LiMPO: lightweight mobility prediction and offloading framework using machine learning for mobile edge computing

Zaman, Sardar Khaliq uz; Jehangiri, Ali Imran; Maqsood, Tahir; Haq, Nuhman ul; Umar, Arif Iqbal; Shuja, Junaid; Ahmad, Zulfiqar; Dhaou, Imed Ben; Alsharekh, Mohammed F.

doi:10.1007/s10586-021-03518-7

Cited by 58 publications

(23 citation statements)

References 48 publications

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“…For both schemes, mobility-awareness is the key to the offloading performance. A lightweight mobility prediction method is proposed in [13] for server selection and task offloading. A multihop task offloading policy is proposed in [14] for the V2V scenario based on the mobility patterns and computing capabilities.…”

Section: ) Diversity Gain Maximization For Task Offloadingmentioning

confidence: 99%

MEET: Mobility-Enhanced Edge inTelligence for Smart and Green 6G Networks

et al. 2023

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Edge intelligence is an emerging paradigm for real-time training and inference at the wireless edge, thus enabling mission-critical applications. Accordingly, base stations (BSs) and edge servers (ESs) need to be densely deployed, leading to huge deployment and operation costs, in particular the energy costs. In this article, we propose a new framework called Mobility-Enhanced Edge inTelligence (MEET), which exploits the sensing, communication, computing, and self-powering capabilities of intelligent connected vehicles for the smart and green 6G networks. Specifically, the operators can incorporate infrastructural vehicles as movable BSs or ESs, and schedule them in a more flexible way to align with the communication and computation traffic fluctuations. Meanwhile, the remaining compute resources of opportunistic vehicles are exploited for edge training and inference, where mobility can further enhance edge intelligence by bringing more compute resources, communication opportunities, and diverse data.In this way, the deployment and operation costs are spread over the vastly available vehicles, so that the edge intelligence is realized cost-effectively and sustainably. Furthermore, these vehicles can be either powered by renewable energy to reduce carbon emissions, or charged more flexibly during off-peak hours to cut electricity bills. I. INTRODUCTION6G networks are expected to support numerous mission-critical applications, such as autonomous driving, smart city, and industrial Internet of things. Artificial intelligence (AI)-based

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Section: ) Diversity Gain Maximization For Task Offloadingmentioning

confidence: 99%

MEET: Mobility-Enhanced Edge inTelligence for Smart and Green 6G Networks

et al. 2023

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“…The mixture of the two can effectively reduce the transmission energy to reduce the response time. In the literature [36], a lightweight mobility prediction and offloading framework and a multi-objective genetic algorithm-based server selection technique are proposed with energy efficiency and time delay as the constrained objectives. The framework can offload computational tasks to predicted user locations using artificial neural networks with relatively low complexity.…”

Section: Multi-user Computing Offload Service (1) Reduce Latencymentioning

confidence: 99%

Research on computational offloading strategies based on mobile edge computing

Wang

2022

Second International Conference on Optics and Communication Technology (ICOCT 2022)

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With the advent and continuous development of the Internet of Things era, a large number of emerging applications, such as driverless, have emerged. Usually such applications need to consume a lot of computing resources and have high demand for low latency and data processing. However, the limited computing power of the devices themselves cannot meet the low latency and other needs of emerging applications, which limits the further development of IoT. Mobile edge computing (MEC) is an emerging computing paradigm where mobile devices interact directly with MEC platforms at the network edge and offload computational tasks to MEC servers to solve the problem of limited device resources. In this paper, we conduct an in-depth study of the computational offloading problem in mobile edge computing, and provide an in-depth and comprehensive summary of the current status and results of the research on MEC-oriented computational offloading strategies. First, the MEC technology and its typical application scenarios are introduced; then, the computation offloading strategy with optimized time, energy consumption, and hybrid goals for system performance gain is introduced from two aspects: single-user computation offloading and multi-user computation offloading.

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“…Player D i broadcasts S * i to all the other edge devices (8) Player D i sends the token to the next node ( 9) UntilMSE(S * (R) , S * (R− 1) ) < ξ (10)…”

Section: Input: λmentioning

confidence: 99%

“…A common application of EC is task ofoading from edge devices to ECN/ECS, i.e., tasks generated in edge devices are delivered to ECN/ECS for remote computing [9]. Recently, several studies have investigated computing task ofoading strategies in many network environments for EC [10][11][12]. However, to the best of our knowledge, few studies have considered their applications in TSN.…”

Section: Introductionmentioning

confidence: 99%

Multitask Offloading Strategy for Edge Computing in Time-Sensitive Networking

Chen

Wang

et al. 2022

Mobile Information Systems

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Edge computing is a creative computing paradigm that enhances the computing capacity of the edge device close to the data source. As the key technology of edge computing, task offloading, which can improve the response speed and the stability of the network system, has attracted much attention and has been applied in many network scenarios. However, few studies have considered the application of task offloading in time-sensitive networking (TSN), which is a promising technology that has the potential to guarantee data delivery with bounded latency and low jitter. To this end, we establish a task offloading stream transmission model for TSN based on the queueing theory. With the model, the average response time can be achieved by quantitative calculation. Then, we introduce the backward method to construct a utility function and formulate an exact potential game to model the task offloading competition among edge devices considering the minimization of the average response time of all tasks. Furthermore, a distributed and sequential decision-making algorithm for multitask offloading (DSDA-MO) is proposed to find the Nash equilibrium. Through numerical studies, we evaluate the algorithm performance as well as the benefit of the multitask offloading mechanism. The results reveal that through the proposed game theoretic approach, we can obtain the optimal multitask offloading strategy, which can significantly reduce the task computation delay in TSN, within a finite number of rounds of calculation.

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LiMPO: lightweight mobility prediction and offloading framework using machine learning for mobile edge computing

Cited by 58 publications

References 48 publications

MEET: Mobility-Enhanced Edge inTelligence for Smart and Green 6G Networks

MEET: Mobility-Enhanced Edge inTelligence for Smart and Green 6G Networks

Research on computational offloading strategies based on mobile edge computing

Multitask Offloading Strategy for Edge Computing in Time-Sensitive Networking

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