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An adaptive range-query optimization technique with distributed replicas
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AI
DOI：10.1007/s11771-014-1930-7.png)
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Abstract
En 中文
Replication is an approach often used to speed up the execution of queries submitted to a large dataset. A compile-time/run-time approach is presented for minimizing the response time of 2-dimensional range when a distributed replica of a dataset exists. The aim is to partition the query payload (and its range) into subsets and distribute those to the replica nodes in a way that minimizes a client's response time. However, since query size and distribution characteristics of data (data dense/sparse regions) in varying ranges are not known a priori, performing efficient load balancing and parallel processing over the unpredictable workload is difficult. A technique based on the creation and manipulation of dynamic spatial indexes for query payload estimation in distributed queries was proposed. The effectiveness of this technique was demonstrated on queries for analysis of archived earthquake-generated seismic data records.
Keywords:
distributed systems
load balancing
range query
query optimization
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