Static scheduling algorithms for allocating directed task graphs to multiprocessors
ACM Computing Surveys (CSUR)
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
An Integrated Approach to Parallel Scheduling Using Gang-Scheduling, Backfilling, and Migration
JSSPP '01 Revised Papers from the 7th International Workshop on Job Scheduling Strategies for Parallel Processing
Design and Evaluation of a Resource Selection Framework for Grid Applications
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
Condor-G: A Computation Management Agent for Multi-Institutional Grids
HPDC '01 Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Genetic Algorithm Based Scheduler for Computational Grids
HPCS '05 Proceedings of the 19th International Symposium on High Performance Computing Systems and Applications
Risk-Resilient Heuristics and Genetic Algorithms for Security-Assured Grid Job Scheduling
IEEE Transactions on Computers
Task scheduling strategies for workflow-based applications in grids
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid (CCGrid'05) - Volume 2 - Volume 02
Adaptive grid job scheduling with genetic algorithms
Future Generation Computer Systems
A dynamic task scheduling approach based on wasp algorithm in grid environment
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Queuing network of scale free topology: on modelling large scale network
The Journal of Supercomputing
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We introduce a Chaotic Genetic Algorithm (CGA) to schedule Grid jobs with uncertainties. We adopt a Fuzzy Set based Execution Time (FSET) model to describe uncertain operation time and flexible deadline of Grid jobs. We incorporate chaos into standard Genetic Algorithm (GA) by logistic function, a simple equation involving chaos. A distinguishing feature of our approach is that the convergence of CGA can be controlled automatically by the three famous characteristics of logistic function: convergent, bifurcating, and chaotic. Following this idea, we propose a chaotic mutation operatorbased on the feedback of fitness function that ameliorates GA, in terms of convergent speed and stability. We present an entropy based metrics to evaluate the performance of CGA. Experimental results illustrate the efficiency and stability of the resulting algorithm.