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CLOUD computing

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Abstract CLOUD computing (Grid or utility computing, computing on‐demand) which was the talk of the computing circles at the end of 1990s has become once again a relevant computational topic. CLOUD computing, also considered as a fifth utility after water, electric power, gas, and telephony, is on the basis of the hosting of services on clusters of computers housed in server farms. This article reviews CLOUD computing fundamentals in general, its operational modeling and quantitative (statistical) risk assessment of its much neglected service quality issues. As an example of a CLOUD, a set of distributed parallel computers is considered to be working independently or dependently, but additively to serve the cumulative needs of a large number of customers requiring service. Quantitative methods of statistical inference on the quality of service (QoS) or conversely, loss of service (LoS), as commonly used customer satisfaction metrics of system reliability and security performance are reviewed. The goal of those methods is to optimize what must be planned about how to improve the quality of a CLOUD operation and what countermeasures to take. Also, a discrete event simulation is reviewed to estimate the risk indices in a large CLOUD computing environment favorably compared to the intractable and lengthy theoretical Markov solutions. WIREs Comp Stat 2011 3 47–68 DOI: 10.1002/wics.139 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Reliability, Survivability, and Quality Control Statistical Models > Simulation Models Software for Computational Statistics > Software/Statistical Software

Schematic representation of CLOUD computing.

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Large‐scale cyber CLOUD example Markov Solution for 398 Units using Alabama Supercomputer for large‐scale example (3).

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Simulation results for a large cyber CLOUD with 398 components in large‐scale example (3).

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(a) Large cyber CLOUD data file for Markov state solution. (b) Large cyber CLOUD schema. (c) Digital event simulation results for large cyber CLOUD in large‐scale example (2).

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Group 1's Component 1 up‐down fluctuations in a year (8760 cycles) of operations.

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Reserve Margin = Installed Capacity − Outages − Service Demand (load). Below zero implies Loss of Service (LoSE). Above zero (red) implies surplus service.

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Hourly available (blue colored available = Installed 26,237 GB − Outage Capacity due to hourly failures) and load (red colored customer demand) cycles over a year.

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The print out shows P(LoSE > 1202 h) = 0.4883 (slight right‐skewed) as in Figure 13.

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The frequency distribution of the LoSE (M = 1202, q = 7.16) from Figures 11 and 13.

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Simulation results for a large cyber CLOUD with 443 components for large‐scale example (1).

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An example of hourly load (values) demand from 1st to 8760th hour in a calendar year.

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Markov states, small cyber CLOUD, markov state space equations, DES solutions.

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Eqs. (17)–(19) plotted versus time for units 1, 2 and their sum with 1.5 GB = constant demand.

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For Component 2, up (green) and down (red) with wait times (yellow) from Figure 5.

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For Component 1, up (green) and down (red) and no wait times (yellow) from Figure 3.

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Small cyber CLOUD same as Figure 3 with only one repair crew instead of two crews.

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Simulated outage history for a simple two component independent‐additive system.

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Simulation results for a simple two component independent‐additive system.

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Qualitative or descriptive security metrics.

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Browse by Topic

Software for Computational Statistics > Software/Statistical Software
Statistical and Graphical Methods of Data Analysis > Reliability, Survivability, and Quality Control
Statistical Models > Simulation Models

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