Availability Quantification for Iaas Cloud

Topics: Cloud computing, Grid computing, Utility computing Pages: 29 (2900 words) Published: November 24, 2011
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

Duke University
Research Triangle Park (RTP)


Duke UNC-CH NC state


North Carolina


Trivedi’s Research Triangle
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

Blue, Red, White

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



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

Quantitative Evaluation

Discrete-event simulation Model-based Hybrid Analytic Models

Numerical solution of analytic models not as well utilized; Unnecessarily excessive use of simulation

Closed-form solution

Numerical solution via a tool

Copyright © 2011 by K.S. Trivedi 7

Analytic Modeling Taxonomy
Measurement-based Quantitative Dependability Evaluation Model-based

Discrete-event simulation Hybrid Analytic Models

Non-state-space models Analytic models State-space models Hierarchical composition Fixed point iterative models Copyright © 2011 by K.S. Trivedi 8

Non-state space models
Modeling using reliability block diagrams (RBDs), reliability graphs (relgraphs) and fault trees (FTs) are easy to use and efficient to solve for system reliability, system availability and system mean time to failure (MTTF)

Product-form queuing networks for performance analysis

Example: Reliability Analysis of Boeing 787
Current Return Network Modeled as a Reliability Graph (Relgraph)


Reliability Analysis of Boeing 787 (cont’d)
This real problem has too many minpaths
Non-state space models also face largeness problem
Number of paths from source to target


Reliability Analysis of Boeing 787 (cont’d)
Our Approach : Developed a new efficient algorithm for (un)reliability bounds computation developed and incorporated in SHARPE

SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) 12

Non-State-Space Models

Failure/Repair Dependencies are often present; RBDs, relgraphs, FTREEs cannot easily handle these (e.g., shared repair, warm/cold spares, imperfect coverage, non-zero switching time, travel time of repair person, reliability with repair). Product-form does not often hold when modeling real-life aspects such as simultaneous resource possession, priorities, retries, etc.


State-space models : Markov chains
To model complex interactions between components, use models such as Markov chains or more generally state space models. Many examples of dependencies among system components have...
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