Adaptive application offloading decision and transmission scheduling for mobile cloud computing

Wang, Junyi; Peng, Jie; Wei, Yanheng; Liu, Didi; Fu, Jielin

doi:10.1109/cc.2017.7897332

Cited by 28 publications

(10 citation statements)

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“…In [18], the authors consider a dynamic environment with different wireless networks among which the decision is to select the network reducing the execution cost. Reference [19] proposes a fog offloading scheme in a mobile environment.…”

Section: Related Workmentioning

confidence: 99%

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Computation Offloading in Heterogeneous Vehicular Edge Networks: On-Line and Off-Policy Bandit Solutions

Bozorgchenani

Tarchi

Hossain

2022

IEEE Trans. on Mobile Comput.

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With the rapid advancement of Intelligent Transportation Systems (ITS) and vehicular communications, Vehicular Edge Computing (VEC) is emerging as a promising technology to support low-latency ITS applications and services. In this paper, we consider the computation offloading problem from mobile vehicles/users in a heterogeneous VEC scenario, and focus on the networkand base station selection problems, where different networks have different traffic loads. In a fast-varying vehicular environment, computation offloading experience of users is strongly affected by the latency due to the congestion at the edge computing servers co-located with the base stations. However, as a result of the non-stationary property of such an environment and also information shortage, predicting this congestion is an involved task. To address this challenge, we propose an on-line learning algorithm and an off-policy learning algorithm based on multi-armed bandit theory. To dynamically select the least congested network in a piece-wise stationary environment, these algorithms predict the latency that the offloaded tasks experience using the offloading history. In addition, to minimize the task loss due to the mobility of the vehicles, we develop a method for base station selection. Moreover, we propose a relaying mechanism for the selected network, which operates based on the sojourn time of the vehicles. Through intensive numerical analysis, we demonstrate that the proposed learning-based solutions adapt to the traffic changes of the network by selecting the least congested network, thereby reducing the latency of offloaded tasks. Moreover, we demonstrate that the proposed joint base station selection and the relaying mechanism minimize the task loss in a vehicular environment.

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Section: Related Workmentioning

confidence: 99%

“…All the EUs have certain computational and storage capabilities. The second layer includes M wireless networks, each having certain number of BSs [18]. m = {N 1 , .…”

Section: Vec Modelmentioning

confidence: 99%

Computation Offloading in Heterogeneous Vehicular Edge Networks: On-Line and Off-Policy Bandit Solutions

Bozorgchenani

Tarchi

Hossain

2022

IEEE Trans. on Mobile Comput.

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“…Computation offloading is a key point in the research of MEC and many works have devoted to this aspect [3][4][5][9][10][11][12][13]. In terms of the objective of optimisation, many works aim to increase the performance of SMDs in reducing the processing delay of tasks and thus to enable computation intensive tasks, and other works intend to decrease the energy consumption of SMDs and thus to extend the standby time of SMDs.…”

Section: Introductionmentioning

confidence: 99%

“…In [5] we considered the joint optimisation of task offloading and Internet content caching in a system with one MEC node. A single-SMD-multiple-MECS adaptive task offloading framework was proposed in [12], where by offloading computation to different MECS, the weighted averaged cost of processing delay and energy consumption was minimised. The authors in [13] further considered a system consisting of multiple SMDs and a remote cloud server.…”

Section: Introductionmentioning

confidence: 99%

Task offloading, load balancing, and resource allocation in MEC networks

Li¹,

Zhai

et al. 2020

IET Communications

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“…The authors in [2] investigated a delay minimizing computation offloading problem for a single-SMD-based MEC system. An adaptive task offloading framework with an SMD and multiple MECSs was proposed in [3], and by offloading tasks to different MECSs, the weighted average cost of processing delay and EC was minimized. The authors in [4] further considered a system comprising multiple SMDs, multiple long term evolution eNB-based access point, and a cloud server.…”

mentioning

confidence: 99%

Energy-efficient task offloading, load balancing, and resource allocation in mobile edge computing enabled IoT networks

Li¹,

Zhai

et al. 2018

Sci. China Inf. Sci.

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Adaptive application offloading decision and transmission scheduling for mobile cloud computing

Cited by 28 publications

References 0 publications

Computation Offloading in Heterogeneous Vehicular Edge Networks: On-Line and Off-Policy Bandit Solutions

Computation Offloading in Heterogeneous Vehicular Edge Networks: On-Line and Off-Policy Bandit Solutions

Task offloading, load balancing, and resource allocation in MEC networks

Energy-efficient task offloading, load balancing, and resource allocation in mobile edge computing enabled IoT networks

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