang University, CHN
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- Zomaya, Albert Y. (författare)
- University of Sydney, AUS
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2020
- 2020
- Engelska.
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Ingår i: IEEE Transactions on Industrial Informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1551-3203 .- 1941-0050. ; 16:9, s. 6103-6113
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Edge computing (EC) is now emerging as a key paradigm to handle the increasing Internet-of-Things (IoT) devices connected to the edge of the network. By using the services deployed on the service provisioning system which is made up of edge servers nearby, these IoT devices are enabled to fulfill complex tasks effectively. Nevertheless, it also brings challenges in trustworthiness management. The volatile environment will make it difficult to comply with the service-level agreement (SLA), which is an important index of trustworthiness declared by these IoT services. In this article, by denoting the trustworthiness gain with how well the SLA can comply, we first encode the state of the service provisioning system and the resource allocation scheme and model the adjustment of allocated resources for services as a Markov decision process (MDP). Based on these, we get a trained resource allocating policy with the help of the reinforcement learning (RL) method. The trained policy can always maximize the services' trustworthiness gain by generating appropriate resource allocation schemes dynamically according to the system states. By conducting a series of experiments on the YouTube request dataset, we show that the edge service provisioning system using our approach has 21.72% better performance at least compared to baselines.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Servers
- Resource management
- Task analysis
- Cloud computing
- Edge computing
- Learning (artificial intelligence)
- Internet-of-Things (IoT)
- trust management
- resource allocation
- Computer Science
- Datavetenskap
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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