Efficient Computing Resource Sharing for Mobile Edge-Cloud Computing Networks

Zhang, Yongmin; Lan, Xiaolong; Ren, Ju; Cai, Lin

doi:10.1109/tnet.2020.2979807

Cited by 162 publications

(53 citation statements)

References 41 publications

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“…Wang et al [26] considered the task state information and the channel state information. Zhang et al [27] considered the social welfare maximization and profit maximization. Chen et al [28] considered the multiple resources and proposed an optimal demand computation algorithm, but they did not consider the binary computation offloading mode.…”

Section: A Related Workmentioning

confidence: 99%

Energy-Efficient Task Allocation of Heterogeneous Resources in Mobile Edge Computing

Liu

2021

IEEE Access

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The edge cloud provides heterogeneous resources, such as cores, memory, and storage which are then allocated to mobile applications in mobile edge computing, which require multiple types of resources to execute. While current work on mobile edge computing focuses on tasks needing a single resource to run, we investigate the task allocation problem aiming to minimize total energy consumption while considering heterogeneous resource settings. Specifically, we consider the binary computation offloading mode, in which a task executes successfully as a whole. We formulate this as an integer programming problem, and transform that to the two subproblems of the resource allocation and the offloading decision. We propose a heuristic approach to solve the resource allocation subproblem and an approximation algorithm for the offloading decision subproblem. We show that our proposed approximation algorithm is a polynomial time approximation scheme, and hence it achieves a tradeoff between optimality loss and time complexity. To the best of our knowledge, this is the first study of task allocation considering both multiple types of resources and binary computation offloading mode. Experimental results demonstrate that the performance of our proposed algorithm scales very well for multi-resource allocation in different conditions, and it achieves a good balance of performance and speed.INDEX TERMS Mobile edge computing, computation offloading, resource allocation, energy efficient, polynomial time approximation scheme.

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Section: A Related Workmentioning

confidence: 99%

Energy-Efficient Task Allocation of Heterogeneous Resources in Mobile Edge Computing

Liu

2021

IEEE Access

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“…Zhao et al [24] employed a vehicle network comprising edge and cloud, and tried to optimize cloud utilization using convex optimization. Zhang et al [25,26] used whole-sale and buy-back models between edge and cloud to share computing resources, and optimized the profit from the edge's perspective. Ruan et al [27] assumed an energy management infrastructure as a cloud model comprising three tiers, and optimized for latency using joint optimization between Stackelberg and Lyapunov-based pricing and energy demand.…”

Section: Previous Workmentioning

confidence: 99%

Comparative Analysis of 5G Mobile Communication Network Architectures

et al. 2020

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Mobile communication technology is evolving from 4G to 5G. Compared to previous generations, 5G has the capability to implement latency-critical services, such as autonomous driving, real-time AI on handheld devices and remote drone control. Multi-access Edge Computing is one of the key technologies of 5G in guaranteeing ultra-low latency aimed to support latency critical services by distributing centralized computing resources to networks edges closer to users. However, due to its high granularity of computing resources, Multi-access Edge Computing has an architectural vulnerability in that it can lead to the overloading of regional computing resources, a phenomenon called regional traffic explosion. This paper proposes an improved communication architecture called Hybrid Cloud Computing, which combines the advantages of both Centralized Cloud Computing and Multi-access Edge Computing. The performance of the proposed network architecture is evaluated by utilizing a discrete-event simulation model. Finally, the results, advantages, and disadvantages of various network architectures are discussed.

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“…Because of the widespread applications of SIoT, millions of sensors and devices continue to generate data and exchange important information [4]. In order to alleviate the problem of resource congestion, more and more service providers choose collaborative edge computing (CEC) [5], [6], which migrates data computation and storage to the network edge near the users [7]. Therefore, various nodes distributed on the network can offload computation away from the centralized data center, which can significantly reduce the waiting time of the message exchange.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach

Nie

Wang

et al. 2022

IEEE Trans. Comput. Soc. Syst.

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The Social Internet of Things (SIoT) now penetrates our daily lives. As a strategy to alleviate the escalation of resource congestion, collaborative edge computing (CEC) has become a new paradigm for solving the needs of the Internet of Things (IoT). CEC can provide computing, storage, and network connection resources for remote devices. Because the edge network is closer to the connected devices, it involves a large amount of users' privacy. This also makes edge networks face more and more security issues, such as Denial-of-Service (DoS) attacks, unauthorized access, packet sniffing, and man-inthe-middle attacks. To combat these issues and enhance the security of edge networks, we propose a deep learning-based intrusion detection algorithm. Based on the generative adversarial network (GAN), we designed a powerful intrusion detection method. Our intrusion detection method includes three phases. First, we use the feature selection module to process the collaborative edge network traffic. Second, a deep learning architecture based on GAN is designed for intrusion detection aiming at a single attack. Finally, we propose a new intrusion detection model by combining several intrusion detection models that aim at a single attack. Intrusion detection aiming at multiple attacks is realized through the designed GAN-based deep learning architecture. Besides, we provide a comprehensive evaluation to verify the effectiveness of the proposed method. Index Terms-Collaborative edge computing (CEC), generative adversarial network (GAN), intrusion detection, social internet of things (SIoT).

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Efficient Computing Resource Sharing for Mobile Edge-Cloud Computing Networks

Cited by 162 publications

References 41 publications

Energy-Efficient Task Allocation of Heterogeneous Resources in Mobile Edge Computing

Energy-Efficient Task Allocation of Heterogeneous Resources in Mobile Edge Computing

Comparative Analysis of 5G Mobile Communication Network Architectures

Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach

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