Peak power plays in database engines

Kunjir, Mayuresh; Birwa, Puneet K.; Haritsa, Jayant R.

doi:10.1145/2247596.2247648

Cited by 27 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors adapt their static model to dynamic workloads using a feedback control mechanism to periodically update model parameters using real-time energy measurements. The authors of [10] propose a technique for modeling the peak power of database operations. A pipeline-based model of query execution plans was developed to identify the sources of the peak power consumption for a query and to recommend plans with low peak power.…”

Section: Related Workmentioning

confidence: 99%

“…Based on their proposed framework, they choose query plans that reduce energy consumption. In [10], cost-based driven approach is proposed to generate query plans minimizing the peak power. In [19], genetic algorithm with a fitness function based on a energy consumption cost model, is given to select materialize views reducing energy and optimizing queries.…”

Section: Optimization Techniquesmentioning

confidence: 99%

“…The most used non-functional requirement by query optimizers represents the workload performance. Choosing query plans with low-energy consumption without sacrificing the performance was the motivation of several research studies [23, 10,11]. Their main results show the existence of power-performance trade-off in database query optimization.…”

Section: Query Optimizationmentioning

confidence: 99%

“…We use a similar setup used in the state-of-arts [11,24,10]. Our machine is equipped with a "Watts UP?…”

Section: Experiments Setupmentioning

confidence: 99%

See 3 more Smart Citations

Obre: Offer-Borrow-Reform-Evaluate Initiatives for Green DBMSS

Roukh

Bellatreche

Tziritas

2019

AzJHPC

View full text Add to dashboard Cite

In the last few years, we have been seeing a significant increase in research about the energy efficiency of hardware and software components by both academic and industry. Today, energy efficiency is one of the most challenging issues in the area of information technologies and communication. In data-centric applications, database management systems are one of the major energy consumers, in which, a large amount of data is queried by complex queries running daily. Designing and implementing of an energy-aware DBMS that enables significant energy conservation while processing queries become a necessary need. Traditionally, existing DBMSs focus to high-performance during query optimization phase, while totally ignoring the energy consumption of the queries. In this paper, we propose a methodology, supported by a tool called EcoProD, focusing on query optimizers. To show its effectiveness, we implement it in Post-greSQL DBMS aiming reducing energy consumption without degrading query response time. A mathematical cost model is used to estimate the energy consumption. Its parameters are identified by a machine learning technique. We conduct intensive experiments using our cost models and a measurement tool dedicated to compute energy using dataset of TPC-H benchmark. Based on the obtained results, a probabilistic proof to demonstrate the confidence bounds of our model and results is given. CCS Concepts: Information systems-Relational database model; DBMS engine architectures; Database query processing; Relational database query languages;

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Optimization Techniquesmentioning

confidence: 99%

Section: Query Optimizationmentioning

confidence: 99%

“…We use a similar setup used in the state-of-arts [11,24,10]. Our machine is equipped with a "Watts UP?…”

Section: Experiments Setupmentioning

confidence: 99%

See 2 more Smart Citations

Obre: Offer-Borrow-Reform-Evaluate Initiatives for Green DBMSS

Roukh

Bellatreche

Tziritas

2019

AzJHPC

View full text Add to dashboard Cite

show abstract

“…Another work [10] proposed query rescheduling and CPU frequency control as two means for green data management and supported their claims with experimental results. Various other topics such as energy quantification of database servers [13,14,17], benchmarking [15], cost-based query plan evaluation [9] are also reported. As compared to the above projects, we focus on a systematic framework for energy conservation inside a DBMS rather than individual means.…”

Section: Introductionmentioning

confidence: 99%

A system for energy-efficient data management

Wang

Zeng

et al. 2014

SIGMOD Rec.

View full text Add to dashboard Cite

Energy consumption of computer systems has increased at a steep rate in recent years. Following extensive energyrelated research and practice in the hardware and OS communities, much attention has been paid to developing energy-efficient applications. With database systems being a heavy energy consumer in modern data centers, we face the challenge of designing DBMSs with energy as a first-class performance goal. This paper presents our on-going work in designing and implementing a DB-MS that enables significant energy conservations while maintaining other performance targets. We follow two new strategies in DBMS implementation to achieve our system design goal. The first one is to change the resource consumption patterns via energy-aware query optimization and reorganizing data records to enable load consolidation in disks. The second strategy is active control of power modes of hardware (i.e., CPU and hard disks) toward energy reduction. Specifically, we use control-theoretic techniques to allow dynamic adjustment of CPU frequency and online data migration to achieve disk load consolidation. Preliminary results have shown the effectiveness of our design.

show abstract

Energy consumption analysis of data stream processing: a benchmarking approach

Dayarathna

Wen

et al. 2016

Softw Pract Exp

View full text Add to dashboard Cite

Summary Energy efficiency of data analysis systems has become a very important issue in recent times because of the increasing costs of data center operations. Although distributed streaming workloads have increasingly been present in modern data centers, energy‐efficient scheduling of such applications remains as a significant challenge. In this paper, we conduct an energy consumption analysis of data stream processing systems in order to identify their energy consumption patterns. We follow stream system benchmarking approach to solve this issue. Specifically, we implement Linear Road benchmark on six stream processing environments (S4, Storm, ActiveMQ, Esper, Kafka, and Spark Streaming) and characterize these systems' performance on a real‐world data center. We study the energy consumption characteristics of each system with varying number of roads as well as with different types of component layouts. We also use a microbenchmark to capture raw energy consumption characteristics. We observed that S4, Esper, and Spark Streaming environments had highest average energy consumption efficiencies compared with the other systems. Using a neural networkbased technique with the power/performance information gathered from our experiments, we developed a model for the power consumption behavior of a streaming environment. We observed that energy‐efficient execution of streaming application cannot be specifically attributed to the system CPU usage. We observed that communication between compute nodes with moderate tuple sizes and scheduling plans with balanced system overhead produces better power consumption behaviors in the context of data stream processing systems. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

Peak power plays in database engines

Cited by 27 publications

References 13 publications

Obre: Offer-Borrow-Reform-Evaluate Initiatives for Green DBMSS

Obre: Offer-Borrow-Reform-Evaluate Initiatives for Green DBMSS

A system for energy-efficient data management

Energy consumption analysis of data stream processing: a benchmarking approach

Contact Info

Product

Resources

About