Task Allocation for Energy Optimization in Fog Computing Networks with Latency Constraints

Bartosz Kopras; Bartosz Bossy; Filip Idzikowski; Paweł Kryszkiewicz; Hanna Bogucka

doi:10.1109/TCOMM.2022.3216645

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Title

Task Allocation for Energy Optimization in Fog Computing Networks with Latency Constraints

Authors

Bartosz Kopras (WIiT) ^{[ 1 ][ 2.3 ][ SzD ]}
Bartosz Bossy (WIiT) ^{[ 2 ][ 2.3 ][ P ]}
Filip Idzikowski (WIiT) ^{[ 2 ][ 2.3 ][ P ]}
Paweł Kryszkiewicz (WIiT) ^{[ 2 ][ 2.3 ][ P ]}
Hanna Bogucka (WIiT) ^{[ 2 ][ 2.3 ][ P ]}

^{[ 1 ]} Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | ^{[ 2 ]} Instytut Radiokomunikacji, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | ^{[ SzD ]} doctoral school student | ^{[ P ]} employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2022

Published in

IEEE Transactions on Communications

Journal year: 2022 | Journal volume: vol. 70 | Journal number: iss. 12

Article type

scientific article

Publication language

english

Keywords

EN

Fog network
energy-efficiency
latency
cloud
edge computing

Abstract

EN Fog networks offer computing resources of varying capacities at different distances from end users. A Fog Node (FN) closer to the network edge may have less powerful computing resources compared to the cloud, but the processing of com- putational tasks in FN limits long-distance transmission. How should the tasks be distributed between fog and cloud nodes? We formulate a universal non-convex Mixed-Integer Nonlinear Programming (MINLP) problem minimizing task transmission- and processing-related energy with delay constraints to answer this question. It is transformed with Successive Convex Approximation (SCA) and decomposed using the primal and dual decomposition techniques. Two practical algorithms called Energy-EFFicient Resource Allocation (EEFFRA) and Low- Complexity (LC)-EEFFRA are proposed and their effectiveness is tested for various network and traffic scenarios. Using EEFFRA/LC-EEFFRA can significantly decrease the number of computational requests with unmet delay requirements when compared with baseline solutions (from 48% to 24% for 10 MB requests). Utilizing Dynamic Voltage and Frequency Scaling (DVFS) minimizes energy consumption (by one-third) while satisfying delay requirements.

Pages (from - to)

8229 - 8243

DOI

10.1109/TCOMM.2022.3216645

URL

https://ieeexplore.ieee.org/document/9927444

License type

CC BY (attribution alone)

Open Access Mode

czasopismo hybrydowe

Open Access Text Version

final published version