Multi‐tier MEC offloading strategy based on dynamic channel characteristics

Li, Nana; Yang, Shiwen; Wang, Zhaoyang; Hao, Wanming; Zhu, Yifan

doi:10.1049/iet-com.2020.0371

Cited by 9 publications

(4 citation statements)

References 35 publications

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“…Computation offloading strategies have been studied extensively [116][117][118][119] and generally fall under two categories, full offloading (coarse-grained) [120] and partial offloading (fine-grained) [91], [121][122][123][124]. For coarse-grained mode, the data set of a task has to be executed as a whole either locally or remotely on an edge server.…”

Section: Offloading Strategiesmentioning

confidence: 99%

A Review of the Current Task Offloading Algorithms, Strategies and Approach in Edge Computing Systems

Acheampong¹,

Zhang²,

Xu³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Task offloading is an important concept for edge computing and the Internet of Things (IoT) because computationintensive tasks must be offloaded to more resource-powerful remote devices. Task offloading has several advantages, including increased battery life, lower latency, and better application performance. A task offloading method determines whether sections of the full application should be run locally or offloaded for execution remotely. The offloading choice problem is influenced by several factors, including application properties, network conditions, hardware features, and mobility, influencing the offloading system's operational environment. This study provides a thorough examination of current task offloading and resource allocation in edge computing, covering offloading strategies, algorithms, and factors that influence offloading. Full offloading and partial offloading strategies are the two types of offloading strategies. The algorithms for task offloading and resource allocation are then categorized into two parts: machine learning algorithms and non-machine learning algorithms. We examine and elaborate on algorithms like Supervised Learning, Unsupervised Learning, and Reinforcement Learning (RL) under machine learning. Under the non-machine learning algorithm, we elaborate on algorithms like non(convex) optimization, Lyapunov optimization, Game theory, Heuristic Algorithm, Dynamic Voltage Scaling, Gibbs Sampling, and Generalized Benders Decomposition (GBD). Finally, we highlight and discuss some research challenges and issues in edge computing.

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Section: Offloading Strategiesmentioning

confidence: 99%

A Review of the Current Task Offloading Algorithms, Strategies and Approach in Edge Computing Systems

Acheampong¹,

Zhang²,

Xu³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…, the Bedman equation can be obtained through the above Q optimal function, as shown in formula (12):…”

Section: Based On the Drl Offloading Policymentioning

confidence: 99%

“…However, some unmanned aircraft cannot meet the requirements of intensive operations. UAV energy collection, causality and speed limit, documents [12] proposed the use of two-stage algorithm and three-stage alternative algorithm respectively. The algorithm is fast and low complexity.…”

Section: Introductionmentioning

confidence: 99%

Offloading strategy for UAV power inspection task based on deep reinforcement learning

Tong

Shan

et al. 2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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Due to the limitation of computer capacity and energy of equipment, unmanned equipment cannot perform intensive computer tasks well during emergency failure inspection. In order to solve the above problems, this paper proposes a task waste strategy based on deep reinforcement learning (DRL), which is mainly applicable to several UAVs and individual ES scenarios. First of all, an end edge cloud cooperative unloading architecture is built in the edge environment of UAV, and the problem of unloading tasks is classified as an optimization problem to achieve the minimum delay under the limit of the computing and communication resources of the edge server (ES). Secondly, the problem is constructed as Markov decision, and deep Q network (DQN) is used to solve the optimization problem, and experience playback mechanism and greedy algorithm are introduced into the learning process. Experiments show that the mitigation strategy has lower latency and higher reliability.

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“…Recent studies addressed the multi-server MEC model to efficiently use limited and shared computation resources on edge servers. [11] organized mobile devices, MEC edge, and helper MEC edge in a three-layer network architecture where the computing data can be partially processed at the MEC edge and helper MEC edge. This work formulated a joint communication, offloading, and computation resource allocation problem and attempted to minimize the computation cost and energy consumption.…”

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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Multi‐tier MEC offloading strategy based on dynamic channel characteristics

Cited by 9 publications

References 35 publications

A Review of the Current Task Offloading Algorithms, Strategies and Approach in Edge Computing Systems

A Review of the Current Task Offloading Algorithms, Strategies and Approach in Edge Computing Systems

Offloading strategy for UAV power inspection task based on deep reinforcement learning

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

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