Approximation Algorithms on Multiprocessor Task Scheduling

2009 ◽  
Author(s):  
Huang Jingui ◽  
Li Rongheng
Keyword(s):  
Approximation Algorithms ◽  
Task Scheduling ◽  
Multiprocessor Task Scheduling

WORST-CASE PERFORMANCE EVALUATION ON MULTIPROCESSOR TASK SCHEDULING WITH RESOURCE AUGMENTATION

2011 ◽  
Vol 22 (04) ◽  
pp. 971-982
Author(s):  
DESHI YE ◽  
QINMING HE
Keyword(s):  
Approximation Algorithms ◽  
Task Scheduling ◽  
Processing Time ◽  
Time Algorithm ◽  
Resource Augmentation ◽  
Worst Case ◽  
Multiprocessor Task Scheduling ◽  
Lpt Algorithm ◽  
On Line ◽  
The Greedy Algorithm

We study the worst-case performance of approximation algorithms for the problem of multiprocessor task scheduling on m identical processors with resource augmentation, whose objective is to minimize the makespan. In this case, the approximation algorithms are given k (k ≥ 0) extra processors than the optimal off-line algorithm. For on-line algorithms, the Greedy algorithm and shelf algorithms are studied. For off-line algorithm, we consider the LPT (longest processing time) algorithm. Particularly, we prove that the schedule produced by the LPT algorithm is no longer than the optimal off-line algorithm if and only if k ≥ m - 2.

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.

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