Managing service performance in NoSQL distributed storage systems

Chalkiadaki, Maria; Magoutis, Kostas

doi:10.1145/2405178.2405183

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…These big data applications are focused towards improving decision making and operational efficiency of organizations. Applications associated with this domain include data management [218,96,280], problemspecific applications for areas like financial services [232] and service performance management [233], and general applications [69,286,150,167,181] for improving the operational efficiency of the system. It is important to mention that applications related to industries and company-specific applications like logs analysis for IT companies are not included in business intelligence.…”

Section: Business Intelligencementioning

confidence: 99%

Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications

Khan¹,

Liu²,

Ali³

et al. 2023

Advances in Computers

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Section: Business Intelligencementioning

confidence: 99%

Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications

Khan¹,

Liu²,

Ali³

et al. 2023

Advances in Computers

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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%

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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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“…QoS-aware infrastructure for monitoring and controlling service performance with NoSQL-distributed storage systems has been developed for data-intensive applications [15]. Also, a service-oriented monitoring framework with REST and Nagios has been developed [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Mechanism and architecture for the migration of service implementation during traffic peaks

Pääkkönen

Pakkala

2014

SOCA

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Service-Oriented Architecture has been widely applied in enterprise computing systems for software-enabled services. However, cost efficiency and scalability requirements have moved the execution environment towards the cloud domain. Hybrid approaches have emerged, which utilise both enterprise and cloud domains in order to balance between the cost of service execution and the provided Quality of Service (QoS) for end users. This paper presents a migration, monitoring and load-balancing mechanism and architecture for scaling services between the enterprise and cloud domains during traffic peaks. The argued benefit of the proposal is the automation of the service-migration process and improvement of the QoS. A prototype system is presented as a proof of the conceptual architecture. The performance results in a hybrid cloud environment indicate that service implementation can be migrated and load can be balanced within 200 ms. Furthermore, the mechanism can improve the QoS for end users during traffic peaks. Our approach differs from existing proposals by focusing on the migration of service implementation, instead of the migration of service as part of a virtual machine.

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Managing service performance in NoSQL distributed storage systems

Cited by 8 publications

References 8 publications

Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications

Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications

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

Mechanism and architecture for the migration of service implementation during traffic peaks

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