2022 · 11 citations · 21 references
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.
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Sinno Jialin Pan, Qiang Yang · IEEE Transactions on Knowledge and Data Engineering · 2009 · 22.5K citations
Karl R. Weiss, Taghi M. Khoshgoftaar, Dingding Wang · Journal Of Big Data · 2016 · 5.9K citations · Full text
Chi-Keung Luk, Robert Cohn, Robert Muth et al. · ACM SIGPLAN Notices · 2005 · 3.2K citations
Engineering, Computer Architecture, Software Engineering +16
Mobile Edge Computing: A Survey
Nasir Abbas, Yan Zhang, Amir Taherkordi et al. · IEEE Internet of Things Journal · 2017 · 2.4K citations
The Emergence of Edge Computing
Mahadev Satyanarayanan · Computer · 2017 · 2.3K citations