Task migration computation offloading with low delay for mobile edge computing in vehicular networks

Qiao, Bingxue; Liu, Chubo; Liu, Jing; Hu, Yikun; Li, Kenli; Li, Keqin

doi:10.1002/cpe.6494

Cited by 10 publications

(3 citation statements)

References 33 publications

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“…Three variants of this problem are analyzed, and a group migration algorithm with known user trajectories is designed. In [15], a heuristic Task Migration Computing Offloading (TMCO) scheme is proposed for the challenges brought by complex network environment and end-user mobility, which can dynamically select the appropriate location to offload tasks for mobile users within the deadline. In [16], for collaborative vehicle edge computing group environment, a computational task migration problem is defined to balance the load and minimize the migration cost, and reinforcement learning algorithm is adopted to solve this problem.…”

Section: A Task Migration In Multi-cloudlets Scenariomentioning

confidence: 99%

Delay-Aware and Profit-Maximizing Task Migration for the Cloudlet Federation

Ye¹,

Guo²,

Li³

2022

IJACSA

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As the result of Open Edge Computing (OEC) project, cloudlet embodies the middle layer of edge computing architecture. Due to the close deployment to the user side, responding to user requests through cloudlet can reduce delay, improve security, and reduce bandwidth occupancy. In order to improve the quality of user experience, more and more cloudlets need to be deployed, which makes the deployment and management costs of Clouldlet service Providers (CLP) significantly increased. Therefore, the cloudlet federation appears as a new paradigm that can reduce the cost of cloudlet deployment and management by sharing cloudlet resources among CLPs.Facing the cloudlet federation scenario, more attention still needs to be paid to the heterogeneity of resource prices, the balance of benefits among CLPs, and the more complex delay computation when exploring task migration strategies. For delay-sensitive and delay-tolerance tasks, a delay-aware and profit-maximizing task migration strategy is proposed considering the relationship between the delay and the quotation of different tasks, as well as the dynamic pricing mechanism when resources are shared among CLPs. Experimental results show that the proposed algorithm outperforms the baseline algorithm in terms of revenue and response delay.

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Section: A Task Migration In Multi-cloudlets Scenariomentioning

confidence: 99%

Delay-Aware and Profit-Maximizing Task Migration for the Cloudlet Federation

Ye¹,

Guo²,

Li³

2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…According to the sorting result, a new parent population is created (15). This process continues until the stop condition is met (9). A stopping criterion is defined as the given number of iterations.…”

Section: Sorting and Selectionmentioning

confidence: 99%

“…Therefore, the completion time (latency) of tasks is an important factor for them. Some authors addressed latency as a metric for computation offloading optimization [5][6][7][8][9]. Other applications (e.g., health care sensors, drones, wearables) require a prolonged battery lifetime.…”

Section: Introductionmentioning

confidence: 99%

Offloading dependent tasks in MEC-enabled IoT systems: A preference-based hybrid optimization method

Sadatdiynov

Cui

Huang

2022

Peer-to-Peer Netw. Appl.

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The rapid development of IoT-based services has resulted in an exponential increase in the number of connected smart mobile devices (SMDs). Processing the massive data generated by the large number of SMDs is becoming a big problem for mobile devices, servers, and wireless communication channels. A Multi-access Edge Computing (MEC) paradigm partially mitigates this problem by deploying edge server nodes at the edge of wireless networks nearby SMDs, but the challenge still remains due to the limited computation capacity of MEC servers and the bandwidth of wireless channels. In addition, the dependency of tasks generated by applications on SMDs increases the complexity of the problem. In this paper, we propose a constrained multiobjective computation offloading optimization solution to resolve the problem of task dependency under limited resources. This solution improves the Quality of Service (QoS) through minimizing the latency, energy consumption, and rate of task failure caused by limited resources. We propose a twostaged hybrid computation offloading optimization method to solve the problem. In the first stage, the computation offloading decisions are made based on the preferences of tasks. Then, in the second stage, global optimal solutions are found using the modified Non-Dominated Sorting Genetic Algorithm (NSGA-III). The overall efficiency of the proposed method is increased owing to the preference-based algorithm Offloading Dependent Tasks in MEC-enabled IoT Systems: A Preference reinforcing the NSGA-III algorithm by generating a better initial population. The results of extensive experiments show that the efficiency of the proposed method is significantly better than the existing methods.

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Maximizing the number of completed tasks in MEC considering time and energy constraints

Liu

Deng

et al. 2023

Soft Comput

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Task migration computation offloading with low delay for mobile edge computing in vehicular networks

Cited by 10 publications

References 33 publications

Delay-Aware and Profit-Maximizing Task Migration for the Cloudlet Federation

Delay-Aware and Profit-Maximizing Task Migration for the Cloudlet Federation

Offloading dependent tasks in MEC-enabled IoT systems: A preference-based hybrid optimization method

Maximizing the number of completed tasks in MEC considering time and energy constraints

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