PAX: Partition-aware autoscaling for the Cassandra NoSQL database

Dipietro, Salvatore; Buyya, Rajkumar; Casale, Giuliano

doi:10.1109/noms.2018.8406271

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…Thus, they improve the TPS in a step by step basis. For a quantitative evaluation, we have used the following metrics: total under-provisioned time (T u ) [36], [37], total under-provisioned area (A u ) [36], [37] and TPS. The metrics indicate how an autoscaler performs when there is a increase in the workload.…”

Section: B Resultsmentioning

confidence: 99%

ATOM: Model-Driven Autoscaling for Microservices

Gias

Casale

Woodside

2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

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Microservices based architectures are increasingly widespread in the cloud software industry. Still, there is a shortage of auto-scaling methods designed to leverage the unique features of these architectures, such as the ability to independently scale a subset of microservices, as well as the ease of monitoring their state and reciprocal calls. We propose to address this shortage with ATOM, a modeldriven autoscaling controller for microservices. ATOM instantiates and solves at run-time a layered queueing network model of the application. Computational optimization is used to dynamically control the number of replicas for each microservice and its associated container CPU share, overall achieving a fine-grained control of the application capacity at run-time. Experimental results indicate that for heavy workloads ATOM offers around 30%-37% higher throughput than baseline modelagnostic controllers based on simple static rules. We also find that model-driven reasoning reduces the number of actions needed to scale the system as it reduces the number of bottleneck shifts that we observe with model-agnostic controllers.

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Section: B Resultsmentioning

confidence: 99%

ATOM: Model-Driven Autoscaling for Microservices

Gias

Casale

Woodside

2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

Self Cite

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“…A final methodology for performance evaluation is to rely on mathematical models (e.g., based on queuing theory) [11] or on simulation [3] to evaluate system performance. While this approach have some merit, especially when different architectures are to be compared, mathematical and simulator models need to be tuned with a characterization of the real system in order to produce accurate results.…”

Section: Related Workmentioning

confidence: 99%

Performance Comparison of Technological Solutions for Spark Applications in AWS

Lancellotti

Rossi

Miano³

et al. 2022

2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

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“…The MapReduce strategy is applied to the k-means clustering algorithm and clustered for the data factors. The k-means [ 46 ] algorithm can be successfully parallelized and clustered on hardware resources. MapReduce can be utilized for k-means clustering.…”

Section: Proposed Cqns Frameworkmentioning

confidence: 99%

An adaptive spark-based framework for querying large-scale NoSQL and relational databases

Khashan

El-Desouky

Elghamrawy³

2021

PLoS ONE

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The growing popularity of big data analysis and cloud computing has created new big data management standards. Sometimes, programmers may interact with a number of heterogeneous data stores depending on the information they are responsible for: SQL and NoSQL data stores. Interacting with heterogeneous data models via numerous APIs and query languages imposes challenging tasks on multi-data processing developers. Indeed, complex queries concerning homogenous data structures cannot currently be performed in a declarative manner when found in single data storage applications and therefore require additional development efforts. Many models were presented in order to address complex queries Via multistore applications. Some of these models implemented a complex unified and fast model, while others’ efficiency is not good enough to solve this type of complex database queries. This paper provides an automated, fast and easy unified architecture to solve simple and complex SQL and NoSQL queries over heterogeneous data stores (CQNS). This proposed framework can be used in cloud environments or for any big data application to automatically help developers to manage basic and complicated database queries. CQNS consists of three layers: matching selector layer, processing layer, and query execution layer. The matching selector layer is the heart of this architecture in which five of the user queries are examined if they are matched with another five queries stored in a single engine stored in the architecture library. This is achieved through a proposed algorithm that directs the query to the right SQL or NoSQL database engine. Furthermore, CQNS deal with many NoSQL Databases like MongoDB, Cassandra, Riak, CouchDB, and NOE4J databases. This paper presents a spark framework that can handle both SQL and NoSQL Databases. Four scenarios’ benchmarks datasets are used to evaluate the proposed CQNS for querying different NoSQL Databases in terms of optimization process performance and query execution time. The results show that, the CQNS achieves best latency and throughput in less time among the compared systems.

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PAX: Partition-aware autoscaling for the Cassandra NoSQL database

Cited by 5 publications

References 26 publications

ATOM: Model-Driven Autoscaling for Microservices

ATOM: Model-Driven Autoscaling for Microservices

Performance Comparison of Technological Solutions for Spark Applications in AWS

An adaptive spark-based framework for querying large-scale NoSQL and relational databases

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