Computing Offloading Decision Based on Multi-objective Immune Algorithm in Mobile Edge Computing Scenario

Zhu, Shixin; Sun, En-lin; Zhang, Qinghua; Cai, Jiang-hao

doi:10.1007/s11277-023-10318-2

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Calculate the existence time t exit of the link between V * and V i com , and the data transmission delay T VV ; (6) if T VV < t exit do (7) calculate the time of transmission fog as T VF and time of driving out of the fog area as T out ; (8) if T VF < T out do (9) will be added V i com to the collection V tran of candidate vehicles; (10) end if (11) end if (12) end for (13) end if (14) if V tran ̸ = ∅ do (15) Select the nearest vehicle in the middle distance as V next ; (16) if the distance of V next after forwarding is closer to the F goal do (17) H temp = H temp + 1; (18) Located in the fog area after receiving the task is F next , the current fog area is F this ; (19) if…”

Section: Algorithm 1 Forwarding Vehicles Predicting Algorithmmentioning

confidence: 99%

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Cloud–Fog Collaborative Computing Based Task Offloading Strategy in Internet of Vehicles

Zhu,

Liu,

Zhu

et al. 2024

Electronics

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Vehicle terminals in the mobile internet of vehicles are faced with difficulty in the requirements for computation-intensive and delay-sensitive tasks, and vehicle mobility also causes dynamic changes in vehicle-to-vehicle (V2V) communication links, which results in a lower task offloading quality. To solve the above problems, a new task offloading strategy based on cloud–fog collaborative computing is proposed. Firstly, the V2V-assisted task forwarding mechanism is introduced under cloud–fog collaborative computing, and a forwarding vehicles predicting algorithm based on environmental information is designed; then, considering the parallel computing relationship of tasks in each computing node, a task offloading cost model is constructed with the goal of minimizing delay and energy consumption; finally, a multi-strategy improved genetic algorithm (MSI-GA) is proposed to solve the above task offloading optimization problem, which adapts the chaotic sequence to initialize the population, comprehensively considers the influence factors to optimize the adaptive operator, and introduces Gaussian perturbation to enhance the local optimization ability of the algorithm. The simulation experiments show that compared with the existing strategies, the proposed task offloading strategy has the lower task offloading cost for a different number of tasks and fog nodes; additionally, the introduced V2V auxiliary task forwarding mechanism can reduce the forwarding load of fog nodes by cooperative vehicles to forward tasks.

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Section: Algorithm 1 Forwarding Vehicles Predicting Algorithmmentioning

confidence: 99%

“…Similarly, refs. [12][13][14][15] formulate the offloading problem as a multi-objective optimization problem and employ various multi-objective evolutionary algorithms to find the optimal offloading strategy. Ref.…”

Section: Introductionmentioning

confidence: 99%