16 Maio 2014, 17:20 - Lucília Abreu
As users of "big data" applications want fresh processing results, we witness a new breed of stream processing systems that are designed to scale to large numbers of cloud-hosted machines. Such systems face new challenges: (i) to benefit from the "pay-as-you-go" model of cloud computing, they must scale out on demand; (ii) with deployments on hundreds of virtual machines (VMs), failures are common -- systems must therefore be fault-tolerant with fast recovery times. An open question is how to achieve these two goals when stream queries include stateful operators whose state may depend on the complete history of the stream. In this talk, I describe an integrated approach for dynamic scale out and recovery of stateful stream processing operators. The idea is to expose internal operator state explicitly to the stream processing system through a set of state management primitives. Externalised operator state is checkpointed periodically and backed up by the system. In addition, the system identifies operator bottlenecks and automatically scales them out by allocating new VMs. We evaluate this approach as part of the SEEP experimental stream processing system on the Amazon EC2 cloud platform and show that it can scale automatically, while recovering quickly from failures. (This talk is based on work published at ACM SIGMOD'13 and USENIX ATC'14.)
Peter Pietzuch is a Senior Lecturer (Associate Professor) at Imperial College London, leading the Large-scale Distributed Systems (LSDS) group in the Department of Computing. His research focuses on the design and engineering of scalable, reliable and secure large-scale software systems, with a particular interest in data management and networking issues. He has published over sixty research papers in international venues, including USENIX ATC, NSDI, SIGMOD, VLDB, ICDE, ICDCS, Middleware and DEBS. He has co-authored a book on Distributed Event-based Systems published by Springer. Before joining Imperial College, he was a post-doctoral fellow at Harvard University. He holds PhD and MA degrees from the University of Cambridge. (http://www.doc.ic.ac.uk/~prp/).
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