Energy-effective IoT Services in Balanced Edge-Cloud Collaboration Systems

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Xiang, ZhengzheZhejiang University City College, China (författare)

Energy-effective IoT Services in Balanced Edge-Cloud Collaboration Systems

Artikel/kapitelEngelska2021

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Institute of Electrical and Electronics Engineers Inc.2021
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LIBRIS-ID:oai:DiVA.org:kau-89035
https://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-89035URI
https://doi.org/10.1109/ICWS53863.2021.00040DOI

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Sammanfattning på:engelska

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The rapid development of the Internet-of-Things (IoT) makes it convenient to sense and collect real-world information with different kinds of widely distributed sensors. With plenty of web services providing diverse functions on the cloud, the collected information can be sufficiently used to complete complex tasks after being uploaded. However, the latency brought by long-distance communication and network congestion limits the development of IoT platforms. A feasible approach to solve this problem is to establish an edge-cloud collaboration (ECC) system based on the multi-access edge computing (MEC) paradigm where the collected information can be refined with the services deployed on nearby edge servers. However, as the edge servers are resource-limited, we should be more careful in allocating the edge resource to services, as well as designing the traffic scheduling strategy. In this paper, we investigated the edge-cloud cooperation mechanism of service provisioning in ECC systems, and to that end, proposed an energy-consumption model for it; we also proposed a performance model and balancing model to quantify the running state of ECC systems. Based on these, we further formulated the energy-effective ECC system optimization problem as a joint optimization problem whose decision variables are the resource allocation strategy and traffic scheduling strategy. With the convexity of this problem proved, we proposed an algorithm to solve it and conducted a series of experiments to evaluate its performance. The results showed that our approach can improve at least 4.3 % of the performance compared with representative baselines.

Ämnesord och genrebeteckningar

NATURVETENSKAP Data- och informationsvetenskap hsv//swe
NATURAL SCIENCES Computer and Information Sciences hsv//eng
Green Computing
Internet-of-Things
Multi-access Edge Computing
Service Management
Edge computing
Energy utilization
Optimization
Scheduling
Web services
Collaboration systems
Edge clouds
Edge server
Energy
Multiaccess
Scheduling strategies
Traffic scheduling
Internet of things
Computer Science
Datavetenskap

Biuppslag (personer, institutioner, konferenser, titlar ...)

Deng, ShuiguangCollege of Computer Science and Technology, Zhejiang University, China (författare)
Zheng, YuhangSchool of Computer and Computing Science, Zhejiang University City College, China (författare)
Wang, DongjingHangzhou Dianzi University, China (författare)
Taheri, JavidKarlstads universitet,Institutionen för matematik och datavetenskap (from 2013)(Swepub:kau)javitahe (författare)
Zheng, ZengweiSchool of Computer and Computing Science, Zhejiang University City College, China (författare)
Zhejiang University City College, ChinaCollege of Computer Science and Technology, Zhejiang University, China (creator_code:org_t)

Sammanhörande titlar

Ingår i:2021 IEEE International Conference on Web Services (ICWS): Institute of Electrical and Electronics Engineers Inc., s. 219-2299781665416818

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2021 IEEE International Conference on Web Services (ICWS) (Sök värdpublikationen i LIBRIS)

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Av författaren/redakt...: Xiang, Zhengzhe; Deng, Shuiguang; Zheng, Yuhang; Wang, Dongjing; Taheri, Javid; Zheng, Zengwei

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NATURVETENSKAP: NATURVETENSKAP; och Data och informa ...

Artiklar i publikationen: 2021 IEEE In ...

Av lärosätet: Karlstads universitet

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