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Designing domain-driven device clouds using Monte Carlo methods

Posted: 10 Nov 2011 ?? ?Print Version ?Bookmark and Share

Keywords:domain-driven design? Monte Carlo method? cloud computing?

By thinking about these concepts, the idea is that we can more easily sort device-cloud design problems and reap the added benefit of keeping designers, developers, and domain experts focused on the problems that exist within the target domain.

Monte Carlo methods
What types of features are possible with device clouds that were not previously conceivable?

With device clouds, in addition to centralized data storage, we open the solution space to include the ability to remotely control equipment, monitor data and trends, enable rich diagnostics and support, provide in-field configuration and software update capabilities, track usability statistics, and give resource-constrained embedded devices access to powerful cloud processing power.

In this report
??Device clouds
??Domain-driven design
??Monte Carlo methods
??Data visualization

With all these added benefits, we now have the ability to collect very large quantities of data about a fleet of devices. Having detailed data about many devices enables two types of analysis:
???Detailed analysis and diagnostics on individual devices. This is great for enabling customer support, software upgrades, and diagnosing problems in the field without sending repair technicians.
???Statistical analysis on many devices. This type of analysis is especially interesting to the author since it uncovers new ways to optimize user experiences, reduce cost, or mitigate failures.

Since we're particularly interested in statistical analysis on data from a number of devices, we turn our attention to one such method called Monte Carlo methods.

Monte Carlo methods were contrived in the 1940s by physicists working on nuclear weapons projects at the Los Alamos National Laboratory. Specifically, MCM were used while investigating radiation shielding to predict the distance that neutrons would travel through various types of materials.

MCM are a class of computational algorithms based on stochastic techniques that are used to calculate the result of complex processes using random variables. You can find MCM used in everything from economic policy, to nuclear physics, to regulating the flow of traffic in metropolitan areas.

Strictly speaking, to call something a Monte Carlo experiment, all you need to do is use random numbers to examine a problem. Here are the key steps to solving a problem using MCM:
???Define a domain of possible inputs.
???Generate inputs randomly from the domain using a probability distribution (such as Gaussian).
???Perform a deterministic computation using the inputs.
???Aggregate the results of the individual computations into the final result.

To illustrate this, here is a quick example of how we can estimate the value of ? using MCM:

    1. Draw a square on the ground, then inscribe a circle within it. From plane geometry, we know that the ratio of the area of an inscribed circle to that of the surrounding square is /4.

    2. Uniformly scatter objects of uniform size throughout the square. For example, you might use grains of rice or sand.

    3. Since the two areas are in the ratio /4, the objects should fall in the areas in approximately the same ratio (uniform distribution).

    4. Count the number of objects in the circle and divide by the total number of objects in the square. By doing this, we will yield an approximation for /4.

    5. Multiply the result by 4 to get an approximation for itself.

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