Mobility-Aware Tasks Offloading in Mobile Edge Computing Environment

Wu, Chunrong; Peng, Qinglan; Xia, Yunni; Lee, Jia

doi:10.1109/candar.2019.00034

Cited by 20 publications

(4 citation statements)

References 14 publications

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“…Ouyang et al [24] optimized the real-time performance of services in a mobile Edge environment based on a Lyapunov technique to achieve a trade-off between the user-perceived latency and the offloading costs. Wu et al [25] presented a deep learning to predict mobile users' trajectories and take task offloading decisions in real-time based on service quality metrics. De Maio et al [26] presented a genetic metaheuristic that predicts Edge devices' availability for partially offloading applications based on a trade-off between the user satisfaction and the provider financial profit.…”

Section: Mobilitymentioning

confidence: 99%

See 1 more Smart Citation

Mobility-Aware IoT Application Placement in the Cloud – Edge Continuum

Kimovski

Mehran

Kerth

et al. 2022

IEEE Trans. Serv. Comput.

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The Edge computing extension of the Cloud services towards the network boundaries raises important placement challenges for IoT applications running in a heterogeneous environment with limited computing capacities. Unfortunately, existing works only partially address this challenge by optimizing a single or aggregate objective (e.g., response time), and not considering the edge devices' mobility and resource constraints. To address this gap, we propose a novel mobility-aware multi-objective IoT application placement (mMAPO) method in the Cloud -Edge Continuum that optimizes completion time, energy consumption, and economic cost as conflicting objectives. mMAPO utilizes a Markov model for predictive analysis of the Edge device mobility and constrains the optimization to devices that do not frequently move through the network. We evaluate the quality of the mMAPO placements using simulation and real-world experimentation on two IoT applications. Compared to related work, mMAPO reduces the economic cost by 28% and decreases the completion time by 80% while maintaining a stable energy consumption.

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Section: Mobilitymentioning

confidence: 99%

“…Research gap: The related work focused on predicting user mobility [10], [24], [25], [26] to improve application scalability. However, it fails to address the effect of Edge device mobility on essential application components with respect to the overall execution.…”

Section: Mobilitymentioning

confidence: 99%

Mobility-Aware IoT Application Placement in the Cloud – Edge Continuum

Kimovski

Mehran

Kerth

et al. 2022

IEEE Trans. Serv. Comput.

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show abstract

“…It is the way of connection that brings huge challenges to the task offloading.Currently, this issue has attracted much attention of authors in [ 9 , 14 , 19 , 21 , 22 , 41 ]. A deep learning method in [ 37 ] is used to predict the trajectory of mobile edge network users for the task distribution among the ESs. O.Tao et al.…”

Section: Related Workmentioning

confidence: 99%

A Parallelizable Task Offloading Model with Trajectory-Prediction for Mobile Edge Networks

Han

Lin

Yuan

et al. 2022

Entropy

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As an emerging computing model, edge computing greatly expands the collaboration capabilities of the servers. It makes full use of the available resources around the users to quickly complete the task request coming from the terminal devices. Task offloading is a common solution for improving the efficiency of task execution on edge networks. However, the peculiarities of the edge networks, especially the random access of mobile devices, brings unpredictable challenges to the task offloading in a mobile edge network. In this paper, we propose a trajectory prediction model for moving targets in edge networks without users’ historical paths which represents their habitual movement trajectory. We also put forward a mobility-aware parallelizable task offloading strategy based on a trajectory prediction model and parallel mechanisms of tasks. In our experiments, we compared the hit ratio of the prediction model, network bandwidth and task execution efficiency of the edge networks by using the EUA data set. Experimental results showed that our model is much better than random, non-position prediction parallel, non-parallel strategy-based position prediction. Where the task offloading hit rate is closed to the user’s moving speed, when the speed is less 12.96 m/s, the hit rate can reach more than 80%. Meanwhile, we we also find that the bandwidth occupancy is significantly related to the degree of task parallelism and the number of services running on servers in the network. The parallel strategy can boost network bandwidth utilization by more than eight times when compared to a non-parallel policy as the number of parallel activities grows.

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“…An ant colony optimization (ACO) algorithm has been designed to minimize system cost, provide better QoS and increase the MEC system performance. A mobility-aware task offloading problem has been studied in the MEC environment [29]. The mobility of edge users is used with a deep-learning approach to identify connectivity patterns and forecast potential user trajectories.…”

Section: Related Workmentioning

confidence: 99%

A User-Centric QoS-Aware Multi-Path Service Provisioning in Mobile Edge Computing

et al. 2021

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Recent development in modern wireless applications and services, such as augmented reality, image processing, and network gaming requires persistent computing on average commercial wireless devices to perform complex tasks with low latency. The traditional cloud systems are unable to meet those requirements solely. In the said perspective, Mobile Edge Computing (MEC) serves as a proxy between the things (devices) and the cloud, pushing the computations at the edge of the network. The MEC provides an effective solution to fulfill the demands of low-latency applications and services by executing most of the tasks within the proximity of users. The main challenge, however, is that too many simultaneous service requests created by wireless access produce severe interference, resulting in a decreased rate of data transmission. In this paper, we made an attempt to overcome the aforesaid limitation by proposing a usercentric QoS-aware multi-path service provisioning approach. A densely deployed base station MEC environment has overlapping coverage regions. We exploit such regions to distribute the service requests in a way that avoid hotspots and bottlenecks. Our approach is adaptive and can tune to different parameters based on service requirements. We performed several experiments to evaluate the effectiveness of our approach and compared it with the traditional Greedy approach. The results revealed that our approach improves the network state by 26.95% and average waiting time by 35.56% as compared to the Greedy approach. In addition, the QoS violations were also reduced by the fraction of 16.

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Mobility-Aware Tasks Offloading in Mobile Edge Computing Environment

Cited by 20 publications

References 14 publications

Mobility-Aware IoT Application Placement in the Cloud – Edge Continuum

Mobility-Aware IoT Application Placement in the Cloud – Edge Continuum

A Parallelizable Task Offloading Model with Trajectory-Prediction for Mobile Edge Networks

A User-Centric QoS-Aware Multi-Path Service Provisioning in Mobile Edge Computing

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