Benchmarking scalability and elasticity of distributed database systems

Kuhlenkamp, Jörn; Klems, Markus; Röss, Oliver

doi:10.14778/2732977.2732995

Cited by 90 publications

(77 citation statements)

References 11 publications

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“…Chalkiadaki and Magoutis [6], Dede et al [11], Kuhlenkamp et al [18], Rabl et al [25], Shankaranarayanan et al [28], Shi et al [30] are studies focusing on the horizontal scalability feature offered by such databases. Few studies consider vertical scaling, e.g.…”

Section: Related Workmentioning

confidence: 99%

“…Few studies consider vertical scaling, e.g. [6,18], and configuration tuning [6,12,22,28]. While Horizontal scaling, vertical scaling and configutation tuning approaches are somentime mixed, optimal placement (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…These benchmarking experiments has been extended in [18] with a performance evaluation of Cassandra on different Amazon EC2 infrastructure configurations. In comparison with those researches we consider only read, write and read and write requests because of interest for our industrial case.…”

Section: Related Workmentioning

confidence: 99%

“…However, our model is independent from the specific type of query. In [18] the authors explore both horizontal and vertical scalability. Their results confirms the experience we had with Cassandra performance on a virtualized environment.…”

Section: Related Workmentioning

confidence: 99%

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Energy-aware auto-scaling algorithms for Cassandra virtual data centers

2017

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Apache Cassandra is an highly scalable and available NoSql datastore, largely used by enterprises of each size and for application areas that range from entertainment to big data analytics. Managed Cassandra service providers are emerging to hide the complexity of the installation, fine tuning and operation of Cassandra virtual data centers (VDCs). This paper address the problem of energy efficient autoscaling of Cassandra VDC in managed Cassandra data centers. We propose three energy-aware autoscaling algorithms: Opt, LocalOpt and LocalOpt-H. The first provides the optimal scaling decision orchestrating horizontal and vertical scaling and optimal placement. The other two are heuristics and provide sub-optimal solutions. Both orchestrate horizontal scaling and optimal placement. LocalOpt consider also vertical scaling. In this paper: we provide an analysis of the computational complexity of the optimal and of the heuristic auto-scaling algorithms; we discuss the issues in auto-scaling Cassandra VDC and we provide best practice for using auto-scaling algorithms; we evaluate the performance of the proposed algorithms under programmed SLA variation, surge of throughput (unexpected) and failures of physical nodes. We also compare the performance of energyaware auto-scaling algorithms with the performance of two energy-blind auto-scaling algorithms, namely BestFit and BestFit-H. at reducing the energy consumption or resource usage in general can heavily reduce the reliability of Cassandra in term of the consistency level offered. Horizontal scaling of Cassandra is very slow and make hard to manage surge of throughput. Vertical scaling is a valid alternative, but it is not supported by all the cloud infrastructures.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Energy-aware auto-scaling algorithms for Cassandra virtual data centers

2017

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“…In the context of databases, it may be defined as the change in performance when new nodes are added, or hardware is improved [99]. NoSQL databases have been developed specifically to target scenarios where scalability is very important.…”

Section: Scalabilitymentioning

confidence: 99%

Choosing the right NoSQL database for the job: a quality attribute evaluation

Lourenço

Cabral

Carreiro

et al. 2015

Journal of Big Data

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For over forty years, relational databases have been the leading model for data storage, retrieval and management. However, due to increasing needs for scalability and performance, alternative systems have emerged, namely NoSQL technology. The rising interest in NoSQL technology, as well as the growth in the number of use case scenarios, over the last few years resulted in an increasing number of evaluations and comparisons among competing NoSQL technologies. While most research work mostly focuses on performance evaluation using standard benchmarks, it is important to notice that the architecture of real world systems is not only driven by performance requirements, but has to comprehensively include many other quality attribute requirements. Software quality attributes form the basis from which software engineers and architects develop software and make design decisions. Yet, there has been no quality attribute focused survey or classification of NoSQL databases where databases are compared with regards to their suitability for quality attributes common on the design of enterprise systems. To fill this gap, and aid software engineers and architects, in this article, we survey and create a concise and up-to-date comparison of NoSQL engines, identifying their most beneficial use case scenarios from the software engineer point of view and the quality attributes that each of them is most suited to.

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Optimizing data partition for scaling out NoSQL cluster

Huang

Wang

Zhong

et al. 2015

Concurrency and Computation

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Data partition impacts the performance of Not Only SQL (NoSQL) systems significantly. Nowadays, many of the peer-to-peer NoSQL systems use consistent hashing to partition data automatically. These systems use virtual nodes and random data placement methods to divide the consistent hashing ring, which may lead to imbalanced data partition and degrade the overall system performance. The problem is prominent especially for scaling out heterogeneous clusters. Considering the capacity of each node, an imbalance coefficient of data distribution for a cluster is proposed firstly in this paper. Based on the imbalance coefficient, we propose a dynamic programming algorithm to calculate the position of the new coming node in the consistent hashing ring, which expands the consistent hashing ring more evenly without re-shuffling the entire datasets.Simulations and experiments on Cassandra with Yahoo! Cloud Serving Benchmark (YCSB) benchmark show our algorithm is better than the state-of-the-art work. . load balance, and (3) traffic load balance. The last two dimensions can be solved by replication mechanism. Our focus in this paper is the nodes distribution over the ring space.Virtual node is proposed in [10][11][12] to improve scalability and load balanced. Chen et al.[13] propose a hybrid data partitioning strategy for heterogeneous cluster load balance, and divide a node into many virtual nodes according to the node ability for the data storage and processing. However, although each node can store the amount of data, which is proportional to its capacity

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Benchmarking scalability and elasticity of distributed database systems

Cited by 90 publications

References 11 publications

Energy-aware auto-scaling algorithms for Cassandra virtual data centers

Energy-aware auto-scaling algorithms for Cassandra virtual data centers

Choosing the right NoSQL database for the job: a quality attribute evaluation

Optimizing data partition for scaling out NoSQL cluster

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