Download E-books Stochastic Models for Fault Tolerance: Restart, Rejuvenation and Checkpointing PDF

By Katinka Wolter

As glossy society depends upon the fault-free operation of advanced computing platforms, approach fault-tolerance has turn into an vital requirement. consequently, we want mechanisms that warrantly right carrier in instances the place procedure elements fail, be they software program or parts. Redundancy styles are regularly occurring, for both redundancy in house or redundancy in time.

Wolter’s e-book information equipment of redundancy in time that have to be issued on the correct second. specifically, she addresses the so-called "timeout choice problem", i.e., the query of selecting the ideal time for various fault-tolerance mechanisms like restart, rejuvenation and checkpointing. Restart exhibits the natural method restart, rejuvenation denotes the restart of the working atmosphere of a role, and checkpointing contains saving the process country periodically and reinitializing the process on the most up-to-date checkpoint upon failure of the method. Her presentation encompasses a short creation to the tools, their unique stochastic description, and likewise facets in their effective implementation in real-world systems.

The e-book is concentrated at researchers and graduate scholars in procedure dependability, stochastic modeling and software program reliability. Readers will locate right here an updated evaluate of the foremost theoretical effects, making this the single finished textual content on stochastic types for restart-related problems.

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Read or Download Stochastic Models for Fault Tolerance: Restart, Rejuvenation and Checkpointing PDF

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Extra info for Stochastic Models for Fault Tolerance: Restart, Rejuvenation and Checkpointing

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169 eight Checkpointing platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 eight. 1 Checkpointing Single-Unit structures . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 eight. 2 Checkpointing in disbursed platforms . . . . . . . . . . . . . . . . . . . . . . . . . 174 nine Stochastic versions for Checkpointing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 nine. 1 Checkpointing at application point . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 nine. 1. 1 Equidistant Checkpointing . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 nine. 1. 2 Checkpointing Real-Time projects . . . . . . . . . . . . . . . . . . . . . . 189 nine. 1. three Random Checkpointing durations . . . . . . . . . . . . . . . . . . . . . . 194 nine. 1. four Algorithms for max Checkpoint choice . . . . . . . . . . 197 nine. 2 Checkpointing at approach point . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 nine. 2. 1 Analytic types for Checkpointing Transaction-Based platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208 nine. 2. 2 Checkpointing rules for Transaction-Based platforms . . . 214 nine. 2. three A Queueing version for Checkpointing Transaction-Based structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224 nine. three A Trade-Off Metric for optimum Checkpoint choice . . . . . . . . . . . 232 nine. four precis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 10 precis, end and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 A houses in Discrete platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 A. 1 Cumulative First second . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 A. 2 The Gamma functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 xvi Contents B very important chance Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 B. 1 Discrete likelihood Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 B. 1. 1 The Binomial Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 B. 1. 2 The Multinomial Distribution . . . . . . . . . . . . . . . . . . . . . . . . . 243 B. 1. three The Geometric Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . 244 B. 1. four The Poisson Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244 B. 2 non-stop likelihood Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . 244 B. 2. 1 The Exponential Distribution . . . . . . . . . . . . . . . . . . . . . . . . . 244 B. 2. 2 The Erlang Distribution and the Hypo-exponential Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 B. 2. three The Hyperexponential Distribution . . . . . . . . . . . . . . . . . . . . 247 B. 2. four The combined Hyper/Hypo-exponential Distribution . . . . . . . . 247 B. 2. five The Weibull Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248 B. 2. 6 The Lognormal Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . 249 C Estimating the possibility cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 C. 1 Cumulative possibility price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 C. 2 Epanechnikov Kernel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 C. three Bandwidth Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252 D The Laplace and the Laplace-Stieltjes remodel . . . . . . . . . . . . . . . . . . 253 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 word list . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 Chapter 1 simple options and difficulties This publication adresses difficulties and questions in desktop fault-tolerance that may be tackled utilizing stochastic types.

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