Think big, start small: a good initiative to design green query optimizers

Dembele, Simon Pierre; Bellatreche, Ladjel; Ordóñez, Carlos; Roukh, Amine

doi:10.1007/s10586-019-03005-0

Cited by 13 publications

(3 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the prediction error rate for sequential read mode is even lower, signifying higher accuracy relative to other models. (5) As the data table size for SQL statement operations increases, the model's prediction errors correspondingly escalate. This variation highlights a clear association between model inaccuracies and the volume of data involved in SQL statement operations.…”

Section: Discussionmentioning

confidence: 99%

“…In recent studies, Hu et al 4 presented an energy-saving approach that combined hardware and software to create and validate the ECM for each component of a system, with the relational database’s energy consumption as the key evaluation metric. Dembele et al 5 estimated energy consumption through Greenplum database queries, investigated strategies to enhance connection energy efficiency and evaluated the variance in connection calculation time under different scales and connection modes. Zhou et al 6 focused on the calculation and energy-efficient collaborative scheduling for Green DBMS centers to optimize their overall energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Applying self-powered sensor and support vector machine in load energy consumption modeling and prediction of relational database

Yang,

Yu,

et al. 2023

Sci Rep

View full text Add to dashboard Cite

This study explores the analysis and modeling of energy consumption in the context of database workloads, aiming to develop an eco-friendly database management system (DBMS). It leverages vibration energy harvesting systems with self-sustaining wireless vibration sensors (WVSs) in combination with the least square support vector machine algorithm to establish an energy consumption model (ECM) for relational database workloads. Through experiments, the performance of self-sustaining WVS in providing power is validated, and the accuracy of the proposed ECM during the execution of Structured Query Language (SQL) statements is evaluated. The findings demonstrate that this approach can reliably predict the energy consumption of database workloads, with a maximum prediction error rate of 10% during SQL statement execution. Furthermore, the ECM developed for relational databases closely approximates actual energy consumption for query operations, with errors ranging from 1 to 4%. In most cases, the predictions are conservative, falling below the actual values. This finding underscores the high predictive accuracy of the ECM in anticipating relational database workloads and their associated energy consumption. Additionally, this paper delves into prediction accuracy under different types of operations and reveals that ECM excels in single-block read operations, outperforming multi-block read operations. ECM exhibits substantial accuracy in predicting energy consumption for SQL statements in sequential and random read modes, especially in specialized database management system environments, where the error rate for the sequential read model is lower. In comparison to alternative models, the proposed ECM offers superior precision. Furthermore, a noticeable correlation between model error and the volume of data processed by SQL statements is observed. In summary, the relational database ECM introduced in this paper provides accurate predictions of workload and database energy consumption, offering a theoretical foundation and practical guidance for the development of eco-friendly DBMS.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying self-powered sensor and support vector machine in load energy consumption modeling and prediction of relational database

Yang,

Yu,

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In this paper, we focus on building a green query processor -considered as one of the most important energy consumers of the DBMS [11] by proposing several costs models estimating the energy consumption of a query executed by an optimizer in several DBMSs offering parallel mode with different functioning policies. The crucial aspect of our work that differentiates it from previous works [63,66,42,70,81,80,79] is the inclusion of the main memory (RAM) parameter in our cost model for PostgreSQL and MonetDB. Additionally, we propose a novel cost model for an in-memory system named Hyrise.…”

Section: Introductionmentioning

confidence: 99%

Big Steps Towards Query Eco-Processing - Thinking Smart

Dembele

Bellatreche

Ordóñez

et al. 2021

Revue Africaine De Recherche en Informatique Et Mathématiques Appliquées

Self Cite

View full text Add to dashboard Cite

Soumission à Episciences International audience Computers and electronic machines in businesses consume a significant amount of electricity, releasing carbon dioxide (CO2), which contributes to greenhouse gas emissions. Energy efficiency is a pressing concern in IT systems, ranging from mobile devices to large servers in data centers, in order to be more environmentally responsible. In order to meet the growing demands in the awareness of excessive energy consumption, many initiatives have been launched on energy efficiency for big data processing covering electronic components, software and applications. Query optimizers are one of the most power consuming components of a DBMS. They can be modified to take into account the energetical cost of query plans by using energy-based cost models with the aim of reducing the power consumption of computer systems. In this paper, we study, describe and evaluate the design of three energy cost models whose values of energy sensitive parameters are determined using the Nonlinear Regression and the Random Forests techniques. To this end, we study in depth the operating principle of the selected DBMS and present an analysis comparing the performance time and energy consumption of typical queries in the TPC benchmark. We perform extensive experiments on a physical testbed based on PostreSQL, MontetDB and Hyrise systems using workloads generatedusing our chosen benchmark to validate our proposal. Les ordinateurs et les machines électroniques des entreprises consomment une quantité importante d’électricité, libérant ainsi du dioxyde de carbone (CO2), qui contribue aux émissions de gaz à effet de serre. L’efficacité énergétique est une préoccupation urgente dans les systèmesinformatiques, partant des équipements mobiles aux grands serveurs dans les centres de données, afin d’être plus respectueux envers l’environnement. Afin de répondre aux exigences croissantes en matière de sensibilisation à l’utilisation excessive de l’énergie, de nombreuses initiatives ont été lancées sur l’efficacité énergétique pour le traitement des données massives couvrant les composantsélectroniques, les logiciels et les applications. Les optimiseurs de requêtes sont l’un des composants les plus énergivores d’un SGBD. Ils peuvent être modifiés pour prendre en compte le coût énergétique des plans des requêtes à l’aide des modèles de coût énergétiques intégrés dans l’optimiseur dans le but de réduire la consommation électrique des systèmes informatiques. Dans cet article, nousétudions, décrivons et évaluons la conception de trois modèles de coût énergétique dont les valeurs des paramètres sensibles à l’énergie sont définis en utilisant la technique de la Régression non linéaire et la technique des forêts aléatoires. Pour ce fait, nous menons une étude approfondie du principe de fonctionnement des SGBD choisis et présentons une analyse des performances en termes de temps et énergie sur des requêtes typiques du benchmarks TPC-H. Nous effectuons des expériences approfondies basées sur les systèmes PostgreSQL, MonetDB et Hyrise en utilisant un jeu de données généré à partir du benchmarks TPC-H afin de valider nos propositions.

show abstract

Internet of Things in the Construction Industry: A General Overview

Lorusso

Celenta

2023

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Think big, start small: a good initiative to design green query optimizers

Cited by 13 publications

References 42 publications

Applying self-powered sensor and support vector machine in load energy consumption modeling and prediction of relational database

Applying self-powered sensor and support vector machine in load energy consumption modeling and prediction of relational database

Big Steps Towards Query Eco-Processing - Thinking Smart

Internet of Things in the Construction Industry: A General Overview

Contact Info

Product

Resources

About