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Toczé, K. (2020). Latency-aware Resource Management at the Edge. (Licentiate dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Latency-aware Resource Management at the Edge
2020 (English)Licentiate thesis, monograph (Other academic)
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2020
Series
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1871
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-163388 (URN)978-91-7929-904-0 (ISBN)
Presentation
2020-03-24, Alan Turing, Hus B, Campus Valla, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2020-02-19 Created: 2020-02-03 Last updated: 2020-02-19Bibliographically approved
Toczé, K., Schmitt, N., Brandic, I., Aral, A. & Nadjm-Tehrani, S. (2019). Towards Edge Benchmarking: A Methodology for Characterizing Edge Workloads. In: In Proceedings of 4th International Workshop on Foundations and Applications of Self* Systems (FAS*W),: . Paper presented at 4th International Workshop on Foundations and Applications of Self* Systems (FAS*W), Umea, Sweden, 16-20 June 2019. IEEE
Open this publication in new window or tab >>Towards Edge Benchmarking: A Methodology for Characterizing Edge Workloads
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2019 (English)In: In Proceedings of 4th International Workshop on Foundations and Applications of Self* Systems (FAS*W),, IEEE, 2019Conference paper, Published paper (Refereed)
Abstract [en]

The edge computing paradigm has recently attracted research efforts coming from different application domains. However, evaluating an edge platform or algorithm is impeded by the lack of suitable benchmarks. We propose a methodology for characterizing edge workloads from different application domains. It is a first step towards defining workloads to be included in a future edge benchmarking suite. We evaluate the methodology on three use cases and find that defining a common and standard set of workloads is plausible.

Place, publisher, year, edition, pages
IEEE, 2019
National Category
Computer Systems
Identifiers
urn:nbn:se:liu:diva-161760 (URN)10.1109/FAS-W.2019.00030 (DOI)2-s2.0-85071474510 (Scopus ID)
Conference
4th International Workshop on Foundations and Applications of Self* Systems (FAS*W), Umea, Sweden, 16-20 June 2019
Available from: 2019-11-08 Created: 2019-11-08 Last updated: 2019-11-18Bibliographically approved
Toczé, K. & Nadjm-Tehrani, S. (2018). A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing. Wireless Communications & Mobile Computing, Article ID 7476201.
Open this publication in new window or tab >>A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
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
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-149767 (URN)10.1155/2018/7476201 (DOI)000435850600001 ()
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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7300-3603

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