Elephant Herding Optimization for Multiprocessor Task Scheduling in Heterogeneous Environment

2020 ◽  
pp. 217-229 ◽  
Author(s):  
Ronali Madhusmita Sahoo ◽  
Sasmita Kumari Padhy
Keyword(s):  
Task Scheduling ◽  
Heterogeneous Environment ◽  
Multiprocessor Task Scheduling

scholarly journals Genetic algorithms and multiprocessor task scheduling: A systematic literature review

2019 ◽  
Author(s):  
Eduardo Silva ◽  
Paulo Gabriel
Keyword(s):  
Systematic Review ◽  
Genetic Algorithms ◽  
Literature Review ◽  
Impact Factor ◽  
Task Scheduling ◽  
Systematic Literature Review ◽  
Meta Analysis ◽  
Scheduling Problem ◽  
Review Of The Literature ◽  
Multiprocessor Task Scheduling

This paper reports a systematic review of the literature about genetic algorithms applied to the multiprocessor task scheduling problem. After defining a protocol with the main rules of this review, the research was performed considering journal papers published between 1990 and 2018. At the end of this process, 37 works were recovered and analyzed. By performing a meta-analysis, a variety of information was extracted and summarized, including impact factor, Eigenfactor score, scenarios considered, optimization metrics, volume of citations, and others.

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

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