Resource Management using Machine Learning in Mobile Edge Computing: A Survey

Zamzam, Marwa; Elshabrawy, Tallal; Ashour, Mohamed

doi:10.1109/icicis46948.2019.9014733

Cited by 18 publications

(10 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Reference [15], the advantage is cloud workload protection and the disadvantage is to dedicate and specialized client-server applications for proper functionality. Zamzam et al [69] discussed resource management using ML in MEC to solve the issue and improve performance. In their reseach, the advantages are improved performance and secure resource management, and the disadvantages are network and optimization error.…”

Section: Discussion and Lessons Learnedmentioning

confidence: 99%

“…It is challenging to provide an ideal answer for an asset to the executives in a unique framework due to the random varieties of undertakings required by the clients and the portability of these clients, therefore, artificial intelligence (AI) strategies are proposed to take care of this optimization issue. Zamzam et al [69] discussed resource management using ML in mobile edge computing (MEC) to solve the issue and improve performance. The authors cutting-edge AI to advance resources in portable edge processing.…”

Section: Supervised Annsmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Machine Learning Algorithms for Cloud Computing Security

et al. 2020

View full text Add to dashboard Cite

Cloud computing (CC) is on-demand accessibility of network resources, especially datastorage and processing power, without special and direct management by the users. CC recentlyhas emerged as a set of public and private datacenters that offers the client a single platform acrossthe Internet. Edge computing is an evolving computing paradigm that brings computation andinformation storage nearer to the end-users to improve response times and spare transmissioncapacity. Mobile CC (MCC) uses distributed computing to convey applications to cell phones.However, CC and edge computing have security challenges, including vulnerability for clients andassociation acknowledgment, that delay the rapid adoption of computing models. Machine learning(ML) is the investigation of computer algorithms that improve naturally through experience. In thisreview paper, we present an analysis of CC security threats, issues, and solutions that utilizedone or several ML algorithms. We review different ML algorithms that are used to overcomethe cloud security issues including supervised, unsupervised, semi-supervised, and reinforcementlearning. Then, we compare the performance of each technique based on their features, advantages,and disadvantages. Moreover, we enlist future research directions to secure CC models.

show abstract

Section: Discussion and Lessons Learnedmentioning

confidence: 99%

Section: Supervised Annsmentioning

confidence: 99%

A Review of Machine Learning Algorithms for Cloud Computing Security

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Several surveys about MEC exist in the literature. They are either general [6,18,19,20] or focus on different aspects and methods [21,22,23]. Mao et al [6] introduce MEC with the modelling of MEC communication and computation, mobile devices and edge server.…”

Section: Related Workmentioning

confidence: 99%

“…They discuss the state-of-art methods and technical service hosting solutions. Zamzam et al [23] propose a resource management survey using machine learning methods. They organize the research by goals and classify machine learning methods.…”

Section: Related Workmentioning

confidence: 99%

A resource management survey for mission-critical and time-critical applications in multiaccess edge computing

Santi¹,

Mitton²

2021

ITU-J FET

View full text Add to dashboard Cite

Multiaccess Edge Computing (MEC) brings additional computing power in proximity of mobile users, reducing latency, saving energy and alleviating the network's bandwidth. This proximity is beneficial, especially for mission-critical applications where each second matters, such as disaster management or military operations. Moreover, it enables MEC resources embedded on mobile units like drones or robots that are flexible to be deployed for mission-critical applications. However, the MEC servers are capacity-limited and thus need an acute management of their resources. The mobile resources also need a smart deployment scheme to deliver their services efficiently. In this survey, we review mission-critical applications, resource allocation and deployment of mobile resources techniques in the context of the MEC. First, we introduce the technical specifics and uses of MEC in mission-critical applications to highlight their needs and requirements. Then, we discuss the resource allocation schemes for MEC and assess their fit depending on the application needs. In the same fashion, we finally review the deployment of MEC mobile resources. We believe this work could serve as a helping hand to design efficient MEC resource management schemes that respond to challenging environments such as mission-critical applications.

show abstract

“…At the same time, the pressure faced by Internet content providers as well as core networks can be relieved effectively. In fact, the network performance improvement in MEC strictly depends on the tasks offloading decision [14]. Furthermore, the decision problem usually involves some necessary factors, such as network bandwidth, timing sequence of application program, and dependence between tasks.…”

Section: Introductionmentioning

confidence: 99%

DRL-Based Edge Computing Model to Offload the FIFA World Cup Traffic

Che

2020

Mobile Information Systems

View full text Add to dashboard Cite

In recent years, the volume of global video traffic has been increasing rapidly and it is considerably significant to offload the traffic during the process of video transmission and improve the experience of users. In this paper, we propose a novel traffic offloading strategy to provide a feasible and efficient reference for the following 2022 FIFA World Cup held in Qatar. At first, we present the system framework based on the Mobile Edge Computing (MEC) paradigm, which supports transferring the FIFA World Cup traffic to the mobile edge servers. Then, the Deep Reinforcement Learning (DRL) is used to provide the traffic scheduling method and minimize the scheduling time of application programs. Meanwhile, the task scheduling operation is regarded as the process of Markov decision, and the proximal policy optimization method is used to train the Deep Neural Network in the DRL. For the proposed traffic offloading strategy, we do the simulation based on two real datasets, and the experimental results show that it has smaller scheduling time, higher bandwidth utilization, and better experience of user than two baselines.

show abstract

Resource Management using Machine Learning in Mobile Edge Computing: A Survey

Cited by 18 publications

References 17 publications

A Review of Machine Learning Algorithms for Cloud Computing Security

A Review of Machine Learning Algorithms for Cloud Computing Security

A resource management survey for mission-critical and time-critical applications in multiaccess edge computing

DRL-Based Edge Computing Model to Offload the FIFA World Cup Traffic

Contact Info

Product

Resources

About