Edge Workload Trace Gathering and Analysis for Benchmarking

Klervie Tocze, Norbert Schmitt, Ulf Kargen, Atakan Aral, Ivona Brandic

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

The emerging field of edge computing is suffering from a lack of representative data to evaluate rapidly introduced new algorithms or techniques. That is a critical issue as this complex paradigm has numerous different use cases which translate into a highly diverse set of workload types.In this work, within the context of the edge computing activity of SPEC RG Cloud, we continue working towards an edge benchmark by defining high-level workload classes as well as collecting and analyzing traces for three real-world edge applications, which, according to the existing literature, are the representatives of those classes. Moreover, we propose a practical and generic methodology for workload definition and gathering. The traces and gathering tool are provided open-source.In the analysis of the collected workloads, we detect discrepancies between the literature and the traces obtained, thus highlighting the need for a continuing effort into gathering and providing data from real applications, which can be done using the proposed trace gathering methodology. Additionally, we discuss various insights and future directions that rise to the surface through our analysis.
Original languageEnglish
Title of host publicationProceedings - 6th IEEE International Conference on Fog and Edge Computing, ICFEC 2022
EditorsL. Mashayekhy, S. Schulte, V. Cardellini, B. Kantarci, Y. Simmhan, B. Varghese
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages34-41
ISBN (Electronic)9781665495240
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event6th IEEE International Conference on Fog and Edge Computing, ICFEC 2022 - Taormina, Italy
Duration: 18 May 202219 May 2022

Conference

Conference6th IEEE International Conference on Fog and Edge Computing, ICFEC 2022
Country/TerritoryItaly
CityTaormina
Period18/05/2219/05/22

Funding

Klervie Toczé is supported by the Swedish national graduate school in computer science (CUGS). Atakan Aral and Ivona Brandić are supported by the CHIST-ERA grant CHIST-ERA-19-CES-005, and by the Austrian Science Fund (FWF): projects Y904-N31 (RUCON) and I5201-N (SWAIN).

FundersFunder number
Austrian Science FundY904-N31, I5201-N
National Graduate School in Computer ScienceCHIST-ERA-19-CES-005

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