Online Learning and Resource Allocation for User Experience Improvement in Mobile Edge Clouds

Lin, Tongtong; Qiu, Jiahang; Fu, Liqun

doi:10.1109/icc42927.2021.9500905

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Their method can quickly adapt to sudden changes in the average packet arrival rate. Lin et al [21] advocated feedbackbased online resource allocation, and designed an online queue resource allocation algorithm based on Lyapunov optimization. The algorithm was utilized to optimize the resource allocation strategy immediately after the arrival of users.…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Mobile Learning Resources Based on a Complex Network

Liu¹,

Zhao²

2022

Int. J. Emerg. Technol. Learn.

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Currently, centralized online learning can no longer meet the fragmented learning needs of learners. It is a hot topic in mobile learning to allocate reasonable mobile learning resources (MLRs) for user terminals and servers. However, the existing studies have rarely discussed the matching relationship between the MLR features of user terminals and servers. To fill up the gap, this paper tries to optimize the allocation of MLRs based on the theory of mobile knowledge complex network. Firstly, a local bidirectional fitness model was established to optimize MLR allocation, and the core nodes were mined from the complex network of MLRs. Next, the authors clarified the causality between the density of MLR complex network and resource integration, constructed an evaluation index system (EIS) for MLR integration ability, and evaluated the overall resource integration ability of MLR network resources. The proposed network was proved effective in optimizing the resource allocation of mobile learning networks through experiments.

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Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Mobile Learning Resources Based on a Complex Network

Liu¹,

Zhao²

2022

Int. J. Emerg. Technol. Learn.

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An Optimized Collaborative Scheduling Algorithm for Prioritized Tasks with Shared Resources in Mobile-Edge and Cloud Computing Systems

et al. 2022

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Mobile edge computing (MEC) is a promising technology that has the potential to meet the latency requirements of next-generation mobile networks. Since MEC servers have limited resources, an orchestrator utilizes a scheduling algorithm to decide where and when each task should execute so that the quality of service (QoS) of each task is achieved. The scheduling algorithm should use the least possible resources required to meet the service demands. In this paper, we develop a two-level cooperative scheduling algorithm with a centralized orchestrator layer. The first scheduling level is used to schedule tasks locally on MEC servers. In contrast, the second level resides at the orchestrator and assigns tasks to a neighboring base station or the cloud. The tasks serve in accordance with their priority, which is determined by the latency and required throughput. We also present a resource optimization algorithm for determining resource distribution in the system in order to ensure satisfactory service availability at the minimum cost. The resource optimization algorithm contains two variations that can be employed depending on the traffic model. One variant is used when the traffic is uniformly distributed, and the other is used when the traffic load is unbalanced among base stations. Numerical results show that the cooperative model of task scheduling outperforms the non-cooperative model. Furthermore, the results show that the suggested scheduling algorithm performs better than other well-known scheduling algorithms, such as shortest job first scheduling and earliest deadline first scheduling.

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The Optimal Resource Allocation Across Multiple Links in Cyber Twin Based Edge Cloud

Wu,

2023

ICC 2023 - IEEE International Conference on Communications

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Online Learning and Resource Allocation for User Experience Improvement in Mobile Edge Clouds

Cited by 4 publications

References 11 publications

Optimal Allocation of Mobile Learning Resources Based on a Complex Network

Optimal Allocation of Mobile Learning Resources Based on a Complex Network

An Optimized Collaborative Scheduling Algorithm for Prioritized Tasks with Shared Resources in Mobile-Edge and Cloud Computing Systems

The Optimal Resource Allocation Across Multiple Links in Cyber Twin Based Edge Cloud

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