Towards Edge Benchmarking: A Methodology for Characterizing Edge Workloads

Toczé, Klervie; Schmitt, Norbert; Brandić, Ivona; Aral, Atakan; Nadjm‐Tehrani, Simin

doi:10.1109/fas-w.2019.00030

Cited by 7 publications

(9 citation statements)

References 4 publications

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“…Current edge benchmarking efforts, e.g. that by McChesney et al [17] and Toczé et al [18], have application characteristics described in broad terms such as "bandwidth/computational intensive" or "close/far". This is not detailed enough to be used in simulations or analyzes that provide deeper insights.…”

Section: Edge Workload Characterization and Modelingmentioning

confidence: 99%

Characterization and modeling of an edge computing mixed reality workload

2020

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The edge computing paradigm comes with a promise of lower application latency compared to the cloud. Moreover, offloading user device computations to the edge enables running demanding applications on resource-constrained mobile end devices. However, there is a lack of workload models specific to edge offloading using applications as their basis. In this work, we build upon the reconfigurable open-source mixed reality (MR) framework MR-Leo as a vehicle to study resource utilisation and quality of service for a time-critical mobile application that would have to rely on the edge to be widely deployed. We perform experiments to aid estimating the resource footprint and the generated load by MR-Leo, and propose an application model and a statistical workload model for it. The idea is that such empirically-driven models can be the basis of evaluations of edge algorithms within simulation or analytical studies. A comparison with a workload model used in a recent work shows that the computational demand of MR-Leo exhibits very different characteristics from those assumed for MR applications earlier.

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Section: Edge Workload Characterization and Modelingmentioning

confidence: 99%

Characterization and modeling of an edge computing mixed reality workload

2020

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“…With the long term goal of creating a benchmark for edge computing algorithms and techniques, the SPEC group has been recently looking deeper into different use cases (mixed reality, vehicular data analytics, and code offloading) in order to characterize their workloads [111]. The thesis work with mixed reality is a part of this effort to look deeper at specific edge applications.…”

Section: Edge Applicationsmentioning

confidence: 99%

“…Apart from the upload/download data size for the MR application that are derived based on a chosen image/text metadata size, the other numerical data is not justified. Similarly, applications considered for the benchmarking effort by McChesney et al [117] and Toczé et al [111], have their characteristics described in broad terms such as "bandwidth/computational intensive" or "close/far". Mukherjee et al [58] summarize different mathematical models used for capturing e.g.…”

Section: Area Applications Articlesmentioning

confidence: 99%

“…When comparing the deployment of applications using different modes, McChesney et al [117] use six applications that are characterized with one or several of the following type tags: latency critical, bandwidth intensive, location aware and computational intensive. Finally, the load indicators considered by Toczé et al [111] in the perspective of benchmarking edge algorithms and techniques are the resource demand (relative to communication, computation and storage), the deadline, the arrival type (to be understood as arrival pattern) and the interrarrival time.…”

Section: Modeling An Edge Mixed Reality Workloadmentioning

confidence: 99%

“…Characterizing the computation demand is not a straightforward task. Based on the literature study performed in Section 6.1.1, the computation demand is sometimes not quantified but described in generic categories such as "High" or "Low" [117,111]. When it is quantified, the metric chosen varies and the numbers are provided without explaining how they were obtained.…”

Section: Computation Demandmentioning

confidence: 99%

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