Delay Minimization Offloading for Interdependent Tasks in Energy-Aware Cooperative MEC Networks

Zhu, Yao; Hu, Yulin; Schmeink, Anke

doi:10.1109/wcnc.2019.8885560

Cited by 26 publications

(11 citation statements)

References 16 publications

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“…The solutions [13]- [18] focus on the wireless transmission when optimizing the service caching strategy. The authors in [16] optimized the task offloading, data content caching, computing resources, and transmission power allocation jointly in a MEC-enabled non-orthogonal multiple access (NOMA) network.…”

Section: Related Workmentioning

confidence: 99%

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When the User-Centric Network Meets Mobile Edge Computing: Challenges and Optimization

Qin

2023

IEEE Commun. Mag.

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In the traditional mobile edge computing (MEC) system, the availability of MEC services is greatly limited for the edge users of the cell due to serious signal attenuation and inter-cell interference. User-centric MEC (UC-MEC) can be seen as a promising solution to address this issue. In UC-MEC, each user is served by a dedicated access point (AP) cluster enabled with MEC capability instead of a single MEC server, however, at the expense of more energy consumption and greater privacy risks. To achieve efficient and reliable resource utilization with user-centric services, we propose an energy efficient blockchainenabled UC-MEC system where blockchain operations and resource optimization are jointly performed. Firstly, we design a resource-aware, reliable, replicated, redundant, and faulttolerant (R-RAFT) consensus mechanism to implement secure and reliable resource trading. Then, an optimization framework based on alternating direction method of multipliers (ADMM) is proposed to minimize the total energy consumed by wireless transmission, consensus and task computing, where APs clustering, computing resource allocation and bandwidth allocation are jointly considered. Simulation results show superiority of the proposed UC-MEC system over reference schemes, at most 33.96% reduction in the total delay and 48.77% reduction in the total energy consumption.

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Section: Related Workmentioning

confidence: 99%

“…Existing works [7]- [18] on edge service caching have mostly adopted the cellular-based edge caching networks where multiple users are served by a single MEC-enabled base station (BS) in the cell. However, the performance of edge service caching is greatly limited by the wireless transmission in traditional cellular-based networks.…”

Section: Introductionmentioning

confidence: 99%

When the User-Centric Network Meets Mobile Edge Computing: Challenges and Optimization

Qin

2023

IEEE Commun. Mag.

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“…Nowadays, billions of mobile devices are connected to the Internet. Because researchers usually assume that mobile computation offloading relies on a central cloud, it is a huge challenge for limited computation on the central cloud [11]. So computation offloading is a very pivotal technology for MEC [12].…”

Section: Computationmentioning

confidence: 99%

A Survey on Secure Deployment of Mobile Services in Edge Computing

Cui

Fei

Yin³

2021

Security and Communication Networks

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Mobile edge computing (MEC) is an emerging technology that is recognized as a key to 5G networks. Because MEC provides an IT service environment and cloud-computing services at the edge of the mobile network, researchers hope to use MEC for secure service deployment, such as Internet of vehicles, Internet of Things (IoT), and autonomous vehicles. Because of the characteristics of MEC which do not have terminal servers, it tends to be deployed on the edge of networks. However, there are few related works that systematically introduce the deployment of MEC. Also, secure service deployment frameworks with MEC are even rare. For this reason, we have conducted a comprehensive and concrete survey of recent research studies on secure deployment. Although numerous research studies and experiments about MEC service deployment have been conducted, there are few systematic summaries that conclude basic concepts and development strategies about secure service deployment of commercial MEC. To make up for the gap, a detailed and complete survey about relative achievements is presented.

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“…This work formulated a joint communication, offloading, and computation resource allocation problem and attempted to minimize the computation cost and energy consumption. [31] analyzed an energy-aware MEC network with multiple servers and gave an optimal offloading method for minimizing the processing delay of tasks under energy constraints. This method applied a three-dimensional decision matrix to appoint at the time moment to offload which task to which server.…”

Section: Related Workmentioning

confidence: 99%

Intelligent task migration with deep Qlearning in multi‐access edge computing

Huang

Lin

2021

IET Communications

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Multi-access edge computing provides computation and network resources in proximity to user applications in mobile environments. Deploying edge servers in network boundary can not only offload the heavy task loading on the cloud, but also alleviate resource-limited capabilities of mobile devices. Rather than many stand-alone edge servers, the concept of multi-server edge computing is recently advocated to contend with the issues of system scalability and service quality against dynamic task workload. This study exploits collaborative computing resources and designs a task migration strategy for multiple edge servers in mobile networks. This study formulates a queueing optimization problem of minimizing the overall service time in a multi-server system. An intelligent task migration scheme is then developed using the deep reinforcement learning and Q-learning techniques. With a variety of numerical attributes derived from the queueing model, this intelligent scheme can arrange the task distribution among edge servers to enhance the task processing capability. Simulation-based results show that the proposed task migration scheme can sustain service efficiency and resource utilization, which is promising as compared with conventional designs without collaborative intelligence in mobile environments.

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Delay Minimization Offloading for Interdependent Tasks in Energy-Aware Cooperative MEC Networks

Cited by 26 publications

References 16 publications

When the User-Centric Network Meets Mobile Edge Computing: Challenges and Optimization

When the User-Centric Network Meets Mobile Edge Computing: Challenges and Optimization

A Survey on Secure Deployment of Mobile Services in Edge Computing

Intelligent task migration with deep Qlearning in multi‐access edge computing

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