Distributing the computation in combinatorial optimization experiments over the cloud
| Autor: | Nikica Hlupić, Mario Brčić, Nenad Katanić |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
Combinatorial optimization Physics and Astronomy (miscellaneous) Computer science Computation 0211 other engineering and technologies Cloud computing 02 engineering and technology lcsh:Technology Empirical research Management of Technology and Innovation 0202 electrical engineering electronic engineering information engineering lcsh:Science Engineering (miscellaneous) Selection (genetic algorithm) Structure (mathematical logic) 021103 operations research business.industry lcsh:T Process (computing) Range (mathematics) 020201 artificial intelligence & image processing lcsh:Q Computational experiments business |
| Zdroj: | Advances in Science, Technology and Engineering Systems, Vol 2, Iss 6, Pp 136-144 (2017) |
| DOI: | 10.25046/aj020617 |
| Popis: | Combinatorial optimization is an area of great importance since many of the real-world problems have discrete parameters which are part of the objective function to be optimized. Development of combinatorial optimization algorithms is guided by the empirical study of the candidate ideas and their performance over a wide range of settings or scenarios to infer general conclusions. Number of scenarios can be overwhelming, especially when modeling uncertainty in some of the problem’s parameters. Since the process is also iterative and many ideas and hypotheses may be tested, execution time of each experiment has an important role in the efficiency and successfulness. Structure of such experiments allows for significant execution time improvement by distributing the computation. We focus on the cloud computing as a cost- efficient solution in these circumstances. In this paper we present a system for validating and comparing stochastic combinatorial optimization algorithms. The system also deals with selection of the optimal settings for computational nodes and number of nodes in terms of performance-cost tradeoff. We present applications of the system on a new class of project scheduling problem. We show that we can optimize the selection over cloud service providers as one of the settings and, according to the model, it resulted in a substantial cost-savings while meeting the deadline. |
| Databáze: | OpenAIRE |
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