Optimization of sub-query processing in distributed data integration systems
Data integration system (DIS) is becoming paramount when Cloud/Grid applications need to integrate and analyze data from geographically distributed data sources. DIS gathers data from multiple remote sources, integrates and analyzes the data to obtain a query result. As Clouds/Grids are distributed over wide-area networks, communication cost usually dominates overall query response time. Therefore we can expect that query performance can be improved by minimizing communication cost. In our method, DIS uses a data flow style query execution model. Each query plan is mapped to a group of ?Engines, each of which is a program corresponding to a particular operator. Thus, multiple sub-queries from concurrent queries are able to share ?Engines. We reconstruct these sub-queries to exploit overlapping data among them. As a result, all the sub-queries can obtain their results, and overall communication overhead can be reduced. Experimental results show that, when DIS runs a group of parameterized queries, our reconstructing algorithm can reduce the average query completion time by 3248%; when DIS runs a group of non-parameterized queries, the average query completion time of queries can be reduced by 2535%.
query data integration data flow cloud computing grid computing