A system for energy-efficient data management

Tu, Yi-Cheng; Wang, Xiaorui; Zeng, Bo; Xu, Zichen

doi:10.1145/2627692.2627696

Cited by 26 publications

(5 citation statements)

References 15 publications

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“…Adopting dynamic powermanagement could help improve energy-efficiency. For example the studies in [16], [52], [43] use the frequency and/or voltage mechanism that allow DBMS to trade energy consumption for performance. In order to save energy, they manage the DVFS level based on throughput target and workload characteristics (e.g., I/O or CPU intensive).…”

Section: Related Workmentioning

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

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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.

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Section: Related Workmentioning

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

View full text Add to dashboard Cite

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“…Many research design the specialized file system for controlling the layout of data on disk [5], reducing file fragments, increasing the sequential access rate so as to improve the energy consumption of the disk [6]. In addition, many energy saving technologies focus on dynamic reduction of the number of high energy disks [7], aggregation of hotspot data on some disks and dynamic configuration of cache devices [8] .…”

Section: Related Workmentioning

confidence: 99%

An energy Conservation Scheme in Video Monitoring Storage Systems

Zhong¹,

Tu²

2017

Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 20

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Abstract. Disk-based network video monitoring storage system has become a popular trend. By careful analysis on video monitoring access characteristics, this paper proposed a low-power hard disk storage scheme which design a specialized data layout methods and disk access model to improve the capabilities of video storage server to support concurrent video data streaming. By means of reducing the number of disks working in high energy consumption, the scheme ultimately achieving the purpose of saving the energy consumption of the storage system.

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“…Other studies propose an energy cost model, using a variety of techniques and metrics, and integrate it into the query planners to direct it towards energy-efficient plans without sacrificing performance significantly [14,3,31,33,32,13,25,27]. Others explicitly exploit energy saving features of hardware [13,19,20].…”

Section: Related Workmentioning

confidence: 99%

Energy Efficiency of ORM Approaches

Procaccianti

Lago

Diesveld³

2016

Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

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Context. Object-Relational Mapping (ORM) frameworks are widely used in business software applications to interact with database systems. Even if ORMs introduce several benefits when compared to a plain SQL approach, these techniques have known disadvantages. Goal. In this paper, we present an empirical study that evaluates the energy efficiency of three different approaches to programmatically access SQL databases in PHP applications. The selected approaches are: plain SQL queries in the source code, and two specialized frameworks, Propel and TinyQueries. Method. We performed an empirical experiment in a controlled environment. We selected three factors for our experimentation: the different ORM approaches, the type of query (Create, Read, Update, Delete) and the size of database tables. Our response variables were execution time and energy consumption. Results. As expected, pure SQL yielded the best performance and energy efficiency in all test cases. Propel exhibited a much higher energy consumption and longer execution times. The TinyQueries tool performed slightly worse than SQL, but significantly better than Propel, offering a convenient trade-off between ORM benefits and energy efficiency. Conclusions. Our experiment shows that ORM approaches have a significant impact on both energy consumption and performance. This helps developers and architects when considering the trade-off between their benefits (e.g. in terms of code maintainability and readability) and drawbacks.

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A system for energy-efficient data management

Cited by 26 publications

References 15 publications

Big Steps Towards Query Eco-Processing - Thinking Smart

Big Steps Towards Query Eco-Processing - Thinking Smart

An energy Conservation Scheme in Video Monitoring Storage Systems

Energy Efficiency of ORM Approaches

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