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A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7300-3603
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-1485-0802
2018 (English)In: Wireless Communications & Mobile Computing, ISSN 1530-8669, E-ISSN 1530-8677, article id 7476201Article, review/survey (Refereed) Published
Abstract [en]

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices at the edge of the current network. To achieve higher performance in this new paradigm, one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis are used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource and less extensive towards the estimation, discovery, and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of nonfunctional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.

Place, publisher, year, edition, pages
WILEY-HINDAWI , 2018. article id 7476201
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:liu:diva-149767DOI: 10.1155/2018/7476201ISI: 000435850600001OAI: oai:DiVA.org:liu-149767DiVA, id: diva2:1234325
Note

Funding Agencies|Swedish National Graduate School in Computer Science (CUGS)

Available from: 2018-07-24 Created: 2018-07-24 Last updated: 2018-08-14

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