Efficient and Scalable Collection of Dynamic Metrics Using MapReduce

Abstract

Dynamic metrics are known to assess the actual behavior of software systems as they are extracted from runtime data obtained during program execution. However, recent literature indicates that dealing with dynamic information remains a formidable challenge due to the huge size of execution data at hand, resulting in long processing delays. We present an efficient and scalable technique to extract design level dynamic metrics from Calling Context Tree (CCT) using cloud based MapReduce paradigm. CCT profiles having node count up to 40 million are used to extract a number of dynamic coupling metrics. On an average, 73% increase in performance is observed as compared to sequential analysis. Also other performance characteristics like speed-up and scale-up are analyzed to strengthen the applicability of our parallel computation approach.