Deep Reinforcement Learning Based Service Migration Strategy for Edge Computing

Gao, Zhipeng; Jiao, Qidong; Xiao, Kaile; Wang, Qian; Mo, Zijia; Yang, Yang

doi:10.1109/sose.2019.00025

Cited by 45 publications

(22 citation statements)

References 18 publications

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“…In this section, we compare our algorithm with the following four other methods: (1) Service Offloading in SDN (SO) 23 in which algorithm services migrate to the nearest MEC node at every opportunity over a long period of time, (2) the method which migration policy based on deep Q-learning (DQN) is applied, 19 (3) never migrate service (non-migration), and (4) genetic algorithm is used to solve the problem of data placement in cloud for online social networks. 24…”

Section: Simulation Settingmentioning

confidence: 99%

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A dynamic service migration strategy based on mobility prediction in edge computing

Rui

Wang

et al. 2021

International Journal of Distributed Sensor Networks

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Mobile edge computing is a new computing paradigm, which pushes cloud computing capabilities away from the centralized cloud to the network edge to satisfy the increasing amounts of low-latency tasks. However, challenges such as service interruption caused by user mobility occur. In order to address this problem, in this article, we first propose a multiple service placement algorithm, which initializes the placement of each service according to the user’s initial location and their service requests. Furthermore, we build a network model and propose a based on Lyapunov optimization method with long-term cost constraints. Considering the importance of user mobility, we use the Kalman filter to correct the user’s location to improve the success rate of communication between the device and the server. Compared with the traditional scheme, extensive simulation results show that the dynamic service migration strategy can effectively improve the service efficiency of mobile edge computing in the user’s mobile scene, reduce the delay of requesting terminal nodes, and reduce the service interruption caused by frequent user movement.

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Section: Simulation Settingmentioning

confidence: 99%

“…Using their solution, the migration policies can be learned with respect to a large variety of optimization goals. Gao et al 19 designed a reinforcement learning–based framework for a single-user edge computing service migration system. And they take many requirements into account, but not user movement and link change.…”

Section: Related Workmentioning

confidence: 99%

A dynamic service migration strategy based on mobility prediction in edge computing

Rui

Wang

et al. 2021

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…Step 3. 11for each Constraint rules r ∈ R do (12) Step 3.1. If r is a dependency rule, an agent Ag depends on at least one agent in the A′ set, do (13) Step 3.1.1.…”

Section: Service Migration Decision Algorithm Based On Mobilementioning

confidence: 99%

“…end if (12) ℵ � getNetworkCondation(RTT, PacketLoss); (13) β � getCpuLoadRatio(); (14) c � getMemoryLoadRatio(); (15)…”

Section: Blockchainunclassified

“…ey have established a dynamic migration platform for edge services based on mobile user behavior cognition, which can predict user behavior and better guide service migration based on traffic data and network resource environment. Literature [12] is dedicated to determining the migration strategy and communication strategy through the reinforcement learning model, so as to solve the problem of edge service migration. However, most of the abovementioned research on edge computing uses virtual machines (VMs) as the carrier of migration.…”

Section: Introductionmentioning

confidence: 99%

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Falcon: A Blockchain-Based Edge Service Migration Framework in MEC

Zhang

Yang

et al. 2020

Mobile Information Systems

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Driven by advanced 5G cellular systems, mobile edge computing (MEC) has emerged as a promising technology that can meet the energy efficiency and latency requirements of IoT applications. Edge service migration in the MEC environment plays an important role in ensuring user service quality and enhancing terminal computing capabilities. Application services on the edge side should be migrated from different edge servers to edge nodes closer to users, so that services follow users and ensure high-quality services. In addition, during the migration process, edge services face security challenges in an edge network environment without centralized management. To tackle this challenge, this paper innovatively proposes a blockchain-based security edge service migration framework, Falcon, which uses mobile agents different from VM and container as edge service carriers, making migration more flexible. Furthermore, we considered the dependencies between agents and designed a service migration algorithm to maximize the migration benefits and obtain better service quality. In order to ensure the migration of edge services in a safe and reliable environment, Falcon maintains an immutable alliance chain among multiple edge clouds. Finally, the experimental results show that “Falcon” has lower energy consumption and higher service quality.

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Research on image processing of electric power system terminals based on reinforcement learning and mobile edge computing optimization

Zhou,

Yu,

Luo

et al. 2024

Adv Control Appl

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This research is dedicated to the optimization of power system terminal image processing based on RL and MEC. With the continuous development of power system, the demand for image processing of terminal equipment is increasing day by day. However, traditional image processing methods have the problems of high computing complexity and real‐time and energy consumption. To solve this problem, this study introduces the idea of RL and MEC to improve the efficiency and performance of image processing of power system terminals. By modeling and optimizing the image processing task of the power system terminal equipment, the intelligent adjustment of the processing parameters is realized to adapt to the needs of different scenarios. MEC technology is introduced to move image processing tasks from the central server to the edge device, reducing data transmission delay and network burden, thus improving real‐time performance and reducing energy consumption. The experimental results show that the proposed optimization method based on RL and MEC has a significant performance improvement compared with the traditional method in the power system terminal image processing. The framework our proposed has achieved significant improvement in task completion latency, achieving higher system energy efficiency compared to traditional methods.

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Deep Reinforcement Learning Based Service Migration Strategy for Edge Computing

Cited by 45 publications

References 18 publications

A dynamic service migration strategy based on mobility prediction in edge computing

A dynamic service migration strategy based on mobility prediction in edge computing

Falcon: A Blockchain-Based Edge Service Migration Framework in MEC

Research on image processing of electric power system terminals based on reinforcement learning and mobile edge computing optimization

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