Assigning real-time tasks to heterogeneous processors by applying ant colony optimization

The problem of determining whether a set of periodic tasks can be assigned to a set of heterogeneous processors without deadline violations has been shown, in general, to be NP-hard. This paper presents a new algorithm based on ant colony optimization (ACO) metaheuristic for solving this problem. A...

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Vydané v:Journal of parallel and distributed computing Ročník 71; číslo 1; s. 132 - 142
Hlavní autori: Chen, Hua, Cheng, Albert Mo Kim, Kuo, Ying-Wei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier Inc 2011
Elsevier
Predmet:
Algorithmics. Computability. Computer arithmetics
Algorithms
Ant colony optimization
Applied sciences
Colonies
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Energy of solution
Energy use
Exact sciences and technology
Heterogeneous processors
Language processing and microprogramming
Mathematical models
Multiprocessors
Optimization
Periodic tasks
Power-aware computing
Processors
Prototypes
Real-time systems
Scheduling
Searching
Software
Theoretical computing
Heterogeneous processors
Ant colony optimization
Multiprocessors
Power-aware computing
Periodic tasks
Scheduling
Real-time systems
Energy consumption
Insecta
Formicoidea
Multiprocessor
Space complexity
Swarm intelligence
Metamodel
Aculeata
Local search
Deadline
Periodicity
Social insect
Real time
Computational complexity
Processor scheduling
Arthropoda
Heuristic method
NP hard problem
Problem solving
Hymenoptera
Invertebrata
Time complexity
ISSN:0743-7315, 1096-0848
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Shrnutí:The problem of determining whether a set of periodic tasks can be assigned to a set of heterogeneous processors without deadline violations has been shown, in general, to be NP-hard. This paper presents a new algorithm based on ant colony optimization (ACO) metaheuristic for solving this problem. A local search heuristic that can be used by various metaheuristics to improve the assignment solution is proposed and its time and space complexity is analyzed. In addition to being able to search for a feasible assignment solution, our extended ACO algorithm can optimize the solution by lowering its energy consumption. Experimental results show that both the prototype and the extended version of our ACO algorithm outperform major existing methods; furthermore, the extended version achieves an average of 15.8% energy saving over its prototype. ► Ant colony optimization (ACO) is used to solve the multiprocessor partitioned scheduling problem. ► A local search heuristic is employed by various metaheuristics to improve the assignment solution. ► Experimental results show that implementations of our ACO algorithm outperform major existing methods. ► The extended version achieves an average of 15.8% energy savings.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:0743-7315
1096-0848
DOI:10.1016/j.jpdc.2010.09.011