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Availability Quantification for Iaas Cloud

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Availability Quantification for Iaas Cloud
Performance, Availability and Power Analysis for IaaS Cloud
Kishor Trivedi kst@ee.duke.edu www.ee.duke.edu/~kst Dept. of ECE, Duke University, Durham, NC 27708

Universita Napoli
September 23, 2011
1

Duke University
Research Triangle Park (RTP)

2

Duke UNC-CH NC state

USA

North Carolina

2

Trivedi’s Research Triangle
Theory
Stochastic modeling methods & numerical solution methods: Large Fault trees, Stochastic Petri Nets, Large/stiff Markov & non-Markov models Fluid stochastic models Performability & Markov reward models Software aging and rejuvenation Attack countermeasure trees

Software Packages

Books:
Blue, Red, White

Applications
Reliability/availability/performance Avionics, Space, Power systems, Transportation systems, Automobile systems Computer systems (hardware/software) Telco systems Computer Networks Virtualized Data center Cloud computing

HARP (NASA), SAVE (IBM), IRAP (Boeing) SHARPE, SPNP, SREPT

3

Talk outline
Overview of Reliability and Availability Quantification Overview of Cloud Computing Performance Quantification for IaaS Cloud (PRDC 2010) Availability Quantification for IaaS Cloud (DSN 2011) Power Quantification for IaaS Cloud (DSN workshop 2011) Future Research

Copyright © 2011 by K.S. Trivedi 4

An Overview of Reliability and Availability Quantification Methods
Software + hardware in operation Dynamic as opposed to static behavior

Copyright © 2011 by K.S. Trivedi 5

Reliability and Availability Quantification
Measurement-Based More Accurate Expensive due to configurations Model-Based Combined approach where measurements are made at the subsystem level and models are built to derive system-level measures
Copyright © 2011 by K.S. Trivedi 6

many parameters and

Not always possible during system design.

Reliability and Availability Evaluation Methods
Measurement-based

Quantitative Evaluation

Discrete-event simulation Model-based Hybrid Analytic Models

Numerical
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