Resource Allocation With Edge Computing in IoT Networks via Machine Learning

Liu, Xiaolan; Yu, Jiadong; Wang, Jian; Gao, Yue

doi:10.1109/jiot.2020.2970110

Cited by 123 publications

(46 citation statements)

References 19 publications

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“…In the same fashion, Edge sites can be grouped for different task resource demands [232] and Edge servers can be grouped using an analysis of the allocated computing resources [233]. Unmanned aerial vehicles are also clustered to enable efficient multi-modal and multi-task offloading [234] and IoT users according to their priorities [235]. The dependencies between tasks can be represented by a graph and, by following a fuzzy clustering [236], makespan (i.e., the time difference between the start and finish of tasks), monetary and energy costs can be minimized.…”

Section: Machine Learningmentioning

confidence: 99%

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

et al. 2021

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Next generation communication networks are expected to accommodate a high number of new and resource-voracious applications that can be offered to a large range of end users. Even though end devices are becoming more powerful, the available local resources cannot cope with the requirements of these applications. This has created a new challenge called task offloading, where computation intensive tasks need to be offloaded to more resource powerful remote devices. Naturally, the Cloud Computing is a well-tested infrastructure that can facilitate the task offloading. However, Cloud Computing as a centralized and distant infrastructure creates significant communication delays that cannot satisfy the requirements of the emerging delay-sensitive applications. To this end, the concept of Edge Computing has been proposed, where the Cloud Computing capabilities are repositioned closer to the end devices at the edge of the network. This paper provides a detailed survey of how the Edge and/or Cloud can be combined together to facilitate the task offloading problem. Particular emphasis is given on the mathematical, artificial intelligence and control theory optimization approaches that can be used to satisfy the various objectives, constraints and dynamic conditions of this end-to-end application execution approach. The survey concludes with identifying open challenges and future directions of the problem at hand.

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Section: Machine Learningmentioning

confidence: 99%

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

et al. 2021

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“…The approach has two objectives, i.e., (i) to satisfy the application's latency requirements and (ii) to optimize the utilization of the node's available resources. Liu et al [30] propose a task offloading technique that aims to minimize the system cost, i.e., energy and latency. This technique groups the users into clusters based on their priorities and decided if a cluster should run all its tasks locally or should be offloaded to an edge server.…”

Section: Related Workmentioning

confidence: 99%

EdgeFlow—Developing and Deploying Latency-Sensitive IoT Edge Applications

Avasalcai

Zarrin

Dustdar

2022

IEEE Internet Things J.

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“…Referred publications Markov decision process [12,23,24,37,64,70,75,84,96,100,101,104,127,130,133,138,144,153,165,167,170,177,188,191,199], [203, 207, 211, 212, 214, 217, 220, 231, 252, 256-259, 263, 264, 272, 274, 281, 291, 309, 313, 320, 340, 343, 346], [369][370][371][372][373][374][375][376] Multiarmed bandit [61,66,102,198,351,377,378] Dynamic programming [16,19,27,52,68,70,84,90,93,…”

Section: Approachmentioning

confidence: 99%

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

2021

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Reinforcement learning (RL) methods can successfully solve complex optimization problems. Our article gives a systematic overview of major types of RL methods, their applications at the field of Industry 4.0 solutions, and it provides methodological guidelines to determine the right approach that can be fitted better to the different problems, and moreover, it can be a point of reference for R&D projects and further researches.

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Resource Allocation With Edge Computing in IoT Networks via Machine Learning

Cited by 123 publications

References 19 publications

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

EdgeFlow—Developing and Deploying Latency-Sensitive IoT Edge Applications

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

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