Resource Allocation Based on Deep Reinforcement Learning in IoT Edge Computing

Xiong, Xiong; Zheng, Kan; Lei, Lei; Hou, Lu

doi:10.1109/jsac.2020.2986615

Cited by 232 publications

(90 citation statements)

References 29 publications

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“…• Compared with FI, FL is better for solving job schedul- ing, resource allocation, and joint issues [99], [101], since it is flexible, easy to understand, compatible with the uncertainty of CC and EC parameters as well as users' behaviors, and can deal with imprecise data and complex problems with several variables. • DRL performs the best among all LBS for resource allocation, task offloading, and joint issues [145], [161], [163], [171]. The superiority of DRL can be summarized as follows: 1) it can automatically adapt and customize itself according to users' requirements [162]; 2) it can discover/learn new knowledge from large databases; 3) it can develop models that are difficult and expensive to be designed manually due to the requirements of specific skills; and 4) it has a strong ability to handle complex problems by efficiently learning from experiences.…”

Section: Discussion and Future Research Trendsmentioning

confidence: 99%

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A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

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Section: Discussion and Future Research Trendsmentioning

confidence: 99%

“…Experimental results reveal that the proposed approach outperforms the traditional open shortest path first algorithm. Xiong et al [163] proposed a DRL-based approach for resource allocation of IoT in EC. They formulated the resource allocation problem as an MDP.…”

Section: Lbs In CC and Ecmentioning

confidence: 99%

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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“…However, this assumption is unreasonable in some cases because data labeling is usually expensive and time-consuming. erefore, we use the accuracy of the model as a comparison index to compare the particle swarm and the neural network optimized by ADAM [43,44]. e application mathematics mining effect of these three models on the three sample sets is shown in Figure 5.…”

Section: Mining Of Applied Mathematics Educational Resourcesmentioning

confidence: 99%

Research on Mining of Applied Mathematics Educational Resources Based on Edge Computing and Data Stream Classification

Zhou²

2021

Mobile Information Systems

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Facing the massive data of higher education institutions, data mining technology is an intelligent information processing technology that can effectively discover knowledge from the massive data and can discover important information that people have previously ignored from the huge data information. This article is dedicated to the development of applied mathematics education resource mining technology based on edge computing and data stream classification. First of all, this article establishes a resource system architecture suitable for existing applied mathematics education through edge computing technology, which can effectively improve the efficiency of data mining. Secondly, the data stream classification algorithm is used for information extraction and classification integration of massive applied mathematical education data. This method provides potential and valuable information for decision-makers and education practitioners. Finally, the simulation and performance test of the system verify that it has the functions of mathematical information mining and data processing. This system will provide strong support for applied mathematics education reform.

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“…In the field of intelligent computing, based on the development of cloud computing, edge computing is applied to the Internet service environment, and mobile edge computing (MEC) technology is born. MEC is a new type of network structure, running and providing information technology services and cloud computing capabilities [20,21]. Now, this method has become a standardized technology.…”

Section: Overview Of Related Technology Researchmentioning

confidence: 99%

E-Commerce Enterprise Supply Chain Financing Risk Assessment Based on Linked Data Mining and Edge Computing

Liu

Bao

2021

Mobile Information Systems

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In recent years, the rapid development of information technology has affected the way the world economy operates. The emergence of e-commerce has greatly shortened the time and space distance between economic participants and maximized the sharing of resources. However, the financial management and risk assessment capabilities of the existing supply are insufficient to adapt to the rapidly developing new environment. This article uses a combination of normative analysis and empirical analysis to analyze the status quo of the supply chain of small and medium e-commerce companies. First, this article establishes an evaluation framework for the supply chain of e-commerce companies based on edge computing. Second, according to the distribution of the supply chain, this article adds the member’s predetermined quota, reputation, execution time, and other indicators as parameters to establish a fuzzy neural network model. On this basis, combined with the price regression model, the pricing plan is evaluated. The results show that the financing risk obtained by this model differs very little from the actual risk. The above-mentioned model constructs an e-commerce enterprise supply chain financing risk management model that adapts to the environment of the new era.

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Resource Allocation Based on Deep Reinforcement Learning in IoT Edge Computing

Cited by 232 publications

References 29 publications

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Research on Mining of Applied Mathematics Educational Resources Based on Edge Computing and Data Stream Classification

E-Commerce Enterprise Supply Chain Financing Risk Assessment Based on Linked Data Mining and Edge Computing

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