2018Unpublished venueRequires access

Improvement of Efficient Scheduling Algorithm for Network Massive Resources

Jing-Jing He

Open publisher page 1 citations

Abstract

In the embedded network design and application, we need to optimize the network resources scheduling design, improve the network process management and memory management efficiency, a network mass resource scheduling algorithm based on double threshold load balancing control is proposed. The model of network mass resource information flow is constructed, and the time scale feature of network mass resource information flow is extracted, and the time axis of network mass resource scheduling is divided into evenly distributed time windows. By using linear coding control method, network mass resource scheduling is implemented simultaneously to realize clustering equilibrium, which improves the anti-interference ability of network mass resource scheduling, and the improved design of network mass resource scheduling algorithm is realized. The simulation results show that the equalization control ability and throughput of mass network resource scheduling using this method are good.

About this research paper

What this paper is about

In the embedded network design and application, we need to optimize the network resources scheduling design, improve the network process management and memory management efficiency, a network mass resource scheduling algorithm based on double threshold load balancing control is proposed. The model of network mass resource information flow is constructed, and the time scale feature of network mass resource information flow is extracted, and the time axis of network mass resource scheduling is divided into evenly distributed time windows. By using linear coding control method, network mass resource scheduling is implemented simultaneously to realize clustering equilibrium, which improves the anti-interference ability of network mass resource scheduling, and the improved design of network mass resource scheduling algorithm is realized. The simulation results show that the equalization control ability and throughput of mass network resource scheduling using this method are good.

Why it matters

OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

In the embedded network design and application, we need to optimize the network resources scheduling design, improve the network process management and memory management efficiency, a network mass resource scheduling algorithm based on double threshold load balancing control is proposed. The model of network mass resource information flow is constructed, and the time scale feature of network mass resource information flow is extracted, and the time axis of network mass resource scheduling is divided into evenly distributed time windows. By using linear coding control method, network mass resource scheduling is implemented simultaneously to realize clustering equilibrium, which improves the anti-interference ability of network mass resource scheduling, and the improved design of network mass resource scheduling algorithm is realized. The simulation results show that the equalization control ability and throughput of mass network resource scheduling using this method are good.

Key concepts: Computer science, Round-robin scheduling, Distributed computing, Fair-share scheduling, Scheduling (production processes), Rate-monotonic scheduling, Dynamic priority scheduling, Two-level scheduling

Related papers

Back to paper searchBrowse research topicsOriginal source
Improvement of Efficient Scheduling Algorithm for Network Massive Resources — Research Paper | ScholarLens