An Estimation of Distribution Algorithm to Optimize the Utility of Task Scheduling Under Fog Computing Systems

The Internet of Things (IoT) is realized initially today. A large amount of data is produced and a range of IoT services are settled down. Based on it, a range of responsive IoT applications arise. To satisfy the quality of experience (QoE) of users, the applications are needed to be processed in a...

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Vydáno v:Fog Computing: Theory and Practice s. 371 - 384
Hlavní autoři: Wu, Chu‐ge, Wang, Ling
Médium: Kapitola
Jazyk:angličtina
Vydáno: Hoboken, NJ, USA Wiley 2020
John Wiley & Sons, Inc
Vydání:1
Témata:
evolutionary computation
fog computing
Internet of Things
scheduling
task utility
Processor scheduling
Computational modeling
Scheduling
Internet of Things
Task analysis
Optimization
Edge computing
ISBN:1119551692, 9781119551690
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Abstract The Internet of Things (IoT) is realized initially today. A large amount of data is produced and a range of IoT services are settled down. Based on it, a range of responsive IoT applications arise. To satisfy the quality of experience (QoE) of users, the applications are needed to be processed in a timely manner. Compared with traditional cloud computing systems, fog computing is one of the promising solutions to processing the huge amount of local data and decreasing the end‐to‐end latency. Different time‐dependent functions are adopted to measure the utility of different tasks and in this work, the resource allocation and task scheduling problem under the fog system is considered to maximize the sum of the utility of tasks. And an estimation of distributed algorithm to maximum the task utility (uEDA) with a repair procedure and local search is adopted to determine the task processing order and computing node allocation. The comparative results show that the performance of our algorithm exceeds significantly the heuristic method on the utility metrics.
AbstractList The Internet of Things (IoT) is realized initially today. A large amount of data is produced and a range of IoT services are settled down. Based on it, a range of responsive IoT applications arise. To satisfy the quality of experience (QoE) of users, the applications are needed to be processed in a timely manner. Compared with traditional cloud computing systems, fog computing is one of the promising solutions to processing the huge amount of local data and decreasing the end‐to‐end latency. Different time‐dependent functions are adopted to measure the utility of different tasks and in this work, the resource allocation and task scheduling problem under the fog system is considered to maximize the sum of the utility of tasks. And an estimation of distributed algorithm to maximum the task utility (uEDA) with a repair procedure and local search is adopted to determine the task processing order and computing node allocation. The comparative results show that the performance of our algorithm exceeds significantly the heuristic method on the utility metrics.
The Internet of Things (IoT) is realized initially today. A large amount of data is produced and a range of IoT services are settled down. Based on it, a range of responsive IoT applications arise. To satisfy the quality of experience (QoE) of users, the applications are needed to be processed in a timely manner. Compared with traditional cloud computing systems, fog computing is one of the promising solutions to processing the huge amount of local data and decreasing the end‐to‐end latency. Different time‐dependent functions are adopted to measure the utility of different tasks and in this work, the resource allocation and task scheduling problem under the fog system is considered to maximize the sum of the utility of tasks. And an estimation of distributed algorithm to maximum the task utility (uEDA) with a repair procedure and local search is adopted to determine the task processing order and computing node allocation. The comparative results show that the performance of our algorithm exceeds significantly the heuristic method on the utility metrics.
Author Wu, Chu‐ge
Wang, Ling
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  organization: Tsinghua University, Department of Automation
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2020 John Wiley & Sons, Inc.
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DOI 10.1002/9781119551713.ch14
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Keywords Processor scheduling
Computational modeling
Scheduling
Internet of Things
Task analysis
Optimization
Edge computing
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John Wiley & Sons, Inc
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Snippet The Internet of Things (IoT) is realized initially today. A large amount of data is produced and a range of IoT services are settled down. Based on it, a range...
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SubjectTerms evolutionary computation
fog computing
Internet of Things
scheduling
task utility
Title An Estimation of Distribution Algorithm to Optimize the Utility of Task Scheduling Under Fog Computing Systems
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