Standard

Benchmarking Hashing Algorithms for Load Balancing in a Distributed Database Environment. / Slesarev, Alexander; Mikhailov, Mikhail ; Chernishev, George (Author and editor).

Advances in Model and Data Engineering in the Digitalization Era: MEDI 2022 Short Papers and DETECT 2022 Workshop Papers, Cairo, Egypt, November 21–24, 2022, Proceedings. ed. / Philippe Fournier-Viger; et al. Springer Nature, 2023. p. 105-118 (Communications in Computer and Information Science; Vol. 1751).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research

Harvard

Slesarev, A, Mikhailov, M & Chernishev, G 2023, Benchmarking Hashing Algorithms for Load Balancing in a Distributed Database Environment. in P Fournier-Viger & EA (eds), Advances in Model and Data Engineering in the Digitalization Era: MEDI 2022 Short Papers and DETECT 2022 Workshop Papers, Cairo, Egypt, November 21–24, 2022, Proceedings. Communications in Computer and Information Science, vol. 1751, Springer Nature, pp. 105-118, MEDI 2022 Short Papers and DETECT 2022 Workshop Papers, Cairo, Egypt, 21/11/22. https://doi.org/10.1007/978-3-031-23119-3_8

APA

Slesarev, A., Mikhailov, M., & Chernishev, G. (2023). Benchmarking Hashing Algorithms for Load Balancing in a Distributed Database Environment. In P. Fournier-Viger, & E. A. (Eds.), Advances in Model and Data Engineering in the Digitalization Era: MEDI 2022 Short Papers and DETECT 2022 Workshop Papers, Cairo, Egypt, November 21–24, 2022, Proceedings (pp. 105-118). (Communications in Computer and Information Science; Vol. 1751). Springer Nature. https://doi.org/10.1007/978-3-031-23119-3_8

Vancouver

Slesarev A, Mikhailov M, Chernishev G. Benchmarking Hashing Algorithms for Load Balancing in a Distributed Database Environment. In Fournier-Viger P, EA, editors, Advances in Model and Data Engineering in the Digitalization Era: MEDI 2022 Short Papers and DETECT 2022 Workshop Papers, Cairo, Egypt, November 21–24, 2022, Proceedings. Springer Nature. 2023. p. 105-118. (Communications in Computer and Information Science). https://doi.org/10.1007/978-3-031-23119-3_8

Author

Slesarev, Alexander ; Mikhailov, Mikhail ; Chernishev, George . / Benchmarking Hashing Algorithms for Load Balancing in a Distributed Database Environment. Advances in Model and Data Engineering in the Digitalization Era: MEDI 2022 Short Papers and DETECT 2022 Workshop Papers, Cairo, Egypt, November 21–24, 2022, Proceedings. editor / Philippe Fournier-Viger ; et al. Springer Nature, 2023. pp. 105-118 (Communications in Computer and Information Science).

BibTeX

@inproceedings{d77baf7da8294218ae16f0d8b5d1198b,
title = "Benchmarking Hashing Algorithms for Load Balancing in a Distributed Database Environment",
abstract = "Modern high load applications store data using multiple database instances. Such an architecture requires data consistency, and it is important to ensure even distribution of data among nodes. Load balancing is used to achieve these goals.Hashing is the backbone of virtually all load balancing systems. Since the introduction of classic Consistent Hashing, many algorithms have been devised for this purpose.One of the purposes of the load balancer is to ensure storage cluster scalability. It is crucial for the performance of the whole system to transfer as few data records as possible during node addition or removal. The load balancer hashing algorithm has the greatest impact on this process.In this paper we experimentally evaluate several hashing algorithms used for load balancing, conducting both simulated and real system experiments. To evaluate algorithm performance, we have developed a benchmark suite based on Unidata MDM—a scalable toolkit for various Master Data Management (MDM) applications. For assessment, we have employed three criteria—uniformity of the produced distribution, the number of moved records, and computation speed. Following the results of our experiments, we have created a table, in which each algorithm is given an assessment according to the abovementioned criteria.",
keywords = "Консистентное хеширование, Базы данных, Сравнительный анализ, Consistent hashing, databases, Benchmarking",
author = "Alexander Slesarev and Mikhail Mikhailov and George Chernishev",
note = "Slesarev, A., Mikhailov, M., Chernishev, G. (2022). Benchmarking Hashing Algorithms for Load Balancing in a Distributed Database Environment. In: , et al. Advances in Model and Data Engineering in the Digitalization Era. MEDI 2022. Communications in Computer and Information Science, vol 1751. Springer, Cham. https://doi.org/10.1007/978-3-031-23119-3_8; MEDI 2022 Short Papers and DETECT 2022 Workshop Papers ; Conference date: 21-11-2022 Through 24-11-2022",
year = "2023",
doi = "https://doi.org/10.1007/978-3-031-23119-3_8",
language = "English",
isbn = "978-3-031-23118-6",
series = "Communications in Computer and Information Science",
publisher = "Springer Nature",
pages = "105--118",
editor = "Fournier-Viger, {Philippe } and {et al.}",
booktitle = "Advances in Model and Data Engineering in the Digitalization Era",
address = "Germany",

}

RIS

TY - GEN

T1 - Benchmarking Hashing Algorithms for Load Balancing in a Distributed Database Environment

AU - Slesarev, Alexander

AU - Mikhailov, Mikhail

A2 - Chernishev, George

A2 - Fournier-Viger, Philippe

A2 - , et al.

N1 - Slesarev, A., Mikhailov, M., Chernishev, G. (2022). Benchmarking Hashing Algorithms for Load Balancing in a Distributed Database Environment. In: , et al. Advances in Model and Data Engineering in the Digitalization Era. MEDI 2022. Communications in Computer and Information Science, vol 1751. Springer, Cham. https://doi.org/10.1007/978-3-031-23119-3_8

PY - 2023

Y1 - 2023

N2 - Modern high load applications store data using multiple database instances. Such an architecture requires data consistency, and it is important to ensure even distribution of data among nodes. Load balancing is used to achieve these goals.Hashing is the backbone of virtually all load balancing systems. Since the introduction of classic Consistent Hashing, many algorithms have been devised for this purpose.One of the purposes of the load balancer is to ensure storage cluster scalability. It is crucial for the performance of the whole system to transfer as few data records as possible during node addition or removal. The load balancer hashing algorithm has the greatest impact on this process.In this paper we experimentally evaluate several hashing algorithms used for load balancing, conducting both simulated and real system experiments. To evaluate algorithm performance, we have developed a benchmark suite based on Unidata MDM—a scalable toolkit for various Master Data Management (MDM) applications. For assessment, we have employed three criteria—uniformity of the produced distribution, the number of moved records, and computation speed. Following the results of our experiments, we have created a table, in which each algorithm is given an assessment according to the abovementioned criteria.

AB - Modern high load applications store data using multiple database instances. Such an architecture requires data consistency, and it is important to ensure even distribution of data among nodes. Load balancing is used to achieve these goals.Hashing is the backbone of virtually all load balancing systems. Since the introduction of classic Consistent Hashing, many algorithms have been devised for this purpose.One of the purposes of the load balancer is to ensure storage cluster scalability. It is crucial for the performance of the whole system to transfer as few data records as possible during node addition or removal. The load balancer hashing algorithm has the greatest impact on this process.In this paper we experimentally evaluate several hashing algorithms used for load balancing, conducting both simulated and real system experiments. To evaluate algorithm performance, we have developed a benchmark suite based on Unidata MDM—a scalable toolkit for various Master Data Management (MDM) applications. For assessment, we have employed three criteria—uniformity of the produced distribution, the number of moved records, and computation speed. Following the results of our experiments, we have created a table, in which each algorithm is given an assessment according to the abovementioned criteria.

KW - Консистентное хеширование

KW - Базы данных

KW - Сравнительный анализ

KW - Consistent hashing

KW - databases

KW - Benchmarking

UR - https://www.mendeley.com/catalogue/d6a1773e-cf0c-3b61-9698-42370d4d29ff/

U2 - https://doi.org/10.1007/978-3-031-23119-3_8

DO - https://doi.org/10.1007/978-3-031-23119-3_8

M3 - Conference contribution

SN - 978-3-031-23118-6

T3 - Communications in Computer and Information Science

SP - 105

EP - 118

BT - Advances in Model and Data Engineering in the Digitalization Era

PB - Springer Nature

T2 - MEDI 2022 Short Papers and DETECT 2022 Workshop Papers

Y2 - 21 November 2022 through 24 November 2022

ER -

ID: 102475769