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Hybrid Cloud Support for Large Scale Analytics and Web Processing
Navraj Chohan, Anand Gupta, Chris Bunch, Kowshik Prakasam, and Chandra Krintz, University of California, Santa Barbara
Platform-as-a-service (PaaS) systems, such as Google App Engine (GAE), simplify web application development and cloud deployment by providing developers with complete software stacks: runtime systems and scalable services accessible from well-defined APIs. Extant PaaS offerings are designed and specialized to support large numbers of concurrently executing web applications (multi-tier programs that encapsulate and integrate business logic, user interface, and data persistence). To enable this, PaaS systems impose a programming model that places limits on available library support, execution duration, data access, and data persistence. Although successful and scalable for web services, such support is not as amenable to online analytical processing (OLAP), which have variable resource requirements and require greater flexibility for ad-hoc query and data analysis. OLAP of web applications is key to understanding how programs are used in live settings.
In this work, we empirically evaluate OLAP support in the GAE public cloud, discuss its benefits, and limitations. We then present an alternate approach, which combines the scale of GAE with the flexibility of customizable offline data analytics. To enable this, we build upon and extend the AppScale PaaS – an open source private cloud platform that is API-compatible with GAE. Our approach couples GAE and AppScale to provide a hybrid cloud that transparently shares data between public and private platforms, and decouples public application execution from private analytics over the same datasets. Our extensions to AppScale eliminate the restrictions GAE imposes and integrates popular data analytic programming models to provide a framework for complex analytics, testing, and debugging of live GAE applications with low overhead and cost.
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