A green framework for DBMS based on energy-aware query optimization and energy-efficient query processing

Guo, Binglei; Yu, Jiong; Liao, Bin; Yang, Dexian; Lu, Lie

doi:10.1016/j.jnca.2017.02.015

Cited by 23 publications

(8 citation statements)

References 25 publications

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“…There are two different opinions about this balance in current research. Prior work [6][7][8] argue that energy consumption and performance are different objectives. Following this opinion, optimization is a tradeoff since improving performance brings with it higher energy consumption, whereas reducing energy consumption necessarily causes lower performance.…”

Section: Related Workmentioning

confidence: 99%

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Energy efficiency optimization in big data processing platform by improving resources utilization

Song

Thomas

et al. 2019

Sustainable Computing: Informatics and Systems

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Big data creates tremendous value for humanity, but also places a heavy burden on the environment. Energy consumption of computing platforms, especially big data processing platforms, cannot be ignored, and optimization of energy usage is imperative. To the out-of-core data processing, this paper proposes that the energy efficiency of a big data processing platform is closely related to the utilization of its computing resources; and an efficient resource allocation strategy for data processing tasks improves the platform's resources utilization. To improve the resources utilization, different resources are allocated according to a taskrelated Best Resource Ratio (BRR for short), such as "cpu, disk, network in the ratio of 1:2:4", rather than the resource's quantity, such as "cpu=1GHz, network=20MB/s". We deduce the BRR of data processing tasks, and design a resource ratio based approach (R 2 ), which includes a task scheduling algorithm and resource allocation algorithm, for energy efficiency optimization. Experiments show that the R 2 approach can improve energy efficiency by 10%, 10% and 6% compared to FIFO, Capacity and Fair schedulers respectively, by maximizing resource utilization of a 12-node MapReduce platform.

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Section: Related Workmentioning

confidence: 99%

“…, the data consumer (outflow from memory) is blocked and idle. According to the definition of the τ function and the relationship between resource throughput and resource quantities, ( 7) is transformed to (8).…”

Section: A C C E P T E D Mmentioning

confidence: 99%

Energy efficiency optimization in big data processing platform by improving resources utilization

Song

Thomas

et al. 2019

Sustainable Computing: Informatics and Systems

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“…PET estimates the energy costs of queries offline and the evaluation engine of the DBMS configures PET parameters towards a desired energy/performance trade-off. Guo et al [16] propose an energy efficient query processing framework in DBMSs. Their approach works out energy cost query plans and makes a trade-off between the performance and the energy plans.…”

Section: Related Workmentioning

confidence: 99%

“…(given in Equation 1 , respectively and C is the sum of energy consumption of nodes in V opt . The ILP problem is formulated as follows: 14and (15) collectively define the deadline constraints, Equations (16), (17), (18) and (19) collectively define the precedence constraints.…”

Section: Ilp-based Algorithmmentioning

confidence: 99%

Energy-Aware Scheduling of Conditional Task Graphs on NoC-Based MPSoCs

Tariq¹,

Ishak²

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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We investigate the problem of scheduling a set of tasks with individual deadlines and conditional precedence constraints on a heterogeneous Network on Chip (NoC)-based Multi-Processor System-on-Chip (MPSoC) such that the total expected energy consumption of all the tasks is minimized, and propose a novel approach. Our approach consists of a scheduling heuristic for constructing a single unified schedule for all the tasks and assigning a frequency to each task and each communication assuming continuous frequencies, an Integer Linear Programming (ILP)-based algorithm and a polynomial time heuristic for assigning discrete frequencies and voltages to tasks and communications. We have performed experiments on 16 synthetic and 4 real-world benchmarks. The experimental results show that compared to the state-of-theart approach, our approach using the ILP-based algorithm and our approach using the polynomial-time heuristic achieve average improvements of 31% and 20%, respectively, in terms of energy reduction. compared to v j and traverse the same links that v j traverses. 4) If v j is a task node, compute its finish time ζ j = r j + t k,j , and insert unconditional directed edges from v j to unscheduled nodes concurrent to v j and mapped on the same processor where v j is mapped. 5) Delete v j from ReadySet and insert all ready nodes in G to ReadySet. Consider the CTG in Figure 1(b) and the MPSoC in Figure 2(a) where all the processors are identical. The execution times of tasks at the maximum processor frequency are t 1,1

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“…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

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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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A green framework for DBMS based on energy-aware query optimization and energy-efficient query processing

Cited by 23 publications

References 25 publications

Energy efficiency optimization in big data processing platform by improving resources utilization

Energy efficiency optimization in big data processing platform by improving resources utilization

Energy-Aware Scheduling of Conditional Task Graphs on NoC-Based MPSoCs

Big Steps Towards Query Eco-Processing - Thinking Smart

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