Automated Storage Tiering Using Markov Chain Correlation Based Clustering

Alshawabkeh, Malak; Riska, Alma; Şahin, Adnan; Awwad, Motasem

doi:10.1109/icmla.2012.71

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…A good coverage of Markov chain model-based clustering is considered in his PhD thesis by van Dongen (2001). Some early work on Markov chain-based clustering is due to Panuccio et al (2002) and van Dongen (2008), whereas some current work on the same technique is due to Alshawabkeh et al (2012) and Holzmann and Schwaiger (2015).…”

Section: Literature Reviewmentioning

confidence: 98%

A fuzzy Markovian model for graph-based clustering

Roy

Mandal

2015

IJCSYSE

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Graph works well as a random walk model which is a key concept in clustering. The Markov chain model mainly simulates flow in a graph. This paper proposes a random walk-based Markov chain clustering model on bipartite graph. A randomly generated point-based bipartite graph from the spatial data points has been created for this purpose. Proximity among data points has been computed based on the novel fuzzy correlation method and dense subgraphs are formed for random walk to work. Standard Markov flow-based clustering has been applied on the final similarity matrix. The model has been tested with standard datasets and the result has been compared with that of other benchmark models.

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Section: Literature Reviewmentioning

confidence: 98%

A fuzzy Markovian model for graph-based clustering

Roy

Mandal

2015

IJCSYSE

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“…Their prophet forecasting model is able to elegantly capture short and long term seasonality as a curve-fitting exercise, all without overfitting training data. Alshawabkeh [12], et. al., uses Markov Chain Correlation of the spacial and temporal characteristics of storage blocks to intelligently place blocks on a tiered storage systems.…”

Section: Related Workmentioning

confidence: 99%

A Smart Background Scheduler for Storage Systems

Kachmar¹,

Kaeli²

2020

Preprint

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In today's enterprise storage systems, supported data services such as snapshot delete or drive rebuild can cause tremendous performance interference if executed inline along with heavy foreground IO, often leading to missing SLOs (Service Level Objectives). Typical storage system applications such as web or VDI (Virtual Desktop Infrastructure) follow a repetitive high/low workload pattern that can be learned and forecasted. We propose a priority-based background scheduler that learns this repetitive pattern and allows storage systems to maintain peak performance and in turn meet service level objectives (SLOs) while supporting a number of data services. When foreground IO demand intensifies, system resources are dedicated to service foreground IO requests and any background processing that can be deferred are recorded to be processed in future idle cycles as long as forecast shows that storage pool has remaining capacity. The smart background scheduler adopts a resource partitioning model that allows both foreground and background IO to execute together as long as foreground IOs are not impacted where the scheduler harness any free cycle to clear background debt. Using traces from VDI application, we show how our technique surpasses a method that statically limit the deferred background debt and improve SLO violations from 54.6% when using a fixed background debt watermark to merely a 6.2% if dynamically set by our smart background scheduler.

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“…Nanyang Technological University Singapore viewing from file-level to classify the data in the storage. In [133], a framework is proposed to detect and predict the properties changing in the workload to make the migration process automatically. The detection and prediction are done through a clustering method utilizing a Markov chain correlation algorithm.…”

Section: Frequency/recency With Other Factorsmentioning

confidence: 99%

Hybrid system analysis and design for scale-out storage environments

Niu¹

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First of all, I would like to express my great appreciation to my supervisor, Prof. Lihua Xie, for his continuous guidance, encouragement and support throughout my research efforts towards this dissertation. Without his inspiration, deep insight, and invaluable advice, this thesis would not have been possible. I have benefited tremendously from his extensive knowledge, professional experience, and strong commitment towards the excellence of research.

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Automated Storage Tiering Using Markov Chain Correlation Based Clustering

Cited by 8 publications

References 3 publications

A fuzzy Markovian model for graph-based clustering

A fuzzy Markovian model for graph-based clustering

A Smart Background Scheduler for Storage Systems

Hybrid system analysis and design for scale-out storage environments

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