Dover: An Optimal On-Line Scheduling Algorithm for Overloaded Uniprocessor Real-Time Systems
SIAM Journal on Computing
Allocating fixed-priority periodic tasks on multiprocessor systems
Real-Time Systems
The ant colony optimization meta-heuristic
New ideas in optimization
Analyzing Fixed-Priority Global Multiprocessor Scheduling
RTAS '02 Proceedings of the Eighth IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'02)
On-Line Scheduling on Uniform Multiprocessors
RTSS '01 Proceedings of the 22nd IEEE Real-Time Systems Symposium
Rate-Monotonic Scheduling on Uniform Multiprocessors
IEEE Transactions on Computers
Robustness Results Concerning EDF Scheduling upon Uniform Multiprocessors
IEEE Transactions on Computers
Ant Colony Optimization
Minimum and Maximum Utilization Bounds for Multiprocessor Rate Monotonic Scheduling
IEEE Transactions on Parallel and Distributed Systems
ETAHM: an energy-aware task allocation algorithm for heterogeneous multiprocessor
Proceedings of the 45th annual Design Automation Conference
Application of fuzzy logic to real-time scheduling
RTC'05 Proceedings of the 14th IEEE-NPSS conference on Real time
Scheduling Algorithm for Real-Time Operating Systems Using ACO
CICN '10 Proceedings of the 2010 International Conference on Computational Intelligence and Communication Networks
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The Ant Colony Optimization ACO algorithms are computational models inspired by the collective foraging behavior of ants. The ACO algorithms provide inherent parallelism, which is very useful in multiprocessor environments. They provide balance between exploration and exploitation along with robustness and simplicity of individual agent. In this paper, ACO based dynamic scheduling algorithm for homogeneous multiprocessor real-time systems is proposed. The results obtained during simulation are measured in terms of Success Ratio SR and Effective CPU Utilization ECU and compared with the results of Earliest Deadline First EDF algorithm in the same environment. It has been observed that the proposed algorithm is very efficient in underloaded conditions and it performs very well during overloaded conditions also. Moreover, the proposed algorithm can schedule some typical instances successfully which are not possible to schedule using EDF algorithm.