Dynamic Backfilling Algorithm to Increase Resource Utilization in Cloud Computing

2019 ◽  
Vol 14 (1) ◽  
pp. 1-26
Author(s):  
Suvendu Chandan Nayak ◽  
Sasmita Parida ◽  
Chitaranjan Tripathy ◽  
Prasant Kumar Pattnaik
Keyword(s):  
Cloud Computing ◽  
Decision Maker ◽  
Resource Utilization ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Cloud Platform ◽  
Start Time ◽  
Current Time ◽  
Task Scheduling Algorithm ◽  
First Come First Serve

In this article, the authors propose a novel backfilling-based task scheduling algorithm to schedule deadline-based tasks. The existing backfilling algorithm has some performance issues in comparison with the number of task scheduling in OpenNebula cloud platform. A lease could not be scheduled if it is not sorted with respect to its start time. In backfilling, a lease is selected in First Come First Serve (FCFS) to be backfilled from the queue in which some ideal resources can be found out and allocated to other leases. However, the scheduling performance is not better if there are similar types of leases to backfill. It requires a decision maker to resolve conflicts. The proposed approach schedules the number of tasks without any decision maker. An additional queue and the current time of the system is implemented to improve the scheduling performance. It performs quite satisfactorily in terms of number of a leases scheduling, and resource utilization. The performance result is compared with the existing backfilling algorithms.

scholarly journals Alts: An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1514
Author(s):  
Aroosa Mubeen ◽  
Muhammad Ibrahim ◽  
Nargis Bibi ◽  
Mohammad Baz ◽  
Habib Hamam ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Resource Utilization ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Problem Size ◽  
Cloud Data ◽  
Sla Violation ◽  
Task Scheduling Algorithm ◽  
User Tasks ◽  
Load Balanced

According to the research, many task scheduling approaches have been proposed like GA, ACO, etc., which have improved the performance of the cloud data centers concerning various scheduling parameters. The task scheduling problem is NP-hard, as the key reason is the number of solutions/combinations grows exponentially with the problem size, e.g., the number of tasks and the number of computing resources. Thus, it is always challenging to have complete optimal scheduling of the user tasks. In this research, we proposed an adaptive load-balanced task scheduling (ALTS) approach for cloud computing. The proposed task scheduling algorithm maps all incoming tasks to the available VMs in a load-balanced way to reduce the makespan, maximize resource utilization, and adaptively minimize the SLA violation. The performance of the proposed task scheduling algorithm is evaluated and compared with the state-of-the-art task scheduling ACO, GA, and GAACO approaches concerning average resource utilization (ARUR), Makespan, and SLA violation. The proposed approach has revealed significant improvements concerning the makespan, SLA violation, and resource utilization against the compared approaches.

scholarly journals IOT based visualization of weightage based static task scheduling algorithm in datacenter

2018 ◽  
Vol 7 (2.8) ◽  
pp. 439
Author(s):  
Preethi M ◽  
Kayalvizhi Jayavel
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Mobile Device ◽  
Scheduling Algorithm ◽  
Task Scheduling Algorithm ◽  
Long Time ◽  
First Come First Serve

Cloud computing has raised majorly to provide everything as a service and also for scaling the resources and utilizing the resources in an effective way. This paper aims to propose a scheduling algorithm which allocates static tasks to the resources effectively without making any tasks starve for the resources for long time. In SJF algorithm, the shortest tasks will be executed initially, and the largest tasks will keep on starving for the resources to be allocated. The proposed algorithm handles such a situation effectively by adding the jobs under different weightage queues and then scheduling them in an SJF order. This gives priority to even largest job. In this paper a framework is proposed, which fetches data from Amazon SDB storage and the processing of data based on proposed algorithm occurs in a cloudsim and finally the results are visualized through an IOT mobile device. The comparison is also made for First Come First Serve (FCFS), which is a default scheduling algorithm and the proposed algorithm.

scholarly journals HTSCC A Hybrid Task Scheduling Algorithm in Cloud Computing Environment

2018 ◽  
Vol 17 (2) ◽  
pp. 7236-7246 ◽  
Author(s):  
Rasha Ali Al-Arasi ◽  
Anwar Saif
Keyword(s):  
Cloud Computing ◽  
Resource Utilization ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Application Software ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Task Scheduling Algorithm ◽  
Np Hard Problem ◽  
Software And Hardware

Nowadays, cloud computing makes it possible for users to use the computing resources like application, software, and hardware, etc., on pay as use model via the internet. One of the core and challenging issue in cloud computing is the task scheduling. Task scheduling problem is an NP-hard problem and is responsible for mapping the tasks to resources in a way to spread the load evenly. The appropriate mapping between resources and tasks reduces makespan and maximizes resource utilization. In this paper, we present and implement an independent task scheduling algorithm that assigns the users' tasks to multiple computing resources. The proposed algorithm is a hybrid algorithm for task scheduling in cloud computing based on a genetic algorithm (GA) and particle swarm optimization (PSO). The algorithm is implemented and simulated using CloudSim simulator. The simulation results show that our proposed algorithm outperforms the GA and PSO algorithms by decreasing the makespan and increasing the resource utilization.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

Sign in / Sign up

Export Citation Format

Share Document