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Optimal Application Deployment in Resource Constrained Distributed Edges
Zhejiang University, CHN.
Zhejiang University, CHN.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0001-9194-010X
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0002-6101-4305
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2021 (English)In: IEEE Transactions on Mobile Computing, ISSN 1536-1233, E-ISSN 1558-0660, Vol. 20, no 5, p. 1907-1923Article in journal (Refereed) Published
Abstract [en]

The dramatically increasing of mobile applications make it convenient for users to complete complex tasks on their mobile devices. However, the latency brought by unstable wireless networks and the computation failures caused by constrained resources limit the development of mobile computing. A popular approach to solve this problem is to establish a mobile service provisioning system based on a mobile edge computing (MEC) paradigm. In the MEC paradigm, plenty of machines are placed at the edge of the network so that the performance of applications can be optimized by using the involved microservice instances deployed on them. In this paper, we explore the deployment problem of microserivce-based applications in the MEC environment and propose an approach to help to optimize the cost of application deployment with the constraints of resources and the requirement of performance. We conduct a series of experiments to evaluate the performance of our approach. The result shows that our approach can improve the average response time of mobile services.

Place, publisher, year, edition, pages
IEEE Computer Society, 2021. Vol. 20, no 5, p. 1907-1923
Keywords [en]
Servers, Urban areas, Mobile computing, Task analysis, Mobile handsets, Time factors, Cloud computing, Mobile service, distributed system, mobile edge computing, service deployment
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-83892DOI: 10.1109/TMC.2020.2970698ISI: 000637531900012Scopus ID: 2-s2.0-85103970967OAI: oai:DiVA.org:kau-83892DiVA, id: diva2:1558464
Available from: 2021-05-31 Created: 2021-05-31 Last updated: 2022-03-03Bibliographically approved

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Taheri, JavidKhoshkholghi, Mohammad Ali

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