Towards energy-efficient database cluster design

Lang, Willis; Harizopoulos, Stavros; Patel, Jignesh M.; Shah, Mehul A.; Tsirogiannis, Dimitris

doi:10.14778/2350229.2350280

Cited by 36 publications

(28 citation statements)

References 24 publications

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“…stated in the summary of [17], evaluating the energy efficiency of a DBMS needs the inclusion of entire workloads, not just single queries. This study makes use of three different and complete workloads that allows a more comprehensive look at the energy efficiency of a relational DBMS.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Energy Efficiency of OLTP Operations

Niemann

Korfiatis

Zicari

et al. 2013

Availability, Reliability, and Security in Information Systems and HCI

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Abstract. With the continuous increase of online services as well as energy costs, energy consumption becomes a significant cost factor for the evaluation of data center operations. A significant contributor to that is the performance of database servers which are found to constitute the backbone of online services. From a software approach, while a set of novel data management technologies appear in the market e.g. key-value based or in-memory databases, classic relational database management systems (RDBMS) are still widely used. In addition from a hardware perspective, the majority of database servers is still using standard magnetic hard drives (HDDs) instead of solid state drives (SSDs) due to lower cost of storage per gigabyte, disregarding the performance boost that might be given due to high cost.In this study we focus on a software based assessment of the energy consumption of a database server by running three different and complete database workloads namely TCP-H, Star Schema Benchmark -SSB as well a modified benchmark we have derived for this study called W22. We profile the energy distribution among the most important server components and by using different resource allocation we assess the energy consumption of a typical open source RDBMS (PostgreSQL) on a standard server in relation with its performance (measured by query time).Results confirm the well-known fact that even for complete workloads, optimization of the RDBMS results to lower energy consumption.

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Section: Discussionmentioning

confidence: 99%

Evaluating the Energy Efficiency of OLTP Operations

Niemann

Korfiatis

Zicari

et al. 2013

Availability, Reliability, and Security in Information Systems and HCI

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“…Thus we adopt a hybrid solution where the namenode and resourcemanager run on one Dell server (master) and the datanode and node-manager run on the 35 Edison nodes (slaves). We also seek to understand the scalability of Hadoop on Edison cluster, since [30] points out that the overhead of coordination and data shipping causes "friction loss" that dilutes the benefits of a low-power cluster.…”

Section: Mapreduce Workloadsmentioning

confidence: 99%

An experimental evaluation of datacenter workloads on low-power embedded micro servers

Zhao

Shen

et al. 2016

Proc. VLDB Endow.

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This paper presents a comprehensive evaluation of an ultralow power cluster, built upon the Intel Edison based micro servers. The improved performance and high energy efficiency of micro servers have driven both academia and industry to explore the possibility of replacing conventional brawny servers with a larger swarm of embedded micro servers. Existing attempts mostly focus on mobile-class micro servers, whose capacities are similar to mobile phones. We, on the other hand, target on sensor-class micro servers, which are originally intended for uses in wearable technologies, sensor networks, and Internet-of-Things. Although sensor-class micro servers have much less capacity, they are touted for minimal power consumption (< 1 Watt), which opens new possibilities of achieving higher energy efficiency in datacenter workloads. Our systematic evaluation of the Edison cluster and comparisons to conventional brawny clusters involve careful workload choosing and laborious parameter tuning, which ensures maximum server utilization and thus fair comparisons. Results show that the Edison cluster achieves up to 3.5× improvement on work-done-per-joule for web service applications and data-intensive MapReduce jobs. In terms of scalability, the Edison cluster scales linearly on the throughput of web service workloads, and also shows satisfactory scalability for MapReduce workloads despite coordination overhead.

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“…They conclude that the most energy-efficient operating point is also the highest performing configuration. Willis et al [12] study the trade-offs between performance scalability and energy efficiency for relational databases. They identify hardware and software bottlenecks that affect performance scalability and energy efficiency.…”

Section: Related Workmentioning

confidence: 99%

On the Energy Proportionality of Distributed NoSQL Data Stores

Subramaniam

Feng

2015

Lecture Notes in Computer Science

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Abstract. The computing community is facing several big data challenges due to the unprecedented growth in the volume and variety of data. Many large-scale Internet companies use distributed NoSQL data stores to mitigate these challenges. These NoSQL data-store installations require massive computing infrastructure, which consume significant amount of energy and contribute to operational costs. This cost is further aggravated by the lack of energy proportionality in servers. Therefore, in this paper, we study the energy proportionality of servers in the context of a distributed NoSQL data store, namely Apache Cassandra. Towards this goal, we measure the power consumption and performance of a Cassandra cluster. We then use power and resource provisioning techniques to improve the energy proportionality of the cluster and study the feasibility of achieving an energy-proportional data store. Our results show that a hybrid (i.e., power and resource) provisioning technique provides the best power savings -as much as 55%.

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Towards energy-efficient database cluster design

Cited by 36 publications

References 24 publications

Evaluating the Energy Efficiency of OLTP Operations

Evaluating the Energy Efficiency of OLTP Operations

An experimental evaluation of datacenter workloads on low-power embedded micro servers

On the Energy Proportionality of Distributed NoSQL Data Stores

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