A Hybrid Task Scheduling Algorithm for Efficient Task Management in Multi-cloud Environment

2018 ◽  
pp. 47-57 ◽  
Author(s):  
Asmita Roy ◽  
Sadip Midya ◽  
Debojyoti Hazra ◽  
Koushik Majumder ◽  
Santanu Phadikar
Keyword(s):  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Management ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Multi Cloud

scholarly journals Priority Intensed Meta Task Scheduling Algorithm for Multi Cloud Environment (PIMTSA)

2020 ◽  
Vol 1427 ◽  
pp. 012007
Author(s):  
BJ. Hubert Shanthan ◽  
L. Arockiam ◽  
A. Cecil Donald ◽  
A.Dalvin Vinoth Kumar ◽  
R. Stephen
Keyword(s):  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Multi Cloud

A Customer-Oriented Task Scheduling for Heterogeneous Multi-Cloud Environment

2016 ◽  
Vol 6 (4) ◽  
pp. 1-17 ◽  
Author(s):  
Sohan Kumar Pande ◽  
Sanjaya Kumar Panda ◽  
Satyabrata Das
Keyword(s):  
Customer Satisfaction ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Computing Systems ◽  
Customer Expectation ◽  
Task Scheduling Algorithm ◽  
Np Complete ◽  
Simulation Results ◽  
Multi Cloud

Task scheduling is widely studied in various environments such as cluster, grid and cloud computing systems. Moreover, it is NP-Complete as the optimization criteria is to minimize the overall processing time of all the tasks (i.e., makespan). However, minimization of makespan does not equate to customer satisfaction. In this paper, the authors propose a customer-oriented task scheduling algorithm for heterogeneous multi-cloud environment. The basic idea of this algorithm is to assign a suitable task for each cloud which takes minimum execution time. Then it balances the makespan by inserting as much as tasks into the idle slots of each cloud. As a result, the customers will get better services in minimum time. They simulate the proposed algorithm in a virtualized environment and compare the simulation results with a well-known algorithm, called cloud min-min scheduling. The results show the superiority of the proposed algorithm in terms of customer satisfaction and surplus customer expectation. The authors validate the results using two statistical techniques, namely T-test and ANOVA.

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

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