As Joyent CTO and Chief Scientist Jason Hoffman told GigaOm, "There actually is not anything of comparison in the world ... not even remotely close [in terms of a general-purpose data center], ... They're the only people who actually sat down in the last 20 years and thought what should a data center look like today, not in 1985."
The end result of this futuristic view of data center requirements is an enormously scaled, highly efficient (1.24 PUE), cost-effective computing environment that makes the typical corporate data center look like a relic ready for the scrap heap. A New Breed of Apps, Big Data and Game Changers
But what's driving the need for corporations to use these external providers? What's changed in terms of computing needs that would require a fundamentally different approach to computing? The final presentation in the conference's infrastructure track illuminated how applications are rapidly transforming to support new business requirements.
Michael Peacock is a United Kingdom-based software developer at Smith Electric Vehicles, which manufactures battery-powered commercial vehicles. These aren't golf cart-sized vehicles, either. They run eight to 13 tons and transport payloads ranging from 7,000 to 16,000 pounds. In a phrase, big iron.
As one might imagine, it's important to keep track of what happens with these trucks throughout the workday -- speeds travelled, power consumption, motor speeds and so on. Smith has extensive telemetry built into its vehicles, so much so that it sends, in near real-time, enough data that it results in 4,000 MySQL inserts per second, totaling 1.5 billion inserts each day.
When Peacock started his project, his company's computing infrastructure was overwhelmed. The changes put into place to support the need for truck telemetry that he described are eye-opening, to say the least.
Suffice it to say that the IT infrastructure of this rather traditional enterprise (it was founded in 1920) now resembles a big data, cloud-based, NoSQL-using Web-scale company, with the migration to the new infrastructure driven by sheer scale.
What did Smith do to its environment to address its telemetry requirements?
One example: To support traffic prone to bursts and unpredictable processing requirements, Smith shifted to a queue-based task submission architecture, with the queue located in a cloud provider's infrastructure.
Additionally, the data loads overwhelmed the capacity of Smith's storage infrastructure, requiring a deep dive into hardware configuration in order to wring as much performance out of the SAN as possible.
A third example: The application's databases were streamlined with schema redesign and sharding to improve application performance.
Finally, to improve analytics performance, data were pre-aggregated via background batch processing in order to reduce processing time for queries.
IT As a the Business Proess
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