Impact of tasks classification and virtual machines categorization on tasks scheduling algorithms in cloud computing

2017 ◽  
Author(s):  
Tahani Aladwani
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Scheduling Algorithms ◽  
Tasks Scheduling

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.

Evaluating map reduce tasks scheduling algorithms over cloud computing infrastructure

2015 ◽  
Vol 27 (18) ◽  
pp. 5686-5699 ◽  
Author(s):  
Qutaibah Althebyan ◽  
Yaser Jararweh ◽  
Qussai Yaseen ◽  
Omar AlQudah ◽  
Mahmoud Al-Ayyoub
Keyword(s):  
Cloud Computing ◽  
Scheduling Algorithms ◽  
Map Reduce ◽  
Tasks Scheduling ◽  
Computing Infrastructure

Cloud Computing Resource Schedule Strategy Based on PSO Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 1332-1336 ◽  
Author(s):  
Ying Yidu Xiong ◽  
Yan Yan Wu
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Virtual Machines ◽  
Scheduling Algorithms ◽  
Pso Algorithm ◽  
Dynamic Adaptation ◽  
The Core ◽  
Schedule Strategy ◽  
Core Technology ◽  
Resource Schedule

Resource schedule Strategy is the core technology of cloud computing. PSO algorithm is one of dynamic adaptation resource scheduling algorithms to cloud computing. The virtual machines and the hosts can be scheduled reasonable by adjusting parameters. The resource can be scheduled quickly because of the dynamic trend calculation of PSO algorithm, to ensure real-time of the Cloud Calculation.

scholarly journals Energy-Efficient Load Balancing Algorithm for Workflow Scheduling in Cloud Data Centers Using Queuing and Thresholds

2021 ◽  
Vol 11 (13) ◽  
pp. 5849
Author(s):  
Nimra Malik ◽  
Muhammad Sardaraz ◽  
Muhammad Tahir ◽  
Babar Shah ◽  
Gohar Ali ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Load Balancing ◽  
Resource Utilization ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Data ◽  
Tasks Scheduling ◽  
Benchmark Datasets ◽  
Cloud Data Centers

Cloud computing is a rapidly growing technology that has been implemented in various fields in recent years, such as business, research, industry, and computing. Cloud computing provides different services over the internet, thus eliminating the need for personalized hardware and other resources. Cloud computing environments face some challenges in terms of resource utilization, energy efficiency, heterogeneous resources, etc. Tasks scheduling and virtual machines (VMs) are used as consolidation techniques in order to tackle these issues. Tasks scheduling has been extensively studied in the literature. The problem has been studied with different parameters and objectives. In this article, we address the problem of energy consumption and efficient resource utilization in virtualized cloud data centers. The proposed algorithm is based on task classification and thresholds for efficient scheduling and better resource utilization. In the first phase, workflow tasks are pre-processed to avoid bottlenecks by placing tasks with more dependencies and long execution times in separate queues. In the next step, tasks are classified based on the intensities of the required resources. Finally, Particle Swarm Optimization (PSO) is used to select the best schedules. Experiments were performed to validate the proposed technique. Comparative results obtained on benchmark datasets are presented. The results show the effectiveness of the proposed algorithm over that of the other algorithms to which it was compared in terms of energy consumption, makespan, and load balancing.

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.  

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

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