A4 Refereed article in a conference publication
Dynamic Computation Migration at the Edge: Is There an Optimal Choice?
Authors: Sina Shahhosseini, Iman Azimi, Arman Anzanpour, Axel Jantsch, Pasi Liljeberg, Nikil Dutt, Amir M. Rahmani
Editors: Houman Homayoun, Baris Taskin
Conference name: Great Lakes Symposium on VLSI
Publisher: Association for Computing Machinery
Publishing place: New York, NY
Publication year: 2019
Book title : GLSVLSI '19: Proceedings of the 2019 on Great Lakes Symposium on VLSI
Journal acronym: PR GR LAK SYMP VLSI
First page : 519
Last page: 524
Number of pages: 6
ISBN: 978-1-4503-6252-8
ISSN: 1066-1395
DOI: https://doi.org/10.1145/3299874.3319336(external)
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/41671541(external)
In the era of Fog computing where one can decide to compute certain time-critical tasks at the edge of the network, designers often encounter a question whether the sensor layer provides the optimal response time for a service, or the Fog layer, or their combination. In this context, minimizing the total response time using computation migration is a communication-computation co-optimization problem as the response time does not depend only on the computational capacity of each side. In this paper, we aim at investigating this question and addressing it in certain situations. We formulate this question as a static or dynamic computation migration problem depending on whether certain communication and computation characteristics of the underlying system is known at design-time or not. We first propose a static approach to find the optimal computation migration strategy using models known at design-time. We then make a more realistic assumption that several sources of variation can affect the system's response latency (e.g., the change in computation time, bandwidth, transmission channel reliability, etc.), and propose a dynamic computation migration approach which can adaptively identify the latency optimal computation layer at runtime. We evaluate our solution using a case-study of artificial neural network based arrhythmia classification using a simulation environment as well as a real test-bed.
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