Energy-Aware Scheduling in Edge Computing Based on Energy Internet

Zhang, Qing; Lin, Xiaoyong; Hao, Yongsheng; Cao, Jie

doi:10.1109/access.2020.3044932

Cited by 10 publications

(2 citation statements)

References 39 publications

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“…Therefore, many energy-saving areas are being explored. Following the development of edge computing, cloud computing, and 5G networks, the most recent focus includes migration methods and scheduling certain tasks from local devices to remote cloud processing [38,39], energy management systems focused on security [40], task allocation algorithms in multi-cloud networks [41], and energy-aware video streaming for mobile devices [42,43]. Increasingly more works are also related to energy saving in the context of Internet of Things networks, e.g., [44][45][46].…”

Section: Related Workmentioning

confidence: 99%

Fluid-Flow Approximation in the Analysis of Vast Energy-Aware Networks

Nycz

Czachórski

2021

Mathematics

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The paper addresses two issues: (i) modeling dynamic flows transmitted in vast TCP/IP networks and (ii) modeling the impact of energy-saving algorithms. The approach is based on the fluid-flow approximation, which applies first-order differential equations to analyze the evolution of queues and flows. We demonstrate that the effective implementation of this method overcomes the constraints of storing large data in numerical solutions of transient problems in vast network topologies. The model is implemented and executed directly in a database system. It can analyze transient states in topologies of more than 100,000 nodes, i.e., the size which was not considered until now. We use it to investigate the impact of an energy-saving algorithm on the performance of a vast network. We find that it reduces network congestion and save energy costs but significantly lower network throughput.

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Section: Related Workmentioning

confidence: 99%

Fluid-Flow Approximation in the Analysis of Vast Energy-Aware Networks

Nycz

Czachórski

2021

Mathematics

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“…Thus, need to achieve green computing by reducing the power consumption of the computational cloud. In this context, in recent literature [1] [2][3], we found VM (virtual machine) workload scheduling can be a good strategy to efficiently utilize the computational resources and reducing power consumption of cloud servers.…”

Section: Introductionmentioning

confidence: 99%