iSWoM: The Incremental Storage Workload Model Based on Hidden Markov Models

Chis, Tiberiu S.; Harrison, Peter G.

doi:10.1007/978-3-642-39408-9_10

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Discretized MMPPs (or hidden Markov models) replicate the burstiness of TCP packet traces, which can be clustered in groups, and, hence, allow model parameters to converge on multiple traces simultaneously at reduced computational complexity [10]. Further, arrival parameters of queueing models can be updated incrementally via online EM learning algorithms [2,6,14], which are suitable for live systems.…”

Section: Resultsmentioning

confidence: 99%

Higher response time moments for M/M/1 discriminatory processor sharing queues

Chis¹,

Harrison²

2016

Proceedings of the 9th EAI International Conference on Performance Evaluation Methodologies and Tools

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Obtaining response time moments in processor sharing (PS) queues is difficult due to serving of multiple jobs. Egaliatarian PS (EPS) queues are limited to one class of arriving jobs. Discriminatory PS (DPS) assigns weights to different job classes and offers more diverse modeling capabilities than EPS. It is known that response time is the representative metric for delay as specified in service level agreements (SLAs), which consider higher moments important. Hence, we build an automated numerical algorithm for calculating higher moments of response time in M/M/1-DPS queues for multiple job classes and test two different case studies.

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Section: Resultsmentioning

confidence: 99%

Higher response time moments for M/M/1 discriminatory processor sharing queues

Chis¹,

Harrison²

2016

Proceedings of the 9th EAI International Conference on Performance Evaluation Methodologies and Tools

Self Cite

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Sliding Hidden Markov Model for Evaluating Discrete Data

Chis

2013

Computer Performance Engineering

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Advanced ECHMM-Based Machine Learning Tools for Complex Big Data Applications

Cuzzocrea

Mumolo

Vercelli

2017

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)

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We present a novel approach for accurate characterization of workloads. Workloads are generally described with statistical models and are based on the analysis of resource requests measurements of a running program. In this paper we propose to consider the sequence of virtual memory references generated from a program during its execution as a temporal series, and to use spectral analysis principles to process the sequence. However, the sequence is time-varying, so we employed processing approaches based on Ergodic Continuous Hidden Markov Models (ECHMMs) which extend conventional stationary spectral analysis approaches to the analysis of time-varying sequences. In this work, we describe two applications of the proposed approach: the on-line classification of a running process and the generation of synthetic traces of a given workload. The first step was to show that ECHMMs accurately describe virtual memory sequences; to this goal a different ECHMM was trained for each sequence and the related run-time average process classification accuracy, evaluated using trace driven simulations over a wide range of traces of SPEC2000, was about 82%. Then, a single ECHMM was trained using all the sequences obtained from a given running application; again, the classification accuracy has been evaluated using the same traces and it resulted about 76%. As regards the synthetic trace generation, a single ECHMM characterizing a given application has been used as a stochastic generator to produce benchmarks for spanning a large application space.

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iSWoM: The Incremental Storage Workload Model Based on Hidden Markov Models

Cited by 3 publications

References 10 publications

Higher response time moments for M/M/1 discriminatory processor sharing queues

Higher response time moments for M/M/1 discriminatory processor sharing queues

Sliding Hidden Markov Model for Evaluating Discrete Data

Advanced ECHMM-Based Machine Learning Tools for Complex Big Data Applications

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