A delay-sensitive resource allocation algorithm for container cluster in edge computing environment

Guo, Shaoyong; Zhang, Ke‐Qin; Gong, Bei; He, Wenchen; Qiu, Xuesong

doi:10.1016/j.comcom.2021.01.020

Cited by 17 publications

(5 citation statements)

References 15 publications

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“…The container mirroring platform is responsible for backing up various data and performing functional analysis. The intelligent edge platform is responsible for the management, control, and adjustment of all edge nodes to ensure the healthy operation of edge nodes [ 29 ].…”

Section: Analysis Of the Theory And Methods Of Ec Applied To Urban Sc...mentioning

confidence: 99%

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The Usage of Designing the Urban Sculpture Scene Based on Edge Computing

Zhu

2022

Computational Intelligence and Neuroscience

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To not only achieve the goal of urban cultural construction but also save the cost of urban sculpture space design, EC (edge computing) is combined with urban sculpture space design and planning first. Then it briefly discusses the service category, system architecture, advantages, and characteristics of urban sculpture, as well as the key points and difficulties of its construction, and the layered architecture of EC for urban sculpture spaces is proposed. Secondly, the cloud edge combination technology is adopted, and the urban sculpture is used as a specific function of the edge system node to conduct an in-depth analysis to build an urban sculpture safety supervision system architecture platform. Finally, the actual energy required for implementation is predicted and evaluated, the specific monitoring system coverage is set up, and some equations are made for calculating the energy consumption of the monitored machines according to the number of devices and route planning required by the urban sculpture safety supervision system. An optimization algorithm for energy consumption is proposed based on reinforcement learning and compared with the three control groups. The results show that when the seven monitoring devices cover detection points less than 800, the required energy consumption increases linearly. When the detection devices cover more than 800 detection points, the required energy consumption is stable and varies from 10000 to 12000; that is, when the number of monitoring devices is 7, the optimal number of monitoring points is about 800. When the number of detection points is fixed, increasing the number of monitoring devices in a small range can reduce the total energy consumption. The optimization algorithm based on the reinforcement learning proposal can obtain an approximate optimal solution. The research results show that the combination of edge computing and urban sculpture can expand the function of urban sculpture and make it serve people better.

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Section: Analysis Of the Theory And Methods Of Ec Applied To Urban Sc...mentioning

confidence: 99%

“…e intelligent edge platform is responsible for the management, control, and adjustment of all edge nodes to ensure the healthy operation of edge nodes [29].…”

Section: System Construction Of Ec In the Application Of Urbanmentioning

confidence: 99%

The Usage of Designing the Urban Sculpture Scene Based on Edge Computing

Zhu

2022

Computational Intelligence and Neuroscience

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“…Each abstraction (either vertical or horizontal) adds another level of complexity and delays, affecting QoS. The delays can depend on each edge node's virtualization and containerization techniques [56]. Additionally, many QoS (latency and processing delays) metrics depend on the current load of the local physical/virtual CPU/memory, network acceleration and service invocation techniques [56], [57].…”

Section: Related Workmentioning

confidence: 99%

Resilient Edge: Building an Adaptive and Resilient Multi-Communication Network for IoT Edge Using LPWAN and WiFi

Kumar

Yadav

Indrusiak

2023

IEEE Trans. Netw. Serv. Manage.

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“…On the basis of the data acquisition model of temperature, vibration, impact, and other parameters of the cytopathology image storage system constructed above, and the balanced deployment of nodes of the cytopathology image storage system by the distributed grid equilibrium configuration method, the delay allocation algorithm of network data transmission was designed. 26 A time delay allocation algorithm for data transmission in the cytopathology image storage system based on time slot window interval equalization control was proposed, and a control model for data acquisition and shortest path optimization in the cytopathology image storage system was established. Assume that the adaptive weighted weight of transmission delay allocation in the cytopathology image storage system is, and the output bit sequence recombination method is used to configure the transmission delay in the process of data acquisition in the cytopathology image storage system, and the distribution characteristic sequence of sensor nodes is…”

Section: Balance Control Of Time Slot Window Intervalmentioning

confidence: 99%