scholarly journals Ganging of Resources via Fuzzy Manhattan Distance Similarity with Priority Tasks Scheduling in Cloud Computing

2018 ◽  
Vol 1 ◽  
pp. 32-41
Author(s):  
S. Sharon Priya ◽  
K. M. Mehata ◽  
W. Aisha Banu
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Manhattan Distance ◽  
Priority Task ◽  
Tasks Scheduling ◽  
Efficient Resource ◽  
Current Task ◽  
Computing Environments ◽  
Resource Characteristics ◽  
The Cost

This paper proposes a fuzzy Manhattan distance-based similarity for gang formation of resources (FMDSGR) method with priority task scheduling in cloud computing. The proposed work decides which processor is to execute the current task in order to achieve efficient resource utilization and effective task scheduling. FMDSGR groups the resources into gangs which rely upon the similarity of resource characteristics in order to use the resources effectively. Then, the tasks are scheduled based on the priority in the gang of processors using gang-based priority scheduling (GPS). This reduces mainly the cost of deciding which processor is to execute the current task. Performance has been evaluated in terms of makespan, scheduling length ratio, speedup, efficiency and load balancing. CloudSim simulator is the toolkit used for simulation and for demonstrating experimental results in cloud computing environments.

scholarly journals Efficient resource management for Cloud computing environments

2010 ◽  
Author(s):  
Andrew J. Younge ◽  
Gregor von Laszewski ◽  
Lizhe Wang ◽  
Sonia Lopez-Alarcon ◽  
Warren Carithers
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Efficient Resource ◽  
Computing Environments

Task Scheduling of Cloud Computing in Weapon Network System

2014 ◽  
Vol 610 ◽  
pp. 695-698
Author(s):  
Qian Tao ◽  
Bo Pan ◽  
Wen Quan Cui
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Evaluation System ◽  
Large Scale ◽  
Rapid Development ◽  
It Industry ◽  
Control Scheme ◽  
Tasks Scheduling ◽  
Constant Bandwidth

In recent years, the rapid development of cloud computing brings significant innovation in the whole IT industry. For the local tasks scheduling on each computational node of the top model of weapon network, an open task scheduling framework was introduced a task accept control scheme based on the tasks based on load balancing, quality of service (QoS) and an improved constant bandwidth server algorithm was presented. The result of simulation shows that the scheduling policies can improve the schedule speed when the number of tasks increases and can meet the demand better for the real time requirementsof the tactical training evaluation system for complexity and Large-scale.

scholarly journals Enhancing the Distribution of Idle Cost for Scheduling Tasks without Setup Cost in Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Redwan A. Al-dilami ◽  
Ammar T. Zahary ◽  
Adnan Z. Al-Saqqaf
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Setup Time ◽  
Setup Cost ◽  
Idle State ◽  
The Cost ◽  
Computing Infrastructure ◽  
Better Than

Issues of task scheduling in the centre of cloud computing are becoming more important, and the cost is one of the most important parameters used for scheduling tasks. This study aims to investigate the problem of online task scheduling of the identified job of MapReduce on cloud computing infrastructure. It was proposed that the virtualized cloud computing setup comprised machines that host multiple identical virtual machines (VMs) that need to be activated earlier and run continuously, and booting a VM requires a constant setup time. A VM that remains running even though it is no longer used is considered an idle VM. Furthermore, this study aims to distribute the idle cost of the VMs rather than the cost of setting up them among tasks in a fair manner. This study also is an extension of previous studies which solved the problems that occurred when distributing the idle cost and setting up the cost of VMs among tasks. It classifies the tasks into three groups (long, mid, and short) and distributes the idle cost among the groups then among the tasks of the groups. The main contribution of this paper is the developing of a clairvoyant algorithm that addressed important factors such as the delay and the cost that occurred by waiting to setup VM (active VM). Also, when the VMs are run continually and some VMs become in idle state, the idle cost will be distributed among the current tasks in a fair manner. The results of this study, in comparison with previous studies, showed that the idle cost and the setup cost that was distributed among tasks were better than the idle cost and the setup cost distributed in those studies.

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