TY  - GEN
A1  - Schlosser, Rainer
A1  - Kossmann, Jan
A1  - Boissier, Martin
T1  - Efficient Scalable Multi-Attribute Index Selection Using Recursive Strategies
T2  - 2019 IEEE 35th International Conference on Data Engineering (ICDE)
N2  - An efficient selection of indexes is indispensable for database performance. For large problem instances with hundreds of tables, existing approaches are not suitable: They either exhibit prohibitive runtimes or yield far from optimal index configurations by strongly limiting the set of index candidates or not handling index interaction explicitly. We introduce a novel recursive strategy that does not exclude index candidates in advance and effectively accounts for index interaction. Using large real-world workloads, we demonstrate the applicability of our approach. Further, we evaluate our solution end to end with a commercial database system using a reproducible setup. We show that our solutions are near-optimal for small index selection problems. For larger problems, our strategy outperforms state-of-the-art approaches in both scalability and solution quality.
Y1  - 2019
SN  - 978-1-5386-7474-1
U6  - https://doi.org/10.1109/ICDE.2019.00113
SN  - 1084-4627
SP  - 1238
EP  - 1249
PB  - IEEE
CY  - New York
ER  - 
TY  - GEN
A1  - Halfpap, Stefan
A1  - Schlosser, Rainer
T1  - Workload-Driven Fragment Allocation for Partially Replicated Databases Using Linear Programming
T2  - 2019 IEEE 35th International Conference on Data Engineering (ICDE)
N2  - In replication schemes, replica nodes can process read-only queries on snapshots of the master node without violating transactional consistency. By analyzing the workload, we can identify query access patterns and replicate data depending to its access frequency. In this paper, we define a linear programming (LP) model to calculate the set of partial replicas with the lowest overall memory capacity while evenly balancing the query load. Furthermore, we propose a scalable decomposition heuristic to calculate solutions for larger problem sizes. While guaranteeing the same performance as state-of-the-art heuristics, our decomposition approach calculates allocations with up to 23% lower memory footprint for the TPC-H benchmark.
KW  - database replication
KW  - allocation problem
KW  - linear programming
Y1  - 2019
SN  - 978-1-5386-7474-1
SN  - 978-1-5386-7475-8
U6  - https://doi.org/10.1109/ICDE.2019.00188
SN  - 1084-4627
SN  - 2375-026X
SN  - 1063-6382
SP  - 1746
EP  - 1749
PB  - IEEE
CY  - New York
ER  - 
TY  - GEN
A1  - Halfpap, Stefan
A1  - Schlosser, Rainer
T1  - A Comparison of Allocation Algorithms for Partially Replicated Databases
T2  - 2019 IEEE 35th International Conference on Data Engineering (ICDE)
N2  - Increasing demand for analytical processing capabilities can be managed by replication approaches. However, to evenly balance the replicas' workload shares while at the same time minimizing the data replication factor is a highly challenging allocation problem. As optimal solutions are only applicable for small problem instances, effective heuristics are indispensable. In this paper, we test and compare state-of-the-art allocation algorithms for partial replication. By visualizing and exploring their (heuristic) solutions for different benchmark workloads, we are able to derive structural insights and to detect an algorithm's strengths as well as its potential for improvement. Further, our application enables end-to-end evaluations of different allocations to verify their theoretical performance.
Y1  - 2019
SN  - 978-1-5386-7474-1
SN  - 978-1-5386-7475-8
U6  - https://doi.org/10.1109/ICDE.2019.00226
SN  - 1084-4627
SN  - 2375-026X
SN  - 1063-6382
SP  - 2008
EP  - 2011
PB  - IEEE
CY  - New York
ER  -