Performance Optimization of Mashup Through Data Flow Transformation
Mashup is a new kind of web2.0 applications created by aggregating and manipulating data from several web data sources.Mashup tools usually support visually designing data flows to create mashup.Because mashup developers are of varying degrees of technical expertise,the data flows may be of high cost because of inefficient design.This will definitely increase the response time and impair the QoS of mashup.In this paper,we target on enhancing the performance of mashup base on data flow transformation techniques such as operator merging,operator swapping,and operator parallelism.A new optimization method is presented for mashup,which models a mashup as a data flow graph,annotates operation semantics features and cost model for mashup component,generates semantics equivalent data flows by transforming rules and construct a partially ordered diagram based on the cost of these data flows for quickly optimal selection.Key implementation techniques are provided and efficiency improvement of mashup is demonstrated by experiments.