PSO-COGENT: Cost and energy efficient scheduling in cloud environment with deadline constraint

2018 ◽  
Vol 19 ◽  
pp. 147-164 ◽  
Author(s):  
Mohit Kumar ◽  
S.C. Sharma
Keyword(s):  
Energy Efficient ◽  
Cloud Environment ◽  
Energy Efficient Scheduling ◽  
Deadline Constraint

Energy Efficient Scheduling for Multiple Workflows in Cloud Environment

2018 ◽  
Vol 13 (3) ◽  
pp. 14-34
Author(s):  
Ritu Garg ◽  
Neha Shukla
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Cloud Environment ◽  
Cloud Data ◽  
Available Energy ◽  
Cloud Data Centers ◽  
Reduce Energy Consumption ◽  
Scheduling Heuristic ◽  
Communication Stage ◽  
Energy Efficient Scheduling

Cloud computing makes utility computing possible with pay as you go model. It virtualizes the systems by polling and sharing the resources, thus we need to handle more than one workflow at the same time. Workflow is the standard to represent compute intensive applications in scientific and engineering domain. Hence, in this article, the authors presented the scheduling heuristic for multiple workflows running parallel in the cloud environment with the aim to reduce the energy consumption as it is one of the major concerns of cloud data centers along with the execution performance. In the proposed approach, first clustering is performed to minimize the energy consumption and execution time during communication corresponding to precedence constraint tasks. Then cluster are scheduled is on the best available energy efficient resources. Finally, DVFS is applied in order to reduce energy consumption further when the nodes are in the idle and communication stage. The simulation has been performed on CloudSim and the results show the reduction in energy consumption by up to 42%.

Energy Efficient Scheduling and Optimization for Parallel Tasks on Homogeneous Clusters

2012 ◽  
Vol 35 (3) ◽  
pp. 591-602 ◽  
Author(s):  
Xin LI ◽  
Zhi-Ping JIA ◽  
Lei JU ◽  
Yan-Heng ZHAO ◽  
Zi-Liang ZONG
Keyword(s):  
Energy Efficient ◽  
Parallel Tasks ◽  
Energy Efficient Scheduling
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