# Scalable Computing

3
How to build or develop an optimal mathematical model for the scalability of the WSN.
Many Parameters affects the Network Scalability; Throughput, QoS, Network Size, Routing, etc.

I would recommend the phenomenal work by Gupta Kumar on their capacity paper,

"http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=825799&tag=1".

Although the work is for ad-hoc network but the mathematical treatment is so rigorous and deliver an insight about how to do it for other networks.

4
Best method for a wired network bus for scalable SoC clusters (Using Raspberry Pi - Compute Module)?

Taking the Raspberry Pi Compute Module as an example, an interesting use case is to build a scalable computing system that utilizes each module as a cluster.

However I am a little curious on the best wired networking model here. Should it be a master-slave design using I2C for example (I don't think its reliable or quick enough)? What are your thoughts here?

I think that the best result can be accomplished by designing and programming a task specific custom communication method (protocol if you will) directly via GPIO and not to rely on already existing communication protocols such as I2C or SPI, as they are designed for different purposes (mainly sensors and C&C - command and control of other devices), not for sharing large amounts of shared memory transfer, task control etc. which is needed when creating a cluster.

1
What is the meaning of "fullSamples" keyword in ABF calculation in NAMD?

While doing Adaptive Biasing Force calculations in NAMD we need to provide the keyword "fullSamples" the default value of which is 200. What is the significance of this keyword? How do I provide the keyword according to the needs of my system?

It is the number of phase space points needed to adequately estimate the average biasing force. Obviously the more points you can use the better, but due to limited time and computational resources one has to find "what is good enough number of points" This can be done by starting with the default number and then incrementally increase it  until a reasonable estimate of the average biasing force is obtained.

4
How do we define and quantify scalability, elasticity and efficiency?
These new quality attributes arise especially for Cloud Computing applications.
I think that elasticity is the property that distinguishes cloud computing from other computing paradigms. A metric for elasticity might be relevant for cloud users: how fast the system responds to your demand? This is an issue for major providers, since the response time must take into account the time needed to find the resource, allocate, configure and deliver it to the user. In conclusion, the time needed to allocate 100 CPUs may be higher or lower than one hundred times the time needed to allocate just one: this depends on many cloud management decisions. The user might appreciate a metric of this sort.

Anther issue related to this aspect is how the cloud reacts to a decrease in the demand: while it is clear that if you explicitely deallocate a node, the effective deallocation should occur sooner or later, it is not clear how cloud management responds if the the user subscribed an "adaptive" service. In both cases, a slow response has a financial impact.

To come to your question, elasticity should be related to the timeliness with which the cloud reacts to a changing demand: both positive and negative changes. A metric should take into account the time needed to effect the change: cpu/sec is the rough idea for computing resources. Financial and management issues (need to change the contract?) contribute to the figure.
12
What are the most common practices in providing high scale access to spatial data with region query support?
An increasing demand for geographic data imposes high query loads on data providers. Main players in the web (e.g. Facebook) use distributed memory caching systems such as Memcached to cache frequently accessed data in a key-value fashion. However, for spatial/geographic data as provided by OpenStreetMap for instance, the key-value paradigm is insufficient or at least unhandy, as many location-based services or applications require data which is located in certain ranges or regions.

How do service providers most commonly implement high scale access to spatial data with region query support?

(a) Is there any widely used distributed caching system similar to Memcached available, which supports region queries out of the box?

(b) Do application developers use standard key-value systems (such as Memcached) and manually remap query ranges to keys via space-filling curves or other spatial re-mapping techniques.

(c) Or can scalability be provided by a relational database with spatial extender which is extensively partitioned and replicated among several db instances.
Hi Javier and Ron,
thanks for the reference to Django, an ORM I have not been aware of until now. Interesting enough is that it also provides a key-value cache API using Memcached as back-end. And that takes me back to Ron's space-filling curves which are required to cache spatial data in key-value fashion. So thanks, Ron, for your offer. I will probably come back to it, as soon as I have started the coding part of this matter.