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Ginga: a self-adaptive query processing system

Adaptive query processing in wide area distributed systems has received significant attention due to the dynamics of the Internet. Ginga is a project currently ongoing at Georgia Institute of Technology aiming at studying the research issues in building a self-adaptive query processing system. We identify three important failure modes for executing queries on the Internet: network delays [1], memory shortage, and sudden unavailability of remote data servers. This extended abstract will give a brief overview of the design of Ginga system and illustrate the proposed adaptation framework using a network delay scenario as example. A query processing system is called adaptive if it has the following two characteristics. First, it collects information about its runtime environment and uses this information to determine its subsequent behavior. Second, this information collection and behavior revision process iterates over time, generating feedback loop between the runtime environment and the behavior of the system. For each query, information about its runtime environment may include statistics on the specific data sources accessed, the current state of the network at the time that such access is attempted, and the control information entered by the users. We call a query processing system self-adaptive if it satisfies the following two properties: (1) information and control feedback is collected or learned automatically by the system rather than provided through interaction with users, and (2) the system reacts to specified environment changes immediately upon receiving feedback. We use self-adaptive to characterize the Ginga system to distributed query processing. The Ginga query adaptation engine collects information about its runtime environment with high frequency and full automation, and reacts to any environment change of interest immediately. We argue that