scholarly journals Hypergraph+: An Improved Hypergraph-Based Task-Scheduling Algorithm for Massive Spatial Data Processing on Master-Slave Platforms

2016 ◽  
Vol 5 (8) ◽  
pp. 141 ◽  
Author(s):  
Bo Cheng ◽  
Xuefeng Guan ◽  
Huayi Wu ◽  
Rui Li
Keyword(s):  
Data Processing ◽  
Task Scheduling ◽  
Spatial Data ◽  
Scheduling Algorithm ◽  
Task Scheduling Algorithm ◽  
Massive Spatial Data ◽  
Spatial Data Processing

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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