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A model for distributed in-network and near-edge computing with heterogeneous hardware
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Cooke, Ryan A. and Fahmy, Suhaib A. (2020) A model for distributed in-network and near-edge computing with heterogeneous hardware. Future Generation Computer Systems, 105 . pp. 395-409. doi:10.1016/j.future.2019.11.040 ISSN 0167-739X.
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Official URL: https://doi.org/10.1016/j.future.2019.11.040
Abstract
Applications that involve analysis of data from distributed networked data sources typically involve computation performed centrally in a datacenter or cloud environment, with some minor pre-processing potentially performed at the data sources. As these applications grow in scale, this centralized approach leads to potentially impractical bandwidth requirements and computational latencies. This has led to interest in edge computing, where processing is moved nearer to the data sources, and recently, in-network computing, where processing is done as data progresses through the network. This paper presents a model for reasoning about distributed computing at the edge and in the network, with support for heterogeneous hardware and alternative software and hardware accelerator implementations. Unlike previous distributed computing models, it considers the cost of computation for compute-intensive applications, supports a variety of hardware platforms, and considers a heterogeneous network. The model is flexible and easily extensible for a range of applications and scales, and considers a variety of metrics. We use the model to explore the key factors that influence where computational capability should be placed and what platforms should be considered for distributed applications.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | |||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||
| Library of Congress Subject Headings (LCSH): | Electronic data processing -- Distributed processing, Neural networks (Computer science), Cloud computing, Electronic circuit design -- Data processing, Computer algorithms | |||||||||
| Journal or Publication Title: | Future Generation Computer Systems | |||||||||
| Publisher: | Elsevier Science BV | |||||||||
| ISSN: | 0167-739X | |||||||||
| Official Date: | April 2020 | |||||||||
| Dates: |
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| Volume: | 105 | |||||||||
| Page Range: | pp. 395-409 | |||||||||
| DOI: | 10.1016/j.future.2019.11.040 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| Date of first compliant deposit: | 23 December 2019 | |||||||||
| Date of first compliant Open Access: | 13 December 2020 | |||||||||
| RIOXX Funder/Project Grant: |
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