Optimized QoS-aware replica placement heuristics and applications in astronomy data grid

Abstract

This paper studies the Quality-of-Service (QoS)-aware replica placement problem in a general graph model. Since the problem was proved NP-hard, heuristic algorithms are the current solutions to the problem. However, these algorithms cannot always find the effective replica placement strategy. We propose two algorithms that can obtain better results within the given time period. The first algorithm is called Cover Distance algorithm, which is based on the Greedy Cover algorithm. The second algorithm is an optimized genetic algorithm, in which we use random heuristic algorithms to generate initial population to avoid enormous useless searching. Then, the 0-Greedy-Delete algorithm is used to optimize the genetic algorithm solutions. According to the performance evaluation, our Cover Distance algorithm can obtain relatively better solution in time critical scenarios. Whereas, the optimized genetic algorithm is better when the replica cost is of higher priority than algorithm execution time. The QoS-aware data replication heuristic algorithms are applied into the data distribution service of an astronomy data grid pipeline prototype, and the operation process is studied in detail.