Accessing data from numerous widely-distributed sources poses signi cant new challenges for query optimization and execution. Congestion and failures in the network can introduce highly-variable response times for wide-area data access. This paper is an initial exploration of solutions to this variability. We introduce a class of dynamic, run-time query plan modi cation techniques that we call query plan scrambling. We present an algorithm that modi es execution plans onthe- y in response to unexpected delays in obtaining initial requested tuples from remote sources. The algorithm both reschedules operators and introduces new operators into the query plan. We present simulation results that demonstrate how the technique e ectively hides delays by performing other useful work while waiting for missing data to arrive.
Laurent Amsaleg, Michael J. Franklin, Anthony Toma