Data centric computing for internet scale enterprises
Yuqing Gao · 2013
In the era of exploding internet usage, social and mobile, enterprises are facing both the challenges and business opportunities that are introduced by Big Data, which has the characteristics of high volume, high velocity, and high variety. Big Data and the emergence of Internet-facing workloads will blur the separation between traditional transactional and analytics workloads. To extract business value and make actionable insight from the unprecedented volume of the data with the agility required from the business, it requires transformational innovations from many fronts. For example, in data management layer, how unstructured data is stored and retrieved efficiently, how data-intensive analytic computation can be done on commercial systems effectively, how the distributed cache should be designed to make use of the latest network protocols so the network-connected memory data can be accessed remotely and seamlessly. Moreover, the trend also motivates many architectural and technological advancement, such as moving from a transaction-centric to a data-centric architecture that supports extreme low and predicable latency, massive scale-out, high concurrency, and real-time situational awareness and analytics, and that requires orders of magnitude improvement over existing systems across each of these characteristics. At the same time, new applications in the Mobile and social space leverage new open source software stacks written in multiple programming languages, e.g., Java, JavaScript, Ruby, PHP, where the developer chooses the best tool for the job. How a polyglot runtime platform can be built that serves as a best practice platform for the programmers' community and in the meantime, optimized for enterprises with elastic, lightweight, resilient, agile runtime for business computing. Last, but not least, how the benchmarks should be enriched to measure the new runtimes, new data-centric systems and architectures.