Mastering Scalable Computing Models
Data as the new currency
The world we live in is generating new data at unprecedented rates today. Worldwide, millions of connected devices collect data about anything and everything around the clock, adding up to a few quintillion bytes per day. In the past two years alone, more than 90 percent of all data has been generated. Most of this data is transmitted over the internet and stored on servers. Thanks to the low cost of disk space, data storage is not a limitation anymore.
So, what happens to all the collected data? Eventually, some of it is used to inform R&D, manufacturing and even marketing decisions at corporations that aim to stay competitive. As storage today costs virtually nothing, all that’s needed to extract the needed insights is heaps of data and computing power capable of processing it. Sounds simple, doesn’t it?
Data-intensive scalable computing
Before the world started drowning in data, standard computing systems were used to perform resource-intense computations. Furthermore, as datasets grew exponentially, so did the requirements on the hardware and software systems needed to successfully manage them.
Today, DISC (data-intensive scalable computing) systems are being used to manage the data that we collect about products, services, customers and suppliers at all physical and digital touchpoints, during manufacturing, operations, launch, sales and post-sale service processes. The applications for this type of data computing for enterprises are wide-ranging, and so are the potential insights. The latter can inform virtually all enterprise processes from selecting the most optimal raw materials, production times and shift managers to choosing the name and branding for the final products.
The insights are extracted by algorithms built to process large
volumes of seemingly meaningless data that can help identify trends years before
they fully unroll. These are just some examples of the potential of scalable
computing for organizations, though, its value can be as far-reaching as one’s
imagination. Any kind of data that is currently recorded and stored can also be
analyzed in detail, in order to draw meaningful conclusions that bring on
powerful insights and result in profitable decisions.
Limitations of cluster computing systems
If enterprise data computing is so powerful, why don’t we currently analyze all big data that we collect to access unknown trends about the future demand for products and services like those we offer? The challenge largely comes down to computing power and internet speed. To process large datasets, the data needs to be split into thousands of disks that can then be handled by individual processors simultaneously. The more complex and powerful the system, the more error-prone it is, which means it must be built to accommodate frequent and multiple points of failure.
Although this may sound like a simple problem to solve given the current state of technological advances, IT departments at large organizations are struggling to perfect the concept of parallel computing needed to ensure high levels of reliability, performance and ease of operation, while not completely breaking the bank. Numerous attempts to design advanced computing environments that meet these needs have hit a brick wall or proven too expensive for what they’re able to deliver.
The power of cloud computing
More than 10 years ago now, Google CEO Eric Schmidt introduced the concept of cloud computing to the world. Unlike the first scalable computing concepts that required the management of local, large-scale server clusters, cloud computing refers to the practice of using networks of remotely located, connected servers to store, manage and process big data.
A novel and attractive approach that requires fewer financial, computing or human resources, the cloud computing model quickly gained popularity among large and mid-sized corporations globally. The main advantage of this scalable model is that it allows organizations to store and analyze their data without requiring an army of IT professionals and sizeable server farms for storage and processing. Essentially, cloud computing was the first truly scalable approach introduced to the market.
Amazon is currently leading the way in providing cloud computing services
to the public with their AWS (Amazon Web Services) offering, though, a similar
approach has also been adopted by their competitors Google Cloud, Microsoft
Azure, IBM and Oracle, to name a few. In the end, the selection of a cloud
computing provider comes down to cost, speed and reliability.
Scalable alternatives to cloud computing
Some academic institutions, whose research largely depends on the
quality of their computing power, have devised lower cost, in-house computing
models out of need. Some of the proposed alternatives to outsourced cloud services
include scalable PC cluster systems that use standard PCs and run inexpensive
or free open-source software. While these types of alternatives to
high-performance computing may work in academia, implementing such makeshift
systems would be unthinkable in the corporate world. This leaves enterprises
with cloud computing as the only viable and cost-efficient outsourced model at present.