Task Allocation Mechanism of Power Internet of Things Based on Cooperative Edge Computing

Wang, Qianjun; Shao, Sujie; Guo, Shaoyong; Qiu, Xuesong; Wang, Zhili

doi:10.1109/access.2020.3020233

Cited by 12 publications

(7 citation statements)

References 35 publications

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“…When the number of users is large, the bandwidth resources that can be allocated are small. Furthermore, we select Benchmark, TCS-IPSO [7] and JOATDEC [8] for comparison. We specify that the number of users is 10, and the edge servers are 10, 20, 30 and 40 respectively.…”

Section: B Experiments Resultsmentioning

confidence: 99%

Delay-sensitive Task offloading combined with Bandwidth Allocation in Multi-access Edge Computing

Song

Zhu

et al. 2022

2022 IEEE 47th Conference on Local Computer Networks (LCN)

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In recent years, multi-access edge computing (MEC) has become a hot topic. In this paper, we study a task offloading problem combined with bandwidth allocation in multi-access edge computing. Based on alliance game, we formulate bandwidth allocation to minimize the dissatisfaction of alliances. Then, we formulate the task offloading decision-making to minimize the delay. The delay consists of communication delay and execution delay. To solve the offloading problem, we convert the dissatisfaction of alliance into a vector, and obtain the Pareto optimal through multi-objective particle swarm algorithm. Then, we use Branch and Bound method to construct the propagation tree to facilitate decision-making. To evaluate the edge servers in the tree, we build an evaluation matrix and transform the matrix to a set of evaluation index which is used on task offloading decision-making. A large number of experimental results show that our algorithm is better than compared algorithm.

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Section: B Experiments Resultsmentioning

confidence: 99%

Delay-sensitive Task offloading combined with Bandwidth Allocation in Multi-access Edge Computing

Song

Zhu

et al. 2022

2022 IEEE 47th Conference on Local Computer Networks (LCN)

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“…Optimization (RQ2) Applied Network Type (RQ3) Distributed task allocation [18] FL WHAB [22] OFB VANETs [24] GT, RL MEC [29] MVAA MEC [45] PSO IoT [50] GT IoT [55] Hybrid MEC [59] CW Blockchain [61] BSUB MEC [63] FL MEC [31] DLA IoT [33] RL EC [34] CFG C-RAN [35] AsP IoT [36] GSO MEC, Fog [65] AsP NDN-IoT [66] AB, GA Wireless, cellular, satellite [67] Heuristic MEC [12] PFA EC [77] MINLP EC [17] ACO IoT [27] Heuristic IoT…”

Section: Proposed Task Allocationmentioning

confidence: 99%

Task Allocation Methods and Optimization Techniques in Edge Computing: A Systematic Review of the Literature

Patsias,

Amanatidis,

Karampatzakis

et al. 2023

Future Internet

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Task allocation in edge computing refers to the process of distributing tasks among the various nodes in an edge computing network. The main challenges in task allocation include determining the optimal location for each task based on the requirements such as processing power, storage, and network bandwidth, and adapting to the dynamic nature of the network. Different approaches for task allocation include centralized, decentralized, hybrid, and machine learning algorithms. Each approach has its strengths and weaknesses and the choice of approach will depend on the specific requirements of the application. In more detail, the selection of the most optimal task allocation methods depends on the edge computing architecture and configuration type, like mobile edge computing (MEC), cloud-edge, fog computing, peer-to-peer edge computing, etc. Thus, task allocation in edge computing is a complex, diverse, and challenging problem that requires a balance of trade-offs between multiple conflicting objectives such as energy efficiency, data privacy, security, latency, and quality of service (QoS). Recently, an increased number of research studies have emerged regarding the performance evaluation and optimization of task allocation on edge devices. While several survey articles have described the current state-of-the-art task allocation methods, this work focuses on comparing and contrasting different task allocation methods, optimization algorithms, as well as the network types that are most frequently used in edge computing systems.

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“…In the second stage of assisted driving service, the RSU process and analyze the data collected by vehicles, and then generate the calculation task T k . Both T m and T k are computing-intensive tasks, so we propose two different computing modes to perform T m and T k : local computing mode and collaborative computing mode [22].…”

Section: Computational Modelmentioning

confidence: 99%

A Trusted Edge Resource Allocation Framework for Internet of Vehicles

Zhong,

Xu,

Liao

et al. 2023

CMC

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With the continuous progress of information technique, assisted driving technology has become an effective technique to avoid traffic accidents. Due to the complex road conditions and the threat of vehicle information being attacked and tampered with, it is difficult to ensure information security. This paper uses blockchain to ensure the safety of driving information and introduces mobile edge computing technology to monitor vehicle information and road condition information in real time, calculate the appropriate speed, and plan a reasonable driving route for the driver. To solve these problems, this paper proposes a trusted edge resource allocation framework for assisted driving service, which includes two stages: the blockchain generation stage (the first stage) and assisted driving service stage (the second stage). Furthermore, in the first stage, a delay-and-throughput-oriented block generation model for the mobile terminal is designed. In the second stage, a balanced offloading algorithm for assisted driving service based on edge collaboration is proposed to solve the problems of unbalanced load of cluster mobile edge computing (MEC) servers and low resource utilization of the system. And this paper optimizes the throughput of blockchain and delay of the transportation network through deep reinforcement learning (DRL) algorithm. Finally, compared with joint computation and communication resources' allocation (JCCR) and resource allocation method based on binary offloading (RAB), our proposed scheme can optimize the delay by 7.4% and 26.7%, and support various application services of the vehicular networks more effectively.

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Task Allocation Mechanism of Power Internet of Things Based on Cooperative Edge Computing

Cited by 12 publications

References 35 publications

Delay-sensitive Task offloading combined with Bandwidth Allocation in Multi-access Edge Computing

Delay-sensitive Task offloading combined with Bandwidth Allocation in Multi-access Edge Computing

Task Allocation Methods and Optimization Techniques in Edge Computing: A Systematic Review of the Literature

A Trusted Edge Resource Allocation Framework for Internet of Vehicles

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