Applying Ant Colony Optimization to the partitioned scheduling problem for heterogeneous multiprocessors

Authors:
Hua Chen;Albert M. K. Cheng
Affiliations:
Real-Time Systems Laboratory, Department of Computer Science, University of Houston, TX;Real-Time Systems Laboratory, Department of Computer Science, University of Houston, TX
Venue:
ACM SIGBED Review - Special issue: IEEE RTAS 2005 work-in-progress
Year:
2005

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Abstract

The problem of determining whether a set of periodic tasks can be assigned to a set of heterogeneous processors in such a way that all timing constraints are met 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. Experimental results show that our ACO approach can outperform the major existing methods. In addition to being able to search for a feasible assignment solution, our ACO approach can further optimize the solution to reduce its energy consumption.