A Distributed Fine-Grained Flow Control System for Scalable Aircraft Spares Management and Optimization in Clouds

In this paper, we presented the design, implementation, and evaluation of a distributed system to manage the parallelized analytics for Aircraft Spare parts Management and Optimizations (SMO), which is a well-known problem in logistics industry. Our proposed solution is able to solve the resource-intensive SMO problem using distributed computing infrastructures (e.g., private or public clouds) in a scalable manner. We designed and fine-tuned a parallel meta-heuristics based on a fine-grained flow control workflow model which enables flow controls of running parallel meta-heuristics in multiple processors and achieved significant performance gains. Together with priority based scheduling, the proposed system effectively dispatches submitted SMO jobs over the set of distributed resources to accommodate different classes of users. Extensive experimental studies were conducted to analyze the performance of parallelized SMO job executions in term of execution time, computation and data transmission time, waiting time, memory usage, etc. Insightful lessons have been drawn from the obtained results, and potential areas for further improvements have also been identified.