Optimizing Applications for Mobile Cloud Computing Through MOCCAA

Baraki, Harun; Jahl, Alexander; Jakob, Stefan; Schwarzbach, Corvin; Fax, Malte; Geihs, Kurt

doi:10.1007/s10723-019-09492-0

Cited by 9 publications

(3 citation statements)

References 46 publications

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“…It is less conducive to the applications which require low latency while accessing. So far, some interesting research topics have been proposed in the MEC network [4,6].…”

Section: Mobile Edge Computing Networkmentioning

confidence: 99%

“…Finally, for the DDRA and ARCM algorithm, both of them consider the blocking probability and the number of queued nodes while assigning the tasks, hence these two algorithms are more balanced than CDRM in terms of node utilization. To help to understand the status of the loading of the system clearly, we calculate MAD value through formula (6). In the formula, n represents the total number of nodes, and 𝑢 𝑖 represents the utilization rate of the nodes.…”

Section: Loosely Environment {Node=20; Workload=1000~10000}mentioning

confidence: 99%

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An adaptive replica configuration mechanism based on predictive file popularity and queue balance in mobile edge computing environment

et al. 2022

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In the current era of the Internet of Things (IoT), various devices can provide more services by connecting to the Internet. However, the explosive growth of connected devices will cause the cloud core overload and significant network delays. To overcome these problems, the Mobile Edge Computing (MEC) network is proposed to provide most of the computing and storage near the radio access network to reduce the traffic of the core cloud network and provide lower latency for the terminal.Mobile edge computing can work with third parties to develop multiple services, such as mobile big data analysis and context-aware services. However, when there is a large amount of popular data accessed in a short period, the system must generate many replicas, which will not only reduce access efficiency but also cause additional traffic overhead. To improve the above problems, an Adaptive Replica Configuration Mechanism (ARCM) is proposed in this paper to predict the popularity of the file and make a replica to the low-blocking node. This method spreads the subsequent access workload by copying the popular file in advance to improve the overall performance of the system.

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“…It is less conducive to the applications which require low latency while accessing. So far, some interesting research topics have been proposed in the MEC network [4,6].…”

Section: Mobile Edge Computing Networkmentioning

confidence: 99%

Section: Loosely Environment {Node=20; Workload=1000~10000}mentioning

confidence: 99%

An adaptive replica configuration mechanism based on predictive file popularity and queue balance in mobile edge computing environment

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Despite the increasingly powerful performance of current devices, mobile devices cannot solve the problem of computation-intensive applications or low-latency, high-reliability communication requirements. In order to solve the problem of insufficient computing resources for mobile devices, various solutions have appeared, and the first one is Mobile Cloud Computing (MCC) [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Approach for Computation Offloading in MEC Dynamic Networks

Fan,

Cai

2023

Preprint

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In this paper, we study the dynamic time-slot scheme server selection problem in mobile edge computing (MEC) system, which takes into account the dynamic access of users at edge servers and introduces the dynamically changing factors affecting the workload of edge servers, such as offloading policies, offloading ratios, user’s transmitting power, and reserved capacity of the servers. However, the above factors make it tricky to achieve long-term optimization in the edge server selection process. To cope with the above challenges, we model the server selection problem faced as a Markov Decision Process (MDP) and propose a Deep Reinforcement Learning (DRL) based algorithm to solve it. Selection strategy learning is performed based on the observed server selection performance in previous phases. Simulation results show that the DRL-based algorithm proposed in this paper has the lowest average latency compared to benchmark algorithms.

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Minimum-Energy Computation Offloading in Mobile Edge Computing with Hybrid PSO-DE Algorithm

Zhang

Tan

Zhou

et al. 2022

Computer Supported Cooperative Work and Social Computing

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Optimizing Applications for Mobile Cloud Computing Through MOCCAA

Cited by 9 publications

References 46 publications

An adaptive replica configuration mechanism based on predictive file popularity and queue balance in mobile edge computing environment

An adaptive replica configuration mechanism based on predictive file popularity and queue balance in mobile edge computing environment

A Deep Reinforcement Approach for Computation Offloading in MEC Dynamic Networks

Minimum-Energy Computation Offloading in Mobile Edge Computing with Hybrid PSO-DE Algorithm

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