Suitability of NoSQL systems — Cassandra and ScyllaDB — For IoT workloads

Mahgoub, Ashraf; Ganesh, Sachandhan; Meyer, Folker; Grama, Ananth; Chaterji, Somali

doi:10.1109/comsnets.2017.7945437

Cited by 10 publications

(16 citation statements)

References 2 publications

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“…We use Cassandra for two main reasons. First, Cassandra is one of the most used and best performing NoSQL databases today, with applications in several different domains ( Duarte & Bernardino, 2016 ; Daz, Martn & Rubio, 2016 ; Mahgoub et al, 2017a ; Le et al, 2014 ; Aniceto et al, 2015 ; Pinheiro et al, 2017 ). Second, the existing documentation is very complete, and it allows to easily replicate and generalize the experiments carried out in this work.…”

Section: Methodsmentioning

confidence: 99%

“…Our approach is completely general, and can be applied to different relational and NoSQL databases with little effort. In this work we choose to study the performance of irace on the Cassandra database, one of the most popular NoSQL databases, used in several real-world applications such as Internet of Things, genomics, or electric consumption data ( Cassandra, 2014 ; Duarte & Bernardino, 2016 ; Daz, Martn & Rubio, 2016 ; Mahgoub et al, 2017a ; Le et al, 2014 ; Aniceto et al, 2015 ; Pinheiro et al, 2017 ). We measure the performance in terms of throughput using the YCSB benchmark ( Cooper et al, 2010 ; Wang & Tang, 2012 ), observing a speedup of up to 30% over the default configuration.…”

Section: Introductionmentioning

confidence: 99%

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Automatic configuration of the Cassandra database using irace

Silva-Muñoz¹,

Franzin²,

Bersini³

2021

PeerJ Computer Science

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Database systems play a central role in modern data-centered applications. Their performance is thus a key factor in the efficiency of data processing pipelines. Modern database systems expose several parameters that users and database administrators can configure to tailor the database settings to the specific application considered. While this task has traditionally been performed manually, in the last years several methods have been proposed to automatically find the best parameter configuration for a database. Many of these methods, however, use statistical models that require high amounts of data and fail to represent all the factors that impact the performance of a database, or implement complex algorithmic solutions. In this work we study the potential of a simple model-free general-purpose configuration tool to automatically find the best parameter configuration of a database. We use the irace configurator to automatically find the best parameter configuration for the Cassandra NoSQL database using the YCBS benchmark under different scenarios. We establish a reliable experimental setup and obtain speedups of up to 30% over the default configuration in terms of throughput, and we provide an analysis of the configurations obtained.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic configuration of the Cassandra database using irace

Silva-Muñoz¹,

Franzin²,

Bersini³

2021

PeerJ Computer Science

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“…‗DMFP' represents execution results when 6 features (Document-oriented, Graph, Key-Value, Wide-Column, Free and Proprietary) were considered for clustering. [67] AllegroGraph [65] Amazon Neptune [68] ArangoDB [70] Accumulo [66] BerkeleyDB [73] Cassandra [77] Cache [76] AnzoGraph [69] BaseX [72] Clusterpoint Database [80] BigTable [75] CDB or Constant Database [78] Cloudant [79] Azure Tables [71] CouchDB [83] CouchBase Server [82] GridGain Systems [93] etcd [90] Coherence [81] DataStax Enterprise Graph [86] CrateIO [84] HBase [4] NoSQLz [117] FoundationDB [92] CosmosDB [85] Dynamo [88] ElasticSearch [89] HyperTable [98] OpenLink Virtuoso [119] GT.M [54] DocumentDB [87] Hazelcast [95] eXist [91] MongoDB [6] Hibari [96] IBM Informix [99] HyperGraphDB [97] Jackrabbit [104] RethinkDB [128] IBM Informix C-ISAM [100] Lotus Domino [110] InfiniteGraph [102] OrientDB…”

Section: Cluster Analysismentioning

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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“…However, there are limitations to this vertical scaling up to the point where the application itself becomes the bottleneck. With the "genomical" data sizes, often noSQL backends are deployed to scale with the increasing sizes and diversity of queries, such as in genomics [101] and IoT-related domains [102]. Further, we have to keep in mind guarantees of safety, availability, and timeliness, either deterministically or stochastically in a complex environment, with humans-inthe-loop.…”

Section: Planet-scale Iotmentioning

confidence: 99%

Resilient Cyberphysical Systems and their Application Drivers: A Technology Roadmap

Chaterji,

Naghizadeh,

Alam

et al. 2020

Preprint

Self Cite

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Cyberphysical systems (CPS) are ubiquitous in our personal and professional lives, and they promise to dramatically improve micro-communities (e.g., urban farms, hospitals), macro-communities (e.g., cities and metropolises), urban structures (e.g., smart homes and cars), and living structures (e.g., human bodies, synthetic genomes). The question that we address in this article pertains to designing these CPS systems to be resilient-from-the-ground-up, and in "learning" iterations, resilientby-reaction. An optimally designed system is resilient to both unique and recurrent attacks with a minimal overhead from "fitting". In the following, our focus is on design and deployment innovations that are broadly applicable across a range of application areas and drivers.We divide our paper into three broad themes. First, we present three prominent application drivers that can lay the basis for the existing and emerging technologies, as follows: smart cities and digital agriculture; planet-scale IoT ; and internet-of-medical-things (IoMT). We select the three application drivers as representative examples of different possible application scenarios that a CPS "designer" may face. These scenarios are orthogonal to each other, operate at different scales, and collectively cover key application domains. Concretely, while the scale of smart cities and digital agriculture on small to large-sized farms can be large, they are not nearly as large as planet-scale IoT, where the scale may pose unique challenges to start with, requiring the consideration of the scale from the outset. For example, a protocol that relies on lots of messages being passed among the participating nodes may work well from an energy efficiency and timeliness standpoint when all the nodes are nearby, say connected through Bluetooth Low Energy (BLE) links. However, when the system is planet-scale and the nodes are dispersed, potentially over large geographic regions, or in highly congested environments, such as urban road environments, such a protocol will become infeasible. In contrast, for IoMT, there is a unique set of challenges with some critical safety requirements as they relate to individuals in medical scenarios.Second, we lay out the foundational technologies-hardware and algorithms-which will power resilient CPS. We base this discussion on two complementary threads for imbuing resilience in these systems, namely, resilience-by-design and resilience-by-reaction. Overall, the notion of resilience can be thought of in the light of three main sources of lack of resilience, as follows: exogenous factors, such as natural variations and attack scenarios; mismatch between engineered designs and exogenous factors ranging from DDoS (distributed denial-of-service) attacks or other cybersecurity

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Suitability of NoSQL systems — Cassandra and ScyllaDB — For IoT workloads

Cited by 10 publications

References 2 publications

Automatic configuration of the Cassandra database using irace

Automatic configuration of the Cassandra database using irace

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

Resilient Cyberphysical Systems and their Application Drivers: A Technology Roadmap

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