Coevolutionary Workflow Scheduling in a Dynamic Cloud Environment

• Denis Nasonov ^[1] ; Mikhail Melnik ^[1] ; Anton Radice ^[1]
  1. [1] Saint Petersburg State University of Information Technologies, Mechanics and Optics

     Saint Petersburg State University of Information Technologies, Mechanics and Optics

     Rusia

     
• Localización: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings / coord. por Manuel Graña Romay, José Manuel López Guede, Oier Echaniz, Álvaro Herrero Cosío, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2017, ISBN 978-3-319-47364-2, págs. 189-210
• Idioma: inglés
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• Resumen

  • In this paper, we present a new coevolutionary algorithm for workflow scheduling in a dynamically changing environment. Nowadays, there are many efficient algorithms for workflow execution planning, many of which are based on the combination of heuristic and metaheuristic approaches or other forms of hybridization. The coevolutionary genetic algorithm (CGA) offers an extended mechanism for scheduling based on two principal operations: task mapping and resource configuration. While task mapping is a basic function of resource allocation, resource configuration changes the computational environment with the help of the virtualization mechanism. In this paper, we present a strategy for improving the CGA for dynamically changing environments that has a significant impact on the final dynamic CGA execution process.