Application scalability has become one of the primary concerns for application designers and administrators. To add to the concern is the fact that existing tier-based business-critical applications are not able to achieve linear scalability due to the inherent limitations of existing architectures. What is needed is a software architecture pattern that allows for linear scalability of applications. Space based architecture (SBA) is one such pattern.

SBA works by co-locating the data and business logic thereby decreasing the inter-tier messaging, which makes it an ideal architecture for building scalable and fault tolerant applications. SBA is closely related to Shared Nothing (SN) based distributed computing architecture used by Google, Amazon and other well know companies for addressing the application scalability. It follows many of the principles of service oriented architecture (SOA), Representational State Transfer (REST), Event driven architecture (EDA), as well as certain elements of grid computing.

SBA is built on the concept of “Tuple Space” paradigm, a technology perfectly suited for implementing distributed caching in parallel computing.

1. Tuple: A tuple is an ordered list of elements. Tuples have their own data type and can be identified using a key comprising one or more fields. Tuples are also referred to as ''Entry Objects'' in Tuple Space terminology.
2. Space (Tuple Space): Space or more formally Tuple Space is an implementation of the associative memory paradigm for parallel/distributed computing i.e. it can be seen as a shared distributed cache memory. Tuple space enables concurrent accessing of tuples. Tuple Space is a paradigm for inter-process communication, information tuples are shared instead of the alternate message passing paradigm. Tuple Space has been implemented in many technologies and languages.

Space Based Architecture emphasizes development of applications as a collection of independent, loosely coupled services, minimizing the need to divide the logic into Tiers. 
The services thus created are basic units of SBA, communicating with data and logic co-located within the same process and known as ''Processing Unit'' (PU).  A PU is an independent entity that can be scaled easily by adding more hardware resources and by deploying it to the additional hardware without changing the existing setup.

SBA Implementations: A number of products are available that implement the concepts of Tuple Space. A few significant products are:
1.    Gigaspaces: Gigaspaces SBA combines and integrates parallel processing (Processing Grid), distributed caching (Data Grid), and content-based distributed messaging (Messaging Grid) to provide a common platform for high availability, clustering, consistency services and location transparency across all tiers.
2.    Javaspaces: Javaspaces is a specification for implementation of Tuple spaces using JINI.

References:
•    Javaspaces, Oracle, http://java.sun.com/developer/technicalArticles/tools/JavaSpaces/
•    Space Based Architecture, Wikipedia, http://en.wikipedia.org/wiki/Space-based_architecture
•    Gigaspaces, http://www.gigaspaces.com/  
•    The Scalability Revolution-From Dead End to Open Road, Gigaspaces, http://www.gigaspaces.com/files/main/Presentations/ByCustomers/white_papers/FromDeadEndToOpenRoad.pdf
•    Migrating from JEE application to SBA, Gigaspaces, http://www.gigaspaces.com/files/main/Presentations/ByCustomers/white_papers/MigratingFromJEEtoSBA.pdf
•    Wikipedia http://en.wikipedia.org/wiki/Tuple_space

*If you find something is misleading or not correct then please throw some light on it.

I stumbled upon a paper published by Proceedings of the National Academy of Sciences (PNAS) called “Network architecture of the long-distance pathways in the macaque brain” with key implication for reverse-engineering the human brain and developing a comprehensive network of cognitive-computing chips. The networks lead to the application of network-theoretic analysis that has been successful in understanding the Internet, social networking, and search in the world-wide web.

Don’t get me wrong. This is not a new field of study. Cognitive science has its roots in the 1950’s. However it’s only in the past 5 years that some significant Research milestones have been reached. The understanding of network of nervous system could act as a template for architecting future networks, cognitive systems, and resource allocation patterns in utility (cloud) based computing models.

Key highlights of PNAS research included the finding of how information travels and is processed across the nervous system. The brain network does not to scale freely like the social networks, which are logical and can grow without any constraints. This finding helps us to design the network routing architecture of cognitive computing chip. By analysing the current state of the body, the brain also allocates priority to body functions. This study can help in understanding and developing resource allocation patterns.  

Sebastian Seung: I am my connectome
Video on TED.com

Now let’s talk about Connectome. What is Connectome, you ask? Well a Connectome is a complete map of every neural connection in the brain.  To know more about Connectome you can view the video podcast by Sebastian Seung.

I believe Connectome will open new avenues of study in advanced computing. The human nervous system is the most complex and advanced Network on earth — probably even in our galaxy (Atleast for now and/or as far as we know of).  Just as the economy is interconnected and mainly controlled by a small but powerful core network, so too is our human brain. This discovery aligns astonishingly with nearly four decades of imaging studies that exhibit a ‘task-positive’ network implicates a goal-focussed performance and a ‘task-negative’ network activated when the brain is reserved and at restless.

Another reference for such research is the book Networks of the Brain, by author Olaf Sporns. In his book Sporns explains how the integrative nature of brain function can be illuminated from a complex network perspective. He introduces network theory to those working on theoretical network models. Sporns''s book unites function, dynamics , neural structure, and connectivity into a single and coherent framework.

Connectome goes a long way toward understanding the dynamic patterns of the brain that underlie behavior and cognition. With our current technology it will take at least 30-40 years (I am being extremely optimistic) to completely map and produce Connectome of the entire human nervous system. Connectome will offer synthesis of the sciences of complex networks that will be an essential foundation for future research in computer science.

Reference:
http://www.ibm.com/smarterplanet/us/en/business_analytics/article/cognitive_computing.html
http://hcp.600series.net/
http://www.kurzweilai.net/ibm-scientists-create-most-comprehensive-map-of-the-brains-network?utm_source=KurzweilAI+Daily+Newsletter&utm_campaign=2e47b007d7-UA-946742-1&utm_medium=email
http://www.dailygalaxy.com/my_weblog/2010/07/scientists-create-most-comprehensive-map-of-the-brains-network.html
http://www.modha.org/
http://humanconnectome.org/about/pressroom/tag/mapping-the-human-brain/
http://en.wikipedia.org/wiki/Connectome
http://www.ted.com/talks/sebastian_seung.html

*If you find something is misleading or not correct then please throw some light on it.