Research of dynamic queue scheduling algorithm

2015 ◽  
pp. 159-162
Keyword(s):  
Scheduling Algorithm ◽  
Queue Scheduling

scholarly journals Load Balancing Using SJF-MMBF and SJF-ELM Approach

2021 ◽  
pp. 74-86
Author(s):  
S. Rekha ◽  
C. Kalaiselvi
Keyword(s):  
Cloud Computing ◽  
Scheduling Algorithm ◽  
Dynamic Environment ◽  
Scheduling Algorithms ◽  
Computing System ◽  
Priority Queue ◽  
Lower Priority ◽  
Cloud Computing System ◽  
Queue Scheduling ◽  
Multi Level

This paper studies the delay-optimal virtual machine (VM) scheduling problem in cloud computing systems, which have a constant amount of infrastructure resources such as CPU, memory and storage in the resource pool. The cloud computing system provides VMs as services to users. Cloud users request various types of VMs randomly over time and the requested VM-hosting durations vary vastly. A multi-level queue scheduling algorithm partitions the ready queue into several separate queues. The processes are permanently assigned to one queue, generally based on some property of the process, such as memory size, process priority or process type. Each queue has its own scheduling algorithm. Similarly, a process that waits too long in a lower-priority queue may be moved to a higher-priority queue. Multi-level queue scheduling is performed via the use of the Particle Swarm Optimization algorithm (MQPSO). It checks both Shortest-Job-First (SJF) buffering and Min-Min Best Fit (MMBF) scheduling algorithms, i.e., SJF-MMBF, is proposed to determine the solutions. Another scheme that combines the SJF buffering and Extreme Learning Machine (ELM)-based scheduling algorithms, i.e., SJF- ELM, is further proposed to avoid the potential of job starva¬tion in SJF-MMBF. In addition, there must be scheduling among the queues, which is commonly implemented as fixed-priority preemptive scheduling. The simulation results also illustrate that SJF- ELM is optimal in a heavy-loaded and highly dynamic environment and it is efficient in provisioning the average job hosting rate.

Research on Queue Scheduling Algorithm Modeling and Analysis

2012 ◽  
Vol 542-543 ◽  
pp. 1390-1393
Author(s):  
Heng Hua Shi ◽  
Xin Xu ◽  
Yu Jie Wang ◽  
Yuan Yue Yang
Keyword(s):  
Scheduling Algorithm ◽  
Scheduling Algorithms ◽  
Network Resource ◽  
Modeling And Analysis ◽  
Core Technology ◽  
Queue Scheduling ◽  
Bottleneck Link ◽  
Use Of The Internet ◽  
Levels Of Service ◽  
Different Levels

With the development of network technology and the wide use of the Internet, QoS has attracted wide attention. Queue scheduling algorithm of Router is an important core technology of the network resource management for QoS. Through controlling usage of the link bandwidth of the different type groups, the different traffics have the different levels of service. Based on the analysis and modeling of FIFO, PQ, and WFQ scheduling algorithms, the simulation experiment simulate three different priority video conferencing traffic, and apply FIFO, PQ, and WFQ scheduling algorithms on the bottleneck link. The simulation results compare network delay of FIFO, PQ, and WFQ scheduling algorithms, and describe the various queue scheduling algorithm characteristics.

The Messaging Mechanism Based on Multi-Level Feedback Queue Scheduling Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 1763-1765
Author(s):  
Ting Shun Li ◽  
Jiao Hui Xu ◽  
Hui Yu
Keyword(s):  
Wireless Communication ◽  
Communication Technology ◽  
Scheduling Algorithm ◽  
Communication Tools ◽  
Queue Scheduling ◽  
Multi Level ◽  
Feedback Queue

With the development of wireless communication technology, SMS , as a kind of flexible communication tools, is widely used in the various units. Aimed at large quantities of SMS processing, this paper proposes a new scheduling algorithm based on multi-level feedback queue. Multi-level feedback queue scheduling algorithm can not only make the high priority jobs response, but also make the short operations (process) done quickly.

scholarly journals User Targeted Offline Advertising using Recognition Based Demographics and Queue Scheduling

2020 ◽  
Vol 9 (3) ◽  
pp. 2751-2757
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Scheduling Algorithm ◽  
Research Paper ◽  
Priority Queues ◽  
Fixed Amount ◽  
Advertising Industry ◽  
Random Fashion ◽  
Queue Scheduling

Offline advertisements are static in nature. Advertising companies use billboards for advertising. These billboards display advertisements in a random fashion depending on the investment made by the advertiser. Advertisers pay a fixed amount of money for displaying their advertisements and not on the basis of relevant viewership. The technology proposed in the paper ensures that this disparity is handled wherein offline advertisements are targeted to the relevant audience. The technology has been named TARP which is an abbreviation for Target. Advertise. Revolutionise. Promote. TARP uses built in cameras on offline advertising platforms such as billboards & TV Screens in malls, restaurants, metro & airports to target advertisements based on gender, age and other relevant demographics. The technology is a boon for the advertising industry and benefits both advertisers and viewers. It displays what viewers want to see and who the advertisers want to reach out to. Convolutional neural networks are used to generate demographics of viewing population. Centroids of the viewing population are maintained for each billboard. Advertisements search for the most relevant billboard for display. Display of advertisements is monitored by a queue scheduling algorithm. The research paper proposes an algorithm to generate demographics, search most relevant billboard for each advertisement as well as generate priority queues.

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