A MapReduce scheduling algorithm for time constraints in heterogeneous environment

2014 ◽  
Author(s):  
Tan Deng ◽  
Kenli Li
Keyword(s):  
Scheduling Algorithm ◽  
Time Constraints ◽  
Heterogeneous Environment ◽  
Mapreduce Scheduling

Flexible protocols improve parallel experimentation throughput

1995 ◽  
Vol 41 (7) ◽  
pp. 1004-1010 ◽  
Author(s):  
R J Aarts ◽  
J S Lindsey ◽  
L A Corkan ◽  
S F Smith
Keyword(s):  
Experimental Research ◽  
Scheduling Algorithm ◽  
Time Constraints ◽  
Experimental Protocols

Abstract Advanced chemical workstations offer the potential to substantially improve the productivity of experimental research. To fully exploit such technologies, effective scheduling of experiments is crucial. Chemists tend to define experimental protocols with rigid time constraints, although often the scientific objectives can be achieved without adhering to such constraints. Investigation of a scheduling algorithm that allows flexible time constraints shows that improvements in workstation throughput as great as 50% can be reached by modest flexibility in the timing of operations in the experiments. Several heuristics that might be used with the scheduling algorithm were tested; a heuristic that schedules long experiments while first keeping the workstation busy was shown to be a good general choice.

Load and Cost Aware Min-Min Workflow Scheduling Algorithm for Heterogeneous Resources in Fog, Cloud and Edge Scenarios

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Load Balancing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
Workflow Scheduling ◽  
Heterogeneous Environment ◽  
Cloud Servers ◽  
Heterogeneous Resources

Fog computing and Edge computing are few of the latest technologies which are offered as solution to challenges faced in Cloud Computing. Instead of offloading of all the tasks to centralized cloud servers, some of the tasks can be scheduled at intermediate Fog servers or Edge devices. Though this solves most of the problems faced in cloud but also encounter other traditional problems due to resource-related constraints like load balancing, scheduling, etc. In order to address task scheduling and load balancing in Cloud-fog-edge collaboration among servers, we have proposed an improved version of min-min algorithm for workflow scheduling which considers cost, makespan, energy and load balancing in heterogeneous environment. This algorithm is implemented and tested in different offloading scenarios- Cloud only, Fog only, Cloud-fog and Cloud-Fog-Edge collaboration. This approach performed better and the result gives minimum makespan, less energy consumption along with load balancing and marginally less cost when compared to min-min and ELBMM algorithms

Improving MapReduce Scheduling Algorithm Using Prediction-Based Application Model in the Cloud

2012 ◽  
Vol 9 (1) ◽  
pp. 307-311
Author(s):  
Seong Kyun Kim ◽  
Hongli Zhang ◽  
Ying Li ◽  
Kyong Hoon Kim
Keyword(s):  
Scheduling Algorithm ◽  
Application Model ◽  
Mapreduce Scheduling
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