Integrating &amp; Extending Machine Learning-Based Prediction...
Creators: name: University Of California Office Of The President
Publisher: name: University of California Office of the President
publisherIdentifier: https://ror.org/00dmfq477
publisherIdentifierScheme: ROR
Year: 2024

description: Google, Microsoft, Facebook, Yahoo, Ebay: now household words, companies like these have driven the IT revolution with Internet services such as search, email, and auctions. The scale of these services is staggering: Facebook serves over 20,000 page views per second to over 100 million users; Google Earth stores 10 times more data than all the text in the Library of Congress. Yet we use these datacenter-scale services daily for free, and they are integral to the state and national economy. However, the complexity of developing, deploying and managing such services remains beyond the reach of all but the largest companies, because of the intense manual effort required to "scale up" programming techniques originally designed for smaller-scale programming. Pierre Omidyar created the first prototype of Ebay over a long weekend, but its spectacular growth required multiple re-engineering efforts costing tens of millions of dollars. Our goal is to enable rapid innovation by making it possible for a single individual--not just a multibillion-dollar company--to develop and operate tomorrow's "killer app". We have successfully collaborated with world leaders in Statistical Machine Learning (SML), a field of applied mathematics for identifying important patterns in extremely large collections of data, to simplify many aspects of datacenter management: alerting operators to impending problems before they happen, automatically predicting resource requirements of giant-scale applications, and even identifying opportunities for saving energy without reducing service quality. In this proposal, we extend our work in two important directions. The first is a facility for storing and manipulating large amounts of data interconnected in "social network" graphs, as in LinkedIn, Facebook and YouTube. While structured data storage techniques for small systems (tens of thousands of users) are mature, they are nonexistent for systems of tens of millions of users. Our novel storage system, SCADS (Scalable Consistency-Adjustable Data Storage), is a system on which programmers can confidently create complex data structures because SCADS uses sophisticated SML to automatically adapt to rapid changes in user demand. The second observation is "don't put all your eggs in one datacenter." Resilience to natural disasters, availability of cheaper or "greener" power at different times or locations, and the desire for a competitive marketplace all suggest that applications will have to span across multiple datacenters. Yet today's technologies for building applications do not readily support this. We will leverage our experience designing "telemetry" and data analysis systems to extend existing techniques across datacenters and create new techniques for automatic cross-datacenter scaling. We will demonstrate our results by simulating datacenters using both a state-of-the-art Sun Microsystems BlackBox containerized computing facility and Amazon's on-demand Elastic Comput