Towards edge benchmarking: A methodology for characterizing edge workloads

Klervie Tocze; Norbert Schmitt; Ivona Brandic; Atakan Aral; Simin Nadjm-Tehrani

doi:10.1109/FAS-W.2019.00030

Towards edge benchmarking: A methodology for characterizing edge workloads

Klervie Tocze, Norbert Schmitt, Ivona Brandic, Atakan Aral, Simin Nadjm-Tehrani

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

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.

Original language	English
Title of host publication	Proceedings - 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	70-71
ISBN (Electronic)	9781728124063
DOIs	https://doi.org/10.1109/FAS-W.2019.00030
Publication status	Published - 1 Jun 2019
Externally published	Yes
Event	4th IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019 - Umea, Sweden Duration: 16 Jun 2019 → 20 Jun 2019

Conference

Conference	4th IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019
Country/Territory	Sweden
City	Umea
Period	16/06/19 → 20/06/19

Funding

Klervie Toczé is supported by the Swedish national graduate school in computer science (CUGS).

Funders	Funder number
National Graduate School in Computer Science

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10.1109/FAS-W.2019.00030

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Tocze, K., Schmitt, N., Brandic, I., Aral, A., & Nadjm-Tehrani, S. (2019). Towards edge benchmarking: A methodology for characterizing edge workloads. In Proceedings - 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019 (pp. 70-71). Article 8792014 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FAS-W.2019.00030

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author = "Klervie Tocze and Norbert Schmitt and Ivona Brandic and Atakan Aral and Simin Nadjm-Tehrani",

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doi = "10.1109/FAS-W.2019.00030",

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Tocze, K, Schmitt, N, Brandic, I, Aral, A & Nadjm-Tehrani, S 2019, Towards edge benchmarking: A methodology for characterizing edge workloads. in Proceedings - 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019., 8792014, Institute of Electrical and Electronics Engineers Inc., pp. 70-71, 4th IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019, Umea, Sweden, 16/06/19. https://doi.org/10.1109/FAS-W.2019.00030

Towards edge benchmarking: A methodology for characterizing edge workloads. / Tocze, Klervie; Schmitt, Norbert; Brandic, Ivona et al.
Proceedings - 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 70-71 8792014.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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Towards edge benchmarking: A methodology for characterizing edge workloads

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