File Descriptors on steroids
Introduction: Data Structures for persisting and organizing data on disk has remained the same as desktops evolved from mainframes. However, as more and more data migrate from the desktop to the cloud storage, the operating system artifacts for the desktop seem woefully inadequate to meet the features of the cloud such as elasticity, redundancy, availability, aging, deduplication, conversion, translation, parsing, sealing extents, IO scheduling and finally, replication. While some of these may be mitigated by higher level applications and services, it brings in players and vendors that have different areas of emphasis. A retail company wanting to own a business specific service at cloud scale starts relying on more technological notions and disparate systems while not addressing the minimal requirement of an in-process compute and storage requirements. Instead if the service were given primitives that looked and behaved the same as they do at an individual compute resource level, then they can also work with resources at cloud scale so long as the primitives are made smarter.
Implementation: We have solved the problem of communications via peer to peer networking and message queuing to not just multicast but implement publisher subscriber models. Moreover, we have made networking and concurrency into libraries that expand on the definition of their corresponding primitives and can be hosted in process. We offer notions of background processing with libraries like Celery that application developers and enterprise architects use to offload intensive processing away from end customers. They however end up creating fatter servers and dumb clients that can work anywhere on any device. If we contrast this model, we can have more uniform processing in peer nodes and on-demand basis if there were operating system primitives that supported the existing and new functionalities for cloud from the operating system notion of a process itself.
Conclusion: Enhanced File descriptors transcend clouds and bring the cloud capabilities in process.

#codingexercise
Find the JacobsthalLucas number
uint GetJacobsthalLucas(uint n)
{
if (n == 0) return 2;
if (n == 1) return 1;
return GetJacobsthalLucas(n-1) + 2 * GetJacobsthalLucas(n-2);
}
0 1 1 3 5 11 ...
While Pascal's triangle forms from diagonal bands of Fibonacci numbers, Jacobsthal Lucas numbers also forms computed values from adjacent numbers along the diagonals.
Jacobsthal and Jacobsthal Lucas numbers are part of Lucas series.