Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms

Elgendy, Ibrahim A.; Zhang, Weizhe; He, Hui; Gupta, Brij B.; El‐Latif, Ahmed A. Abd

doi:10.1007/s11276-021-02554-w

Cited by 135 publications

(48 citation statements)

References 54 publications

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“…ey proposed an effective Lyapunov online algorithm that can perform joint task unloading and dynamic data caching strategies for computing tasks or data content. Elgendy et al [14] cached the completed applications and related codes on the edge server. On this basis, two effective algorithms based on Q-learning and deep-q-network are used to solve the approximate optimal solution of the problem, effectively reducing the overhead of mobile devices.…”

Section: Related Workmentioning

confidence: 99%

Resource Caching and Task Migration Strategy of Small Cellular Networks under Mobile Edge Computing

Liang

Wang

2021

Mobile Information Systems

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As computing-intensive mobile applications become increasingly diversified, mobile devices’ computing power is hard to keep up with demand. Mobile devices migrate tasks to the Mobile Edge Computing (MEC) platform and improve the performance of task processing through reasonable allocation and caching of resources on the platform. Small cellular networks (SCN) have excellent short-distance communication capabilities, and the combination of MEC and SCN is a promising research direction. This paper focuses on minimizing energy consumption for task migration in small cellular networks and proposes a task migration energy optimization strategy with resource caching by combining optimal stopping theory with migration decision-making. Firstly, the process of device finding the MEC platform with the required task processing resources is formulated as the optimal stopping problem. Secondly, we prove an optimal stopping rule’s existence, obtain the optimal processing energy consumption threshold, and compare it with the device energy consumption. Finally, the platform with the best energy consumption is selected to process the task. In the simulation experiment, the optimization strategy has lower average migration energy consumption and higher average data execution energy efficiency and average distance execution energy efficiency, which improves task migration performance by 10% ∼ 60%.

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Section: Related Workmentioning

confidence: 99%

Resource Caching and Task Migration Strategy of Small Cellular Networks under Mobile Edge Computing

Liang

Wang

2021

Mobile Information Systems

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“…In mobile edge computing [15], an automated recommendation system is proposed for the joint computation of multiuser offloading and task caching. In this paper, Q-learning and Deep-Q-Network-based algorithms are proposed for this system.…”

Section: Introductionmentioning

confidence: 99%

A Smart Healthcare Recommendation System for Multidisciplinary Diabetes Patients with Data Fusion Based on Deep Ensemble Learning

Ihnaini

Khan

et al. 2021

Computational Intelligence and Neuroscience

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The prediction of human diseases precisely is still an uphill battle task for better and timely treatment. A multidisciplinary diabetic disease is a life-threatening disease all over the world. It attacks different vital parts of the human body, like Neuropathy, Retinopathy, Nephropathy, and ultimately Heart. A smart healthcare recommendation system predicts and recommends the diabetic disease accurately using optimal machine learning models with the data fusion technique on healthcare datasets. Various machine learning models and methods have been proposed in the recent past to predict diabetes disease. Still, these systems cannot handle the massive number of multifeatures datasets on diabetes disease properly. A smart healthcare recommendation system is proposed for diabetes disease based on deep machine learning and data fusion perspectives. Using data fusion, we can eliminate the irrelevant burden of system computational capabilities and increase the proposed system’s performance to predict and recommend this life-threatening disease more accurately. Finally, the ensemble machine learning model is trained for diabetes prediction. This intelligent recommendation system is evaluated based on a well-known diabetes dataset, and its performance is compared with the most recent developments from the literature. The proposed system achieved 99.6% accuracy, which is higher compared to the existing deep machine learning methods. Therefore, our proposed system is better for multidisciplinary diabetes disease prediction and recommendation. Our proposed system’s improved disease diagnosis performance advocates for its employment in the automated diagnostic and recommendation systems for diabetic patients.

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“…However, URLLC implementation requires a delay to be decreased from 10 ms to 1 ms. This is achieved by the technical features involved in building a network at the level of access [12][13][14][15], in particular by changing the format of transmitted frames or by changing the structure of the network at other levels [3] primarily because of the "approaching" point of service provided by the user (cloud and edge organization) [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Study and Analysis of Multiconnectivity for Ultrareliable and Low‐Latency Features in Networks and V2X Communications

Paramonov

Peng

Kashkarov

et al. 2021

Wireless Communications and Mobile Computing

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Ultrareliable and low-latency connection (URLLC) is one of the novel features in 5G networks and subsequent generations, in which it targets to fulfill stringent requirements on data rates, reliability, and availability. Moreover, the multiconnectivity concept is introduced to meet these requirements, where multiple different technologies are connected simultaneously, and the data packet is duplicated and transmitted from multiple transmitters. To this end, in this paper, we present an analysis, model, and method to ensure the reliability of data delivery when organizing URLLC in 5G networks. In addition, a new approach based on the organization of multiple connections (multiconnectivity) and duplication of transmitted data is considered. Further, an analytical model is presented for assessing the probability of failure, taking into account the traffic intensity, the probability of failure of elements, and the number of used connections. Moreover, an efficient method is proposed for increasing the reliability of data delivery by optimizing the number of connections. Further, a multiconnectivity-based URLLC model has been built for evaluating the proposed method and verifies that the optimal number of routes for data delivery between the user and the point of service can be obtained, where the probability of losses and equipment reliability are jointly considered. Finally, detailed analysis of results shown that with “equal” routes in terms of load (with an equally probable traffic distribution) and the probability of equipment failure, the optimal number of routes can be found, at which the minimum probability of losses is achieved.

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Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms

Cited by 135 publications

References 54 publications

Resource Caching and Task Migration Strategy of Small Cellular Networks under Mobile Edge Computing

Resource Caching and Task Migration Strategy of Small Cellular Networks under Mobile Edge Computing

A Smart Healthcare Recommendation System for Multidisciplinary Diabetes Patients with Data Fusion Based on Deep Ensemble Learning

Study and Analysis of Multiconnectivity for Ultrareliable and Low‐Latency Features in Networks and V2X Communications

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