Abstract

This paper presents a methodology for automatically generating online scheduling strategies for a complex objective defined by a machine provider. To this end, we assume independent parallel jobs and multiple identical machines. The scheduling algorithm is based on a rule system. This rule system classifies all possible scheduling states and assigns a corresponding scheduling strategy. Each state is described by several parameters. The rule system is established in two different ways. In the first approach, an iterative method is applied, that assigns a standard scheduling strategy to all situation classes. Here, the situation classes are fixed and cannot be modified. Afterwards, for each situation class, the best strategy is extracted individually. In the second approach, a Symbiotic Evolution varies the parameter of Gaussian membership functions to establish the different situation classes and also assigns the appropriate scheduling strategies. Finally, both rule systems will be compared by using real workload traces and different possible complex objective functions.

Keywords: Scheduling algorithm development; Online scheduling; Genetic Fuzzy system; Symbiotic Evolution

Article Outline

1. Introduction

2. State of the art

2.1. Scheduling concepts

2.2. Evolution Strategies

2.3. Symbiotic Evolution of Genetic Fuzzy systems

3. Scheduling objectives and features

3.1. Scheduling objectives

3.2. Feature definitions

4. Rule-based scheduling

4.1. Iterative rule base generation

4.2. Symbiotic Evolutionary Approach

4.2.1. Coding of fuzzy rules

4.2.2. Controller output decision

4.2.2.1. General output decision

4.2.2.2. Default output decision

4.2.3. Similarity measure for rules

4.2.4. Fitness assignment

4.2.5. Selection by clusters

4.3. Configuration of the Symbiotic Evolutionary Algorithm

4.3.1. Population size

4.3.2. Recombination operator

4.3.3. Mutation operator

4.3.4. Population for default values

5. Evaluation

5.1. Used workload traces

5.2. Formulation of user group prioritizations

5.3. Results of the iterative approach

5.4. Results of the Symbiotic Evolutionary Approach

6. Conclusion

Acknowledgements

References