Independent Task Scheduling by Artificial Immune Systems, Differential Evolution, and Genetic Algorithms

Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing systems and it is also an appealing NP-complete problem. There is a number of heuristic and metaheuristic algorithms that were tailored to deal with scheduling of independent jobs. In this study we investigate the efficiency of three bio-inspired metaheuristics for finding good schedules of independent tasks.

[1]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[2]  Jonathan Timmis,et al.  Special issue on artificial immune systems , 2010, Swarm Intelligence.

[3]  Václav Snásel,et al.  Evolving Schedules of Independent Tasks by Differential Evolution , 2011, Intelligent Networking, Collaborative Systems and Applications.

[4]  Ladislau Bölöni,et al.  A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems , 2001, J. Parallel Distributed Comput..

[5]  Rasmus K. Ursem,et al.  Models for Evolutionary Algorithms and Their Applications in System Identification and Control Optimization , 2003 .

[6]  David Fernández-Baca,et al.  Allocating Modules to Processors in a Distributed System , 1989, IEEE Trans. Software Eng..

[7]  A. A. Maciejewski,et al.  Heterogeneous Computing , 2002 .

[8]  Jian-Zhong Li,et al.  Performance Analysis of Task Scheduling Heuristics in Grid , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[9]  Han Yu.  Optimizing task schedules using an artificial immune system approach , 2008, GECCO '08.

[10]  Fatos Xhafa,et al.  Genetic algorithm based schedulers for grid computing systems , 2007 .

[11]  Andrew J. Page,et al.  Framework for Task Scheduling in Heterogeneous Distributed Computing Using Genetic Algorithms , 2005, Artificial Intelligence Review.

[12]  Viktor K. Prasanna,et al.  Utilization-based heuristics for statically mapping real-time applications onto the HiPer-D heterogeneous computing system , 2002, Proceedings 16th International Parallel and Distributed Processing Symposium.

[13]  Václav Snásel,et al.  Comparison of Heuristics for Scheduling Independent Tasks on Heterogeneous Distributed Environments , 2009, 2009 International Joint Conference on Computational Sciences and Optimization.

[14]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[15]  Jonathan Timmis,et al.  Theoretical advances in artificial immune systems , 2008, Theor. Comput. Sci..

[16]  Jonathan Timmis,et al.  Application Areas of AIS: The Past, The Present and The Future , 2005, ICARIS.

[17]  Berwin A. Turlach,et al.  Statistical Exploratory Analysis of Genetic Algorithms: The Influence of Gray Codes upon the Difficulty of a Problem , 2004, Australian Conference on Artificial Intelligence.

[18]  Jack J. Dongarra,et al.  Experiments with Scheduling Using Simulated Annealing in a Grid Environment , 2002, GRID.

[19]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[20]  John Levine,et al.  A hybrid ant algorithm for scheduling independent jobs in heterogeneous computing environments , 2004 .

[21]  Andries P. Engelbrecht,et al.  Computational Intelligence: An Introduction , 2002 .

[22]  Annie S. Wu,et al.  Empirical Observations on the Roles of Crossover and Mutation , 1997, ICGA.