Smart contract service migration mechanism based on container in edge computing

Yin, Luxiu; Li, Pengfei; Luo, Juan

doi:10.1016/j.jpdc.2021.02.023

Cited by 15 publications

(6 citation statements)

References 10 publications

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“…(3) while the set of VSF k detection ants is not empty do (4) Divide ants into several groups according to the SSE i of each ant and the distributed VSF k concentration (5) Forward different groups to different adjacent nodes of the current node according to equation ( 5); (6) for each DA i do (7) ifVSF k is found then (8) Add this node into SSE i ; (9) Return back to SE u according to SSE i ; (10) Update VSF k concentrations on links according to equation ( 2); (11) Add this node (i.e., ECS) into ECS VSF k ; (12) else ifHDA i ≥ 1then (13) Add this node into SSE i ; (14) Join the next-hop searching; (15) else ifHDA i � 0then ( 16) Die without any feedback; (17) end if (18) end for (19) requests is low, the SPDs of AVDP and AMSP are 0.079 s and 0.085 s, respectively. When the number of service requests reaches the maximum, the SPD of AVDP just increases by 0.061 s, while the SPD of AMSP increases by 0.125 s. Moreover, when the network load is the heaviest, the SPD of AVDP reduces to 0.132 s with the time period increasing; that is, the SPD of AVDP is optimized with time, while that of AMSP increases to 0.235 s in the same situation.…”

Section: E Simulation Resultsmentioning

confidence: 99%

“…Meanwhile, the VSF k concentrations on the corresponding searching links update and the current node (i.e., ECS) is added into ECS VSF k (lines 6-11). Else if HDA i of the ant is not zero, the ant adds this node into its SSE i and joins to the nexthop searching (lines [12][13][14]. Otherwise, the ant dies without any feedback (lines 15-16).…”

Section: E Forwardingmentioning

confidence: 99%

“…Some types of research have been done on service placement and migration over edge computing scenarios (e.g., [13][14][15][16]). In [13], it introduces a framework for optimal placement of service replicas proactively in the 5G edge network.…”

Section: Introductionmentioning

confidence: 99%

“…It deploys the multimedia service instances on the trajectory edge nodes by integrating the user's path prediction model, so as to provide an optimal deployment technique that traded off between maximizing the QoE and minimizing the deployment cost. In [14], it studies the container-based service migration problem in edge computing and proposes a service migration mechanism based on mobility awareness. Its service migration mechanism firstly triggers the service migration according to the service density of the current node and then selects the service and the corresponding destination node for the optimization object to minimize the service delay.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic Detection and Placement for VSFs over Edge Computing Scenarios: An ACO-Based Approach

Zhang

2022

Security and Communication Networks

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As an extension of cloud computing, the edge computing has become an important pattern to deal with novel service scenarios of the Internet of Everything (IoE), especially for the rapidly increasing different kinds of service requests from edge equipment. It is still a great challenge to satisfy the demands of delay-sensitive applications, so as to optimize the service provision delay for edge equipment under 5G. In this paper, by introducing virtualized service functions (VSFs) into edge computing pattern, the mechanism of service function detection and placement among multiple Edge Computing Servers (ECSs) is proposed. We firstly improve the Ant colony optimization (ACO) method to adapt to the situation that the service requests may frequently change from different edge network domains. Based on the improved ACO, a scheme of searching for the locations (i.e., ECSs) of the requested service functions is devised, so as to optimize the service searching delay. Then, a service function placement scheme is presented, and it deploys most of appropriate service functions in each ECS by predicting the future requested frequencies of functions, which further reduces the overall service provision delay. In addition, it also improves the ECS computing capacity utilization. The simulation results show that the proposed mechanism is feasible and effective.

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Section: E Simulation Resultsmentioning

confidence: 99%

Section: E Forwardingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Detection and Placement for VSFs over Edge Computing Scenarios: An ACO-Based Approach

Zhang

2022

Security and Communication Networks

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“…It randomly generates uniform discrete values and then determines the devices to be migrated according to these values. In [20], a service migration mechanism based on mobile awareness was proposed to solve the problem of multinode service migration in smart contract edge computing. By optimizing business selection and finding the corresponding destination nodes, delays in business and service can be reduced, and the QoS can be improved.…”

Section: Service Migration In Edge Computingmentioning

confidence: 99%

Service Reliability Based on Fault Prediction and Container Migration in Edge Computing

Liu,

Kang,

et al. 2023

Applied Sciences

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With improvements in the computing capability of edge devices and the emergence of edge computing, an increasing number of services are being deployed on the edge side, and container-based virtualization is used to deploy services to improve resource utilization. This has led to challenges in reliability because services deployed on edge nodes are pruned owing to hardware failures and a lack of technical support. To solve this reliability problem, we propose a solution based on fault prediction combined with container migration to address the service failure problem caused by node failure. This approach comprises two major steps: fault prediction and container migration. Fault prediction collects the log of services on edge nodes and uses these data to conduct time-sequence modeling. Machine-learning algorithms are chosen to predict faults on the edge. Container migration is modeled as an optimization problem. A migration node selection approach based on a genetic algorithm is proposed to determine the most suitable migration target to migrate container services on the device and ensure the reliability of the services. Simulation results show that the proposed approach can effectively predict device faults and migrate services based on the optimal container migration strategy to avoid service failures deployed on edge devices and ensure service reliability.

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CSADE: a delay-sensitive scheduling method based on task admission and delay evaluation on edge–cloud collaboration

Zuo

et al. 2023

Cluster Comput

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Smart contract service migration mechanism based on container in edge computing

Cited by 15 publications

References 10 publications

Dynamic Detection and Placement for VSFs over Edge Computing Scenarios: An ACO-Based Approach

Dynamic Detection and Placement for VSFs over Edge Computing Scenarios: An ACO-Based Approach

Service Reliability Based on Fault Prediction and Container Migration in Edge Computing

CSADE: a delay-sensitive scheduling method based on task admission and delay evaluation on edge–cloud collaboration

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