Distributed Query Optimization by Query Trading

Abstract

Large-scale distributed environments, where each node is completely autonomous and offers services to its peers through external communication, pose significant challenges to query processing and optimization. Autonomy is the main source of the problem, as it results in lack of knowledge about any particular node with respect to the information it can produce and its characteristics. Inter-node competition is another source of the problem, as it results in potentially inconsistent behavior of the nodes at different times. In this paper, inspired by e-commerce technology, we recognize queries (and query answers) as commodities and model query optimization as a trading negotiation process. Query parts (and their answers) are traded between nodes until deals are struck with some nodes for all of them. We identify the key parameters of this framework and suggest several potential alternatives for each one. Finally, we conclude with some experiments that demonstrate the scalability and performance characteristics of our approach compared to those of traditional query optimization.KeywordsExecution PlanQuery OptimizationRemote NodeNegotiation ProtocolQuery AnswerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.