Comparing and Analyzing the Energy Efficiency of Cloud Database and Parallel Database

Song, Jie; Li, Tiantian; Liu, Xuebing; Zhu, Zhiliang

doi:10.1007/978-3-642-30111-7_95

Cited by 7 publications

(8 citation statements)

References 8 publications

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“…Even though these data warehouses support ROLAP-like functions, the performances are unsatisfactory. The reasons for this situation are: (1) these systems do not provide big data oriented OLAP optimizations; (2) the join operation, which is quite common operation in ROLAP, is very inefficient when big data are involved (Song et al, 2012). In this work we provide evidences in Section 7 to prove that when data amount or query complexity increases the performance of ROLAP-tools decreases.…”

Section: Introductionmentioning

confidence: 94%

HaoLap: A Hadoop based OLAP system for big data

Song

Guo

Wang

et al. 2015

Journal of Systems and Software

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In recent years, facing information explosion, industry and academia have adopted distributed file system and MapReduce programming model to address new challenges the big data has brought. Based on these technologies, this paper presents HaoLap (Hadoop based oLap), an OLAP (OnLine Analytical Processing) system for big data. Drawing on the experience of Multidimensional OLAP (MOLAP), HaoLap adopts the specified multidimensional model to map the dimensions and the measures; the dimension coding and traverse algorithm to achieve the roll up operation on dimension hierarchy; the partition and linearization algorithm to store dimensions and measures; the chunk selection algorithm to optimize OLAP performance; and MapReduce to execute OLAP. The paper illustrates the key techniques of HaoLap including system architecture, dimension definition, dimension coding and traversing, partition, data storage, OLAP and data loading algorithm. We evaluated HaoLap on a real application and compared it with Hive, HadoopDB, HBaseLattice, and Olap4Cloud. The experiment results show that HaoLap boost the efficiency of data loading, and has a great advantage in the OLAP performance of the data set size and query complexity, and meanwhile HaoLap also completely support dimension operations.

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Section: Introductionmentioning

confidence: 94%

HaoLap: A Hadoop based OLAP system for big data

Song

Guo

Wang

et al. 2015

Journal of Systems and Software

Self Cite

View full text Add to dashboard Cite

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“…for prefix ∈ prefixs do 16: digits in the combination and digitMax gives the maximum value for each digit. Namely, digitNum and digitMax refer to the number of the nodes and the number of the available frequency options respectively.…”

Section: Algorithm 1 Locate Boundary Combinations Algorithmmentioning

confidence: 99%

“…They gave the conclusion that within a single node intended for use in scale-out (shared-nothing) architectures, the most energy-efficient configuration is typically the highest performing one. Jie Song et al [16,17] gave the definition of the energy efficiency in cloud database systems and a novel evaluation model to compute the energy efficiency. In their model, the relationship between energy efficiency and node's CPU frequency and CPU usage is studied.…”

Section: Related Workmentioning

confidence: 99%

Hot-N-Cold model for energy aware cloud databases

Guo

Pierson

Song

et al. 2019

Journal of Parallel and Distributed Computing

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h i g h l i g h t s • The energy efficiency in cloud database systems is discussed. • The resource-providing problem in cloud database systems is considered as two bounded problems which are studied in this work. • Under Hot-N-Cold model, nonlinear programming algorithm and multiphase algorithm are proposed to solve the bounded problems. • The simulation and real case experiments are designed and implemented to measure the proposed algorithms in aspect of performance and assurance.

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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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Comparing and Analyzing the Energy Efficiency of Cloud Database and Parallel Database

Cited by 7 publications

References 8 publications

HaoLap: A Hadoop based OLAP system for big data

HaoLap: A Hadoop based OLAP system for big data

Hot-N-Cold model for energy aware cloud databases

Energy Efficiency of ORM Approaches

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