Managing Service Performance in the Cassandra Distributed Storage System

Chalkiadaki, Maria; Magoutis, Kostas

doi:10.1109/cloudcom.2013.16

Cited by 14 publications

(11 citation statements)

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“…Each shard may be replicated for high availability and failure recovery [16]. Chalkiadaki [17] supports a primary-backup scheme using a shadow QoS controller to replicate the state of the primary for high availability in Cassandra [18]. In Gorilla [19], even if a network partition or other failure leads to disconnection between different data centres, systems operating within any given data centre ought to be able to write data to local TSDB (a time series database) machines and be able to retrieve this data on demand.…”

Section: Availability Availability In Databasementioning

confidence: 99%

Availability Enhancement of Riak TS Using Resource‐Aware Mechanism

Liu

et al. 2019

Mathematical Problems in Engineering

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The dependability and elasticity of various NoSQL stores in critical application are still worth studying. Currently, the cluster and backup technologies are commonly used for improving NoSQL availability, but these approaches do not consider the availability reduction when NoSQL stores encounter performance bottlenecks. In order to enhance the availability of Riak TS effectively, a resource-aware mechanism is proposed. Firstly, the data table is sampled according to time, the correspondence between time and data is acquired, and the real-time resource consumption is recorded by Prometheus. Based on the sampling results, the polynomial curve fitting algorithm is used to constructing prediction curve. Then the resources required for the upcoming operation are predicted by the time interval in the SQL statement, and the operation is evaluated by comparing with the remaining resources. Using the real hydrological sensor dataset as experimental data, the effectiveness of the mechanism is experimented in two aspects of sensitivity and specificity, respectively. The results show that through the availability enhancement mechanism, the average specificity is 80.55% and the sensitivity is 76.31% which use the initial sampling dataset. As training datasets increase, the specificity increases from 80.55% to 92.42%, and the sensitivity increases from 76.31% to 87.90%. Besides, the availability increases from 40.33% to 89.15% in hydrological application scenarios. Experimental results show that this resource-aware mechanism can effectively prevent potential availability problems and enhance the availability of Riak TS. Moreover, as the number of users and the size of the data collected grow, our method will become more accurate and perfect.

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Section: Availability Availability In Databasementioning

confidence: 99%

Availability Enhancement of Riak TS Using Resource‐Aware Mechanism

Liu

et al. 2019

Mathematical Problems in Engineering

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“…Prior work on Cassandra autoscaling includes in particular [3], [4] and [5]. Both [3] and [4] present a reactive autoscaling mechanism.…”

Section: Related Workmentioning

confidence: 99%

“…Prior work on Cassandra autoscaling includes in particular [3], [4] and [5]. Both [3] and [4] present a reactive autoscaling mechanism. [3] develops a Cassandra controller that gathers node and workload performance data to calculate an exponentially-weighted moving average (EWMA) of the response time currently experienced by the users.…”

Section: Related Workmentioning

confidence: 99%

“…Both [3] and [4] present a reactive autoscaling mechanism. [3] develops a Cassandra controller that gathers node and workload performance data to calculate an exponentially-weighted moving average (EWMA) of the response time currently experienced by the users. When this moving average exceeds a pre-defined threshold, the controller adds a new node to the system.…”

Section: Related Workmentioning

confidence: 99%

“…In particular, the architecture of Cassandra lacks a single point of failure, making it well-suited for operation in unreliable environments such as the cloud. In spite of this, only recently researchers have started to systematically investigate autoscaling methods for Cassandra [3], [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

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PAX: Partition-aware autoscaling for the Cassandra NoSQL database

Dipietro

Buyya

Casale

2018

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

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Apache Cassandra has emerged as one of the most widely adopted NoSQL databases. However, there is still a limited understanding on how to optimally operate Cassandra in the cloud using autoscaling methods, by which resources can be scaled up or down to reduce operational costs and meet servicelevel objectives (SLOs). To address this limitation, we present PAX, a partition-aware elastic resource management system for Apache Cassandra. PAX uses low-overhead query sampling and knowledge of the datapartitioning across the nodes to automatically adapt capacity in Cassandra clusters. Differently from existing autoscaling methods for Cassandra, which incur large acquisition times for new nodes, PAX exploits Cassandra's hinted handoff mechanism and a shared hints storage to minimize the time needed to acquire a node into the cluster. We propose a reactive and a proactive implementation of PAX and compare their performance against different workloads with varying intensities and item popularity distributions, finding that the proactive version significantly reduces SLO violations.

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