Genetic algorithm for delay efficient computation offloading in dispersed computing

Liu, Hui; Niu, Zhaocheng; Du, Ji-Xiang; Lin, Xiao‐Min

doi:10.1016/j.adhoc.2023.103109

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…Furthermore, considering the importance of the matching between TaVs and corresponding processing terminals, the authors in [20] propose a four-lane dual carriageway model to simulate the urban traffic environment and use the Kuhn-Munkras algorithm to realize the matching of TaVs and service providers. Some of the literature adopts traditional methods for computing offloading [21][22][23][24][25]. For instance, a queue-based improved multi-objective particle swarm optimization algorithm to solve the problem of multi-dependent task offloading in multi-access edge computing is proposed in [21].…”

Section: Related Workmentioning

confidence: 99%

“…After VECS selection, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems and a distributed offloading strategy based on a binary-coded genetic algorithm is used to obtain an adaptive offloading decision. Considering the heterogeneity of communication modes and computing capabilities of network computing points in ubiquitous networks, a distributed multi-hop computing task offloading framework based on an improved genetic algorithm is proposed in [23] so that tasks could be recursively offloaded among computing points in the ubiquitous network. Benders decomposition technology is used to realize task offloading in [24].…”

Section: Related Workmentioning

confidence: 99%

“…As seen in Table 1, this literature is based on the three execution modes, M 1 , M 2 , and M 3 , which all involve the local execution mode M 0 . Most of the research in the M 1 and M 2 modes adopts the partial offloading mode [9,10,[14][15][16]25,29], and most of the research in the M 3 mode adopts the 0-1 offloading mode [19,20,23,31,32]. Furthermore, we have studied the use of V2V and V2I links to extend the system's computing resources [33,34].…”

Section: Related Workmentioning

confidence: 99%

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Multi-User Computation Offloading and Resource Allocation Algorithm in a Vehicular Edge Network

Liu,

Zheng,

Zhang

et al. 2024

Sensors

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In Vehicular Edge Computing Network (VECN) scenarios, the mobility of vehicles causes the uncertainty of channel state information, which makes it difficult to guarantee the Quality of Service (QoS) in the process of computation offloading and the resource allocation of a Vehicular Edge Computing Server (VECS). A multi-user computation offloading and resource allocation optimization model and a computation offloading and resource allocation algorithm based on the Deep Deterministic Policy Gradient (DDPG) are proposed to address this problem. Firstly, the problem is modeled as a Mixed Integer Nonlinear Programming (MINLP) problem according to the optimization objective of minimizing the total system delay. Then, in response to the large state space and the coexistence of discrete and continuous variables in the action space, a reinforcement learning algorithm based on DDPG is proposed. Finally, the proposed method is used to solve the problem and compared with the other three benchmark schemes. Compared with the baseline algorithms, the proposed scheme can effectively select the task offloading mode and reasonably allocate VECS computing resources, ensure the QoS of task execution, and have a certain stability and scalability. Simulation results show that the total completion time of the proposed scheme can be reduced by 24–29% compared with the existing state-of-the-art techniques.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Multi-User Computation Offloading and Resource Allocation Algorithm in a Vehicular Edge Network

Liu,

Zheng,

Zhang

et al. 2024

Sensors

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“…Many experts and scholars currently have relatively mature research work on computational offloading strategies, mainly optimizing or jointly optimizing metrics such as time delay and energy consumption. There are mainly computational offloading frameworks based on mathematical models [ 6 , 7 ], computational offloading schemes based on intelligent optimization algorithms such as genetic algorithms [ 8 , 9 , 10 ], and whale optimization [ 11 ]. Offloading strategies for collaborative computing have also been studied.…”

Section: Related Workmentioning

confidence: 99%

Research on Cloud-Edge-End Collaborative Computing Offloading Strategy in the Internet of Vehicles Based on the M-TSA Algorithm

Guo

Wang

2023

Sensors

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In the Internet of Vehicles scenario, the in-vehicle terminal cannot meet the requirements of computing tasks in terms of delay and energy consumption; the introduction of cloud computing and MEC is an effective way to solve the above problem. The in-vehicle terminal requires a high task processing delay, and due to the high delay of cloud computing to upload computing tasks to the cloud, the MEC server has limited computing resources, which will increase the task processing delay when there are more tasks. To solve the above problems, a vehicle computing network based on cloud-edge-end collaborative computing is proposed, in which cloud servers, edge servers, service vehicles, and task vehicles themselves can provide computing services. A model of the cloud-edge-end collaborative computing system for the Internet of Vehicles is constructed, and a computational offloading strategy problem is given. Then, a computational offloading strategy based on the M-TSA algorithm and combined with task prioritization and computational offloading node prediction is proposed. Finally, comparative experiments are conducted under task instances simulating real road vehicle conditions to demonstrate the superiority of our network, where our offloading strategy significantly improves the utility of task offloading and reduces offloading delay and energy consumption.

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“…In two-user MEC networks, Ref. [19] examined the effects of inter-user task dependencies on resource allocation and computation offloading decisions. it then proposed an efficient algorithm to optimize resource allocation and computation offloading decisions.…”

Section: Related Workmentioning

confidence: 99%

Energy-efficient multiuser and multitask computation offloading optimization method

Pan

Qian

2023

Intell. and Converged Netw.

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For dynamic application scenarios of Mobile Edge Computing (MEC), an Energy-efficient Multiuser and Multitask Computation Offloading (EMMCO) optimization method is proposed. Under the consideration of multiuser and multitask computation offloading, first, the EMMCO method takes into account the existence of dependencies among different tasks within an implementation, abstracts these dependencies as a Directed Acyclic Graph (DAG), and models the computation offloading problem as a Markov decision process. Subsequently, the task embedding sequence in the DAG is fed to the RNN encoder-decoder neural network with combination of the attention mechanism, the long-term dependencies among different tasks are successfully captured by this scheme. Finally, the Improved Policy Loss Clip-based PPO2 (IPLC-PPO2) algorithm is developed, and the RNN encoder-decoder neural network is trained by the developed algorithm. The loss function in the IPLC-PPO2 algorithm is utilized as a preference for the training process, and the neural network parameters are continuously updated to select the optimal offloading scheduling decisions. Simulation results demonstrate that the proposed EMMCO method can achieve lower latency, reduce energy consumption, and obtain a significant improvement in the Quality of Service (QoS) than the compared algorithms under different situations of mobile edge network.

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Genetic algorithm for delay efficient computation offloading in dispersed computing

Cited by 9 publications

References 33 publications

Multi-User Computation Offloading and Resource Allocation Algorithm in a Vehicular Edge Network

Multi-User Computation Offloading and Resource Allocation Algorithm in a Vehicular Edge Network

Research on Cloud-Edge-End Collaborative Computing Offloading Strategy in the Internet of Vehicles Based on the M-TSA Algorithm

Energy-efficient multiuser and multitask computation offloading optimization method

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