Grouping Computational Data in Resource Caches of Edge-Fog Cloud

Research output: Conference materialsPosterResearchpeer-review

Abstract

Edge-Fog clouds provide an attractive platform for bringing data processing closer to its source in a networked environment. In this paper, we extend our work on Edge-Fog clouds and to an industrial automation scenario, where we show how grouping of computational data in resource caches lowers network traffic and shortens application-experienced latency. Our preliminary results are promising and in our future work we plan to evaluate more realistic scenarios and apply the solutions in real industrial automation cases.
Original languageEnglish
Pages8:1-8:2
Number of pages2
DOIs
Publication statusPublished - 23 Apr 2017
MoE publication typeNot Eligible
EventWorkshop on CrossCloud Infrastructures and Platforms - Belgrade, Serbia
Duration: 23 Apr 201723 Apr 2017
Conference number: 4

Conference

ConferenceWorkshop on CrossCloud Infrastructures and Platforms
CountrySerbia
CityBelgrade
Period23/04/201723/04/2017

Fields of Science

  • 113 Computer and information sciences

Cite this

Mohan, N., Zhou, P., Govindaraj, K., & Kangasharju, J. (2017). Grouping Computational Data in Resource Caches of Edge-Fog Cloud. 8:1-8:2. Poster session presented at Workshop on CrossCloud Infrastructures and Platforms, Belgrade, Serbia. https://doi.org/10.1145/3069383.3069391
Mohan, Nitinder ; Zhou, Pengyuan ; Govindaraj, Keerthana ; Kangasharju, Jussi. / Grouping Computational Data in Resource Caches of Edge-Fog Cloud. Poster session presented at Workshop on CrossCloud Infrastructures and Platforms, Belgrade, Serbia.2 p.
@conference{6060b8a8c1d8469d93d022146f3dd1de,
title = "Grouping Computational Data in Resource Caches of Edge-Fog Cloud",
abstract = "Edge-Fog clouds provide an attractive platform for bringing data processing closer to its source in a networked environment. In this paper, we extend our work on Edge-Fog clouds and to an industrial automation scenario, where we show how grouping of computational data in resource caches lowers network traffic and shortens application-experienced latency. Our preliminary results are promising and in our future work we plan to evaluate more realistic scenarios and apply the solutions in real industrial automation cases.",
keywords = "113 Computer and information sciences",
author = "Nitinder Mohan and Pengyuan Zhou and Keerthana Govindaraj and Jussi Kangasharju",
note = "Volume: Proceeding volume: ; Workshop on CrossCloud Infrastructures and Platforms ; Conference date: 23-04-2017 Through 23-04-2017",
year = "2017",
month = "4",
day = "23",
doi = "10.1145/3069383.3069391",
language = "English",
pages = "8:1--8:2",

}

Mohan, N, Zhou, P, Govindaraj, K & Kangasharju, J 2017, 'Grouping Computational Data in Resource Caches of Edge-Fog Cloud' Workshop on CrossCloud Infrastructures and Platforms, Belgrade, Serbia, 23/04/2017 - 23/04/2017, pp. 8:1-8:2. https://doi.org/10.1145/3069383.3069391

Grouping Computational Data in Resource Caches of Edge-Fog Cloud. / Mohan, Nitinder; Zhou, Pengyuan; Govindaraj, Keerthana ; Kangasharju, Jussi.

2017. 8:1-8:2 Poster session presented at Workshop on CrossCloud Infrastructures and Platforms, Belgrade, Serbia.

Research output: Conference materialsPosterResearchpeer-review

TY - CONF

T1 - Grouping Computational Data in Resource Caches of Edge-Fog Cloud

AU - Mohan, Nitinder

AU - Zhou, Pengyuan

AU - Govindaraj, Keerthana

AU - Kangasharju, Jussi

N1 - Volume: Proceeding volume:

PY - 2017/4/23

Y1 - 2017/4/23

N2 - Edge-Fog clouds provide an attractive platform for bringing data processing closer to its source in a networked environment. In this paper, we extend our work on Edge-Fog clouds and to an industrial automation scenario, where we show how grouping of computational data in resource caches lowers network traffic and shortens application-experienced latency. Our preliminary results are promising and in our future work we plan to evaluate more realistic scenarios and apply the solutions in real industrial automation cases.

AB - Edge-Fog clouds provide an attractive platform for bringing data processing closer to its source in a networked environment. In this paper, we extend our work on Edge-Fog clouds and to an industrial automation scenario, where we show how grouping of computational data in resource caches lowers network traffic and shortens application-experienced latency. Our preliminary results are promising and in our future work we plan to evaluate more realistic scenarios and apply the solutions in real industrial automation cases.

KW - 113 Computer and information sciences

UR - http://doi.acm.org/10.1145/3069383.3069391

U2 - 10.1145/3069383.3069391

DO - 10.1145/3069383.3069391

M3 - Poster

SP - 8:1-8:2

ER -

Mohan N, Zhou P, Govindaraj K, Kangasharju J. Grouping Computational Data in Resource Caches of Edge-Fog Cloud. 2017. Poster session presented at Workshop on CrossCloud Infrastructures and Platforms, Belgrade, Serbia. https://doi.org/10.1145/3069383.3069391