Konstantinos (Kostas) Kanellis

Publications

1. VLDB’22

   LlamaTune: Sample-Efficient DBMS Configuration Tuning

   Konstantinos Kanellis, Cong Ding, Brian Kroth, Andreas Müller, Carlo Curino, Shivaram Venkataraman

   Proceedings of the VLDB Endowment, Vol. 15, Jul 2022

   Abs Bib PDF Slides Website

   Tuning a database system to achieve optimal performance on a given workload is a long-standing problem in the database community. A number of recent works have leveraged ML-based approaches to guide the sampling of large parameter spaces (hundreds of tuning knobs) in search for high performance configurations. Looking at Microsoft production services operating millions of databases, sample efficiency emerged as a crucial requirement to use tuners on diverse workloads.This motivates our investigation in LlamaTune, a tuner design that leverages domain knowledge to improve the sample efficiency of existing optimizers. LlamaTune employs an automated dimensionality reduction technique based on randomized projections, a biased-sampling approach to handle special values for certain knobs, and knob values bucketization, to reduce the size of the search space. LlamaTune compares favorably with the state-of-the-art optimizers across a diverse set of workloads. It identifies the best performing configurations with up to 11X fewer workload runs, and reaching up to 21% higher throughput. We also show that benefits from LlamaTune generalize across both BO-based and RL-based optimizers, as well as different DBMS versions.While the journey to perform database tuning at cloud-scale remains long, LlamaTune goes a long way in making automatic DBMS tuning practical at scale.

   @article{10.14778/3551793.3551844,
     author = {Kanellis, Konstantinos and Ding, Cong and Kroth, Brian and M\"{u}ller, Andreas and Curino, Carlo and Venkataraman, Shivaram},
     title = {LlamaTune: Sample-Efficient DBMS Configuration Tuning},
     year = {2022},
     issue_date = {July 2022},
     publisher = {VLDB Endowment},
     volume = {15},
     number = {11},
     issn = {2150-8097},
     url = {https://doi.org/10.14778/3551793.3551844},
     doi = {10.14778/3551793.3551844},
     journal = {Proc. VLDB Endow.},
     month = jul,
     pages = {2953–2965},
     numpages = {13},
   }

2. HotStorage’20

   Too Many Knobs to Tune? Towards Faster Database Tuning by Pre-Selecting Important Knobs

   Konstantinos Kanellis, Ramnatthan Alagappan, Shivaram Venkataraman

   12th USENIX Conference on Hot Topics in Storage and File Systems, Jul 2020

   Abs Bib PDF Slides Website

   To achieve high performance, recent research has shown that it is important to automatically tune the configuration knobs present in database systems. However, as database systems usually have 100s of knobs, auto-tuning frameworks spend a significant amount of time exploring the large configuration space and need to repeat this as workloads change. Given this challenge, we ask a more fundamental question of how many knobs do we need to tune in order to achieve good performance. Surprisingly, we find that with YCSB workload-A on Cassandra, tuning just five knobs can achieve 99% of the performance achieved by the best configuration that is obtained by tuning many knobs. We also show that our results hold across workloads and applies to other systems like PostgreSQL, motivating the need for tools that can automatically filter out the knobs that need to be tuned. Based on our results, we propose an initial design for accelerating auto-tuners and detail some future research directions.

   @inbook{10.5555/3488733.3488749,
     author = {Kanellis, Konstantinos and Alagappan, Ramnatthan and Venkataraman, Shivaram},
     title = {Too Many Knobs to Tune? Towards Faster Database Tuning by Pre-Selecting Important Knobs},
     year = {2020},
     publisher = {USENIX Association},
     address = {USA},
     booktitle = {Proceedings of the 12th USENIX Conference on Hot Topics in Storage and File Systems},
     articleno = {16},
     numpages = {1},
   }

3. FPL 2017

   A programming model and runtime system for approximation-aware heterogeneous computing

   Ioannis Parnassos, Nikolaos Bellas, Nikolaos Katsaros, Nikolaos Patsiatzis, Athanasios Gkaras,  Konstantinos Kanellis, Christos D. Antonopoulos, Michalis Spyrou, Manolis Maroudas

   27th International Conference on Field Programmable Logic and Applications (FPL), Sep 2017

   Abs Bib

   Heterogeneous platforms that include diverse architectures such as multicore CPUs, FPGAs and GPUs are becoming very popular due to their superior performance and energy efficiency. Besides heterogeneity, a promising approach for minimizing energy consumption is through approximate computing which relaxes the requirement that all parts of a program are considered equally important to the output quality, thus, all should be executed at full accuracy. Our work extends a traditional OpenMP-like programming model and runtime system to support seamless execution on hybrid architectures with approximation semantics. Starting from a common application code, annotated with our programming model, the programmer can not only target heterogeneous architectures comprising CPU, FPGA and GPU components, but can also regulate the amount of approximation. We evaluate our framework on a number of large-scale applications and demonstrate that the combination of heterogeneous and approximate computing can provide a powerful dynamic interplay between performance and output quality.

   @inproceedings{8056774,
     author = {Parnassos, Ioannis and Bellas, Nikolaos and Katsaros, Nikolaos and Patsiatzis, Nikolaos and Gkaras, Athanasios and Kanellis, Konstantinos and Antonopoulos, Christos D. and Spyrou, Michalis and Maroudas, Manolis},
     booktitle = {2017 27th International Conference on Field Programmable Logic and Applications (FPL)},
     title = {A programming model and runtime system for approximation-aware heterogeneous computing},
     year = {2017},
     volume = {},
     number = {},
     pages = {1-4},
     keywords = {},
     doi = {10.23919/FPL.2017.8056774},
     issn = {1946-1488},
     month = sep,
   }