Resource bricolage and resource selection for parallel database systems

Resource bricolage and resource selection for parallel database systems Running parallel database systems in an environment with heterogeneous resources has become increasingly common, due to cluster evolution and increasing interest in moving applications into public clouds. Performance differences among machines in the same cluster pose new challenges for parallel database systems. First, for database systems running in a heterogeneous cluster, the default uniform data partitioning strategy may overload some of the slow machines, while at the same time it may underutilize the more powerful machines. Since the processing time of a parallel query is determined by the slowest machine, such an allocation strategy may result in a significant query performance degradation. Second, since machines might have varying resources or performance, different choices of machines may lead to different costs or performance for executing the same workload. By carefully selecting the most suitable machines for running a workload, we may achieve better performance with the same budget, or we may meet the same performance requirements with a lower cost. We address these challenges by introducing techniques we call resource bricolage and resource selection that improve database performance in heterogeneous environments. Our approaches quantify the performance differences among machines with various resources as they process workloads with diverse resource requirements. For the purpose of better resource utilization, we formalize the problem of minimizing workload execution time and view it as an optimization problem, and then, we employ linear programming to obtain a recommended data partitioning scheme. For the purpose of better resource selection, we formalize two problems: One minimizes the total workload execution time with a given budget, and the other minimizes the total budget with a given performance target. We then employ different mixed-integer programs to search for the optimal resource selection decisions. We verify the effectiveness of both resource bricolage and resource selection techniques with an extensive experimental study. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Resource bricolage and resource selection for parallel database systems

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-016-0435-4
Publisher site
See Article on Publisher Site

Abstract

Running parallel database systems in an environment with heterogeneous resources has become increasingly common, due to cluster evolution and increasing interest in moving applications into public clouds. Performance differences among machines in the same cluster pose new challenges for parallel database systems. First, for database systems running in a heterogeneous cluster, the default uniform data partitioning strategy may overload some of the slow machines, while at the same time it may underutilize the more powerful machines. Since the processing time of a parallel query is determined by the slowest machine, such an allocation strategy may result in a significant query performance degradation. Second, since machines might have varying resources or performance, different choices of machines may lead to different costs or performance for executing the same workload. By carefully selecting the most suitable machines for running a workload, we may achieve better performance with the same budget, or we may meet the same performance requirements with a lower cost. We address these challenges by introducing techniques we call resource bricolage and resource selection that improve database performance in heterogeneous environments. Our approaches quantify the performance differences among machines with various resources as they process workloads with diverse resource requirements. For the purpose of better resource utilization, we formalize the problem of minimizing workload execution time and view it as an optimization problem, and then, we employ linear programming to obtain a recommended data partitioning scheme. For the purpose of better resource selection, we formalize two problems: One minimizes the total workload execution time with a given budget, and the other minimizes the total budget with a given performance target. We then employ different mixed-integer programs to search for the optimal resource selection decisions. We verify the effectiveness of both resource bricolage and resource selection techniques with an extensive experimental study.

Journal

The VLDB JournalSpringer Journals

Published: Jun 25, 2016

References

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