Latency-aware service migration with decision theory for Internet of Vehicles in mobile edge computing

Liu, Zhong‐Jian; Huang, Qihe

doi:10.1007/s11276-022-02978-y

Cited by 6 publications

(3 citation statements)

References 46 publications

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“…The literature [32] developed an optimal iterative relaxation-and-rounding based solution approach to minimize the migration cost. Considering the high mobility of vehicles, Considering the high mobility of vehicles, The authors transform the service migration problem into an uncertain decision optimization problem and designed a a Latency-aware S ervice M igration method with Decision theory (LSMD) [21]. However, it is difficult to achieve fast convergence of the algorithm due to the traditional heuristic algorithm's high time complexity.…”

Section: Service Migration In Edge Computing (Ec)mentioning

confidence: 99%

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Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Networks

Bai

et al. 2022

Preprint

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With the booming proliferation of user requests in the Internet of Things (IoT) network, Edge Computing (EC) is emerging as a promising paradigm to provide flexible and reliable services. Considering the resource constraints of IoT devices, for some delay-aware user requests, a heavy workload IoT device may not respond on time. EC spurs a popular wave of offloading user requests to edge servers at the edge of the network. The orchestration for user-requested offloading schemes is a remarkable challenge for the execution time and energy consumption for IoT devices in edge networks. To address this challenge, we propose a dynamic computation offloading strategy consisting of the following two parts: (i) we propose the concept of intermediate nodes, which can minimize the time and energy cost to process the current task by dynamically combining task offloading and service migration strategies; and (ii) based in the current network’s workload state, we model such intermediate node selection problem as multiple dimensional Markov Decision Process (MDP) spaces and implement deep reinforcement learning algorithms to reduce the large MDP space and achieve fast decision-making. Experimental results show that this strategy performs better than existing baseline approaches on the reduction of delay and energy consumption.

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Section: Service Migration In Edge Computing (Ec)mentioning

confidence: 99%

“…An autonomous bandwidth allocation approach is proposed to support the low-latency service migration [20]. The authors used a decision theory based algorithm to find an optimal service migration path [21]. Most of the existing works concentrate on finding an optimal algorithm to lower the delay of task offloading or service migration.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Networks

Bai

et al. 2022

Preprint

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“…With the construction of a power supply company's power dispatching data network, the main station has been consecutively connected to all county dispatching services, resulting in further convergence and an increase in information flow in the main station, which puts considerable pressure on the dispatching data network and the main station dispatching automation system [19,20]. Due to access to county transfer automation business, the operation analysis report of power dispatch automation shows that many hidden defects are generated in the information flow, which is very difficult to trace and analyze due to the large amount of data and fast disappearance of information flow [21,22].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges

Luo¹,

Li²,

Yang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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The emergence of power dispatching automation systems has greatly improved the efficiency of power industry operations and promoted the rapid development of the power industry. However, with the convergence and increase in power data flow, the data dispatching network and the main station dispatching automation system have encountered substantial pressure. Therefore, the method of online data resolution and rapid problem identification of dispatching automation systems has been widely investigated. In this paper, we perform a comprehensive review of automated dispatching of massive dispatching data from the perspective of intelligent identification, discuss unresolved research issues and outline future directions in this area. In particular, we divide intelligent identification over power big data into data acquisition and storage processes, anomaly detection and fault discrimination processes, and fault tracing for dispatching operations during communication. A detailed survey of the solutions to the challenges in intelligent identification over power big data is then presented. Moreover, opportunities and future directions are outlined.

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Dynamic Computation Offloading Leveraging Horizontal Task Offloading and Service Migration in Edge Networks

Bai,

Zhou,

2023

Communications in Computer and Information Science

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Latency-aware service migration with decision theory for Internet of Vehicles in mobile edge computing

Cited by 6 publications

References 46 publications

Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Networks

Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Networks

Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges

Dynamic Computation Offloading Leveraging Horizontal Task Offloading and Service Migration in Edge Networks

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