scholarly journals Efficient Ideal Algorithm for Task Scheduling in Cloud Computing

2018 ◽  
Vol 7 (3.12) ◽  
pp. 5
Author(s):  
A M K Kanna Babu ◽  
M Sree Latha
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Task Order ◽  
Data Control ◽  
Efficient Access ◽  
Tasks Scheduling ◽  
Cpu Usage ◽  
Day By Day ◽  
Ideal Algorithm

Present days cloud computing is one of the best raising technologies in distributed computing sectors which permits pays in line with model as per customer demand and requirements.Cloud includes a set of virtual machines which incorporates each persistent andcomputational facility. Providing efficient access of any data to the remote area through network is the primary motto of the cloud computing. Day by day cloud is facing many demanding situations. In which it is facing scheduling is the key one. The process through which the task can be done in certain order via pc device is called scheduling. The word scheduling defines different package of rules for controlling the task order for running the known job through network via computer machines. An awesome scheduler adapts the strategy of scheduling consistent with converting execution of job to sort of different tasks. In research paper we presented, data control of different virtual machines in cloud computing is the new target to the researchers. Algorithms of different tasks scheduling plays an essential and important role for solving some problems. The intention of the scheduling is running of different tasks successfully with in less time and accuracy.And we discussing about various components that cloud computing depends like CPU usage, Load balancing, and Time.  

A PSO Algorithm Based Task Scheduling in Cloud Computing

2019 ◽  
Vol 10 (4) ◽  
pp. 1-17 ◽  
Author(s):  
Mohit Agarwal ◽  
Gur Mauj Saran Srivastava
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Pso Algorithm ◽  
Emerging Technology ◽  
The Internet ◽  
Hard Problem ◽  
Swarm Optimization ◽  
Np Hard Problem ◽  
Simulation Results

Cloud computing is an emerging technology which involves the allocation and de-allocation of the computing resources using the internet. Task scheduling (TS) is one of the fundamental issues in cloud computing and effort has been made to solve this problem. An efficient task scheduling mechanism is always needed for the allocation to the available processing machines in such a manner that no machine is over or under-utilized. Scheduling tasks belongs to the category of NP-hard problem. Through this article, the authors are proposing a particle swarm optimization (PSO) based task scheduling mechanism for the efficient scheduling of tasks among the virtual machines (VMs). The proposed algorithm is compared using the CloudSim simulator with the existing greedy and genetic algorithm-based task scheduling mechanism. The simulation results clearly show that the PSO-based task scheduling mechanism clearly outperforms the others as it results in almost 30% reduction in makespan and increases the resource utilization by 20%.

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.

scholarly journals REVIEW OF TASK SCHEDULING METHODS FOR REAL TIME TASKS IN CLOUD ENVIRONMENT

2020 ◽  
Vol 5 (1) ◽  
pp. 85-89
Author(s):  
Dinkan Patel ◽  
Anjuman Ranavadiya
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Dynamic Scheduling ◽  
Scheduling Algorithms ◽  
Minimum Time ◽  
Cloud Environment ◽  
Static Scheduling ◽  
On Demand

Cloud Computing is a type of Internet model that enables convenient, on-demand resources that can be used rapidly and with minimum effort. Cloud Computing can be IaaS, PaaS or SaaS. Scheduling of these tasks is important so that resources can be utilized efficiently with minimum time which in turn gives better performance. Real time tasks require dynamic scheduling as tasks cannot be known in advance as in static scheduling approach. There are different task scheduling algorithms that can be utilized to increase the performance in real time and performing these on virtual machines can prove to be useful. Here a review of various task scheduling algorithms is done which can be used to perform the task and allocate resources so that performance can be increased.

scholarly journals Measuring the Performance on Load Balancing Algorithms

2019 ◽  
pp. 41-49 ◽  
Author(s):  
Saumendu Roy ◽  
Dr. Md. Alam Hossain ◽  
Sujit Kumar Sen ◽  
Nazmul Hossain ◽  
Md. Rashid Al Asif
Keyword(s):  
Cloud Computing ◽  
Reaction Time ◽  
Load Balancing ◽  
Virtual Machines ◽  
Huge Amount ◽  
Heavy Load ◽  
Cloud Server ◽  
Load Balancing Algorithm ◽  
Day By Day ◽  
Better Than

Load balancing is an integrated aspect of the environment in cloud computing. Cloud computing has lately outgoing technology. It has getting exoteric day by day residence widespread chance in close to posterior. Cloud computing is defined as a massively distributed computing example that is moved by an economic scale in which a repertory of abstracted virtualized energetically. The number of clients in cloud computing is increasing exponentially. The huge amount of user requests attempt to entitle the collection for numerous applications. Which alongside with heavy load not far afield off from cloud server. Whenever particular (Virtual Machine) VMs are overloaded then there are no more duties should be addressed to overloaded VM if under loaded VMs are receivable. For optimizing accomplishment and better response or reaction time the load has to be balanced between overloaded VMs (virtual machines). This Paper describes briefly about the load balancing accession and identifies which is better than others (load balancing algorithm).

scholarly journals An Attribute-Oriented Task Scheduling Strategy for Improvement of Quality of Service in Cloud Computing

2021 ◽  
Author(s):  
Jianying Miao
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Simulation Environment ◽  
Attribute Matching ◽  
Resource Allocation Strategy ◽  
And Task

This thesis describes an innovative task scheduling and resource allocation strategy by using thresholds with attributes and amount (TAA) in order to improve the quality of service of cloud computing. In the strategy, attribute-oriented thresholds are set to decide on the acceptance of cloudlets (tasks), and the provisioning of accepted cloudlets on suitable resources represented by virtual machines (VMs,). Experiments are performed in a simulation environment created by Cloudsim that is modified for the experiments. Experimental results indicate that TAA can significantly improve attribute matching between cloudlets and VMs, with average execution time reduced by 30 to 50% compared to a typical non-filtering policy. Moreover, the tradeoff between acceptance rate and task delay, as well as between prioritized and non-prioritized cloudlets, may be adjusted as desired. The filtering type and range and the positioning of thresholds may also be adjusted so as to adapt to the dynamically changing cloud environment.

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.

Application of Particle Swarm Optimisation-R Factor Optimisation Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment

2020 ◽  
Vol 17 (4) ◽  
pp. 1990-1998
Author(s):  
R. Valarmathi ◽  
T. Sheela
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Management ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Cost Optimization ◽  
Optimal Solution ◽  
Service Needs ◽  
Cloud Environment ◽  
Efficient Resource

Cloud computing is a powerful technology of computing which renders flexible services anywhere to the user. Resource management and task scheduling are essential perspectives of cloud computing. One of the main problems of cloud computing was task scheduling. Usually task scheduling and resource management in cloud is a tough optimization issue at the time of considering quality of service needs. Huge works under task scheduling focuses only on deadline issues and cost optimization and it avoids the significance of availability, robustness and reliability. The main purpose of this study is to develop an Optimized Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment. This study uses PSO and R factor algorithm. The main aim of PSO algorithm is that tasks are scheduled to VM (virtual machines) to reduce the time of waiting and throughput of system. PSO is a technique inspired by social and collective behavior of animal swarms in nature and wherein particles search the problem space to predict near optimal or optimal solution. A hybrid algorithm combining PSO and R-factor has been developed with the purpose of reducing the processing time, make span and cost of task execution simultaneously. The test results and simulation reveals that the proposed method offers better efficiency than the previously prevalent approaches.

scholarly journals A Survey on Load Balancing Algorithms

2019 ◽  
pp. 1156-1163
Author(s):  
Er. Ruchi ◽  
Harish Kumar
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Comparative Study ◽  
Response Time ◽  
Task Scheduling ◽  
Dynamic Load ◽  
Dynamic Load Balancing ◽  
Distributed Resources ◽  
The Cost ◽  
Day By Day

Cloud computing is referred to as biggest technology of today’s environment that provide access to distributed resources on the basis of pay-per-use. Everyone try to use cloud to reduce the cost and maintenance of infrastructure due to which lots of load is increasing day by day. Therefore, there is need to balance that load since resources of cloud are limited but usage is increasing at every moment. This paper discuss how the resources are allocated and how the tasks are scheduled among those resources. Task scheduling mainly focuses on enhancing the utilization of resources and hence reduction in response time. There are various static and dynamic load balancing algorithms to balance the load, this paper discusses comparative study of these algorithms.

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