43,546 research outputs found
Automated Physical Database Design and Tuning in SQL Server
Import 22/07/2015Tato diplomová práce pojednává o ladění fyzického návrhu databáze v SQL Serveru. SQL Server má k dispozici nástroj Database Engine Tuning Advisor, sloužící pro nalezení optimálního fyzického návrhu. Jedním z cílů této práce je ukázat, že intuitivní návrh člověka bude často lepší než návrh, který nalezne Database Engine Tuning Advisor. Dalším cílem bude vytvoření aplikace, která umožní otestovat a porovnat více fyzických návrhů pro databázi. Nakonec provedeme porovnání fyzických návrhů pro TPC-H Benchmark databázi, které byly získány jak nástrojem SQL Serveru, tak člověkem.This master's thesis is about automated physical database design and tuning in SQL Server. SQL Server has a Database Engine Tuning Advisor tool used to find the optimal physical database design. One of the goals of this work is to show that the intuitive physical database design built by man will often be better than the design found by Database Engine Tuning Advisor. Another goal will be to create an application for testing and comparing of other physical database designs. Finally, we will compare the TPC-H Benchmark database designs created by both the SQL Server tool and the human designer.460 - Katedra informatikyvýborn
CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads
Index tuning, i.e., selecting the indexes appropriate for a workload, is a
crucial problem in database system tuning. In this paper, we solve index tuning
for large problem instances that are common in practice, e.g., thousands of
queries in the workload, thousands of candidate indexes and several hard and
soft constraints. Our work is the first to reveal that the index tuning problem
has a well structured space of solutions, and this space can be explored
efficiently with well known techniques from linear optimization. Experimental
results demonstrate that our approach outperforms state-of-the-art commercial
and research techniques by a significant margin (up to an order of magnitude).Comment: VLDB201
Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning
Fine tuning distributed systems is considered to be a craftsmanship, relying
on intuition and experience. This becomes even more challenging when the
systems need to react in near real time, as streaming engines have to do to
maintain pre-agreed service quality metrics. In this article, we present an
automated approach that builds on a combination of supervised and reinforcement
learning methods to recommend the most appropriate lever configurations based
on previous load. With this, streaming engines can be automatically tuned
without requiring a human to determine the right way and proper time to deploy
them. This opens the door to new configurations that are not being applied
today since the complexity of managing these systems has surpassed the
abilities of human experts. We show how reinforcement learning systems can find
substantially better configurations in less time than their human counterparts
and adapt to changing workloads
Paraiso : An Automated Tuning Framework for Explicit Solvers of Partial Differential Equations
We propose Paraiso, a domain specific language embedded in functional
programming language Haskell, for automated tuning of explicit solvers of
partial differential equations (PDEs) on GPUs as well as multicore CPUs. In
Paraiso, one can describe PDE solving algorithms succinctly using tensor
equations notation. Hydrodynamic properties, interpolation methods and other
building blocks are described in abstract, modular, re-usable and combinable
forms, which lets us generate versatile solvers from little set of Paraiso
source codes.
We demonstrate Paraiso by implementing a compressive hydrodynamics solver. A
single source code less than 500 lines can be used to generate solvers of
arbitrary dimensions, for both multicore CPUs and GPUs. We demonstrate both
manual annotation based tuning and evolutionary computing based automated
tuning of the program.Comment: 52 pages, 14 figures, accepted for publications in Computational
Science and Discover
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