Tim Kraska

"Forecast: Cloudy with a Chance of Consistency"

Cloud computing promises virtually unlimited scalability and high availability at low cost. However, it is commonly believed that a system's consistency must be relaxed in order to achieve these properties. We evaluated existing commercial cloud offerings for transactional workloads and found that consistency is indeed expensive and limits scalability in these systems. Because of these costs, many systems designers have chosen to provide only relaxed consistency guarantees, if any, making such systems inappropriate for many mission-critical applications. This dichotomy is based on unrealistically pessimistic assumptions about Big Data environments. First, it assumes that consistency is an all or nothing decision that must be applied uniformly to all data in a system. Secondly, even in situations where strong consistency is required, previous transaction commit protocols were based on worst-case assumptions regarding the likelihood of conflicts. In this talk, I will describe two techniques that build on a more nuanced view of consistency requirements and the costs of maintaining them.

I will first describe Consistency Rationing, which builds on inventory holding models used in Operations Research to help classify and manage data based on their consistency requirements. Consistency Rationing exploits the fact that for some data the cost of maintaining consistency outweighs the benefit obtained by avoiding inconsistencies. In the second part of the talk, I will present a new optimistic commit protocol for the wide-area network. For a long time, synchronized wide-area replication was considered to be infeasible with strong consistency. With MDCC, I will show how we can achieve strong consistency with similar response-time guarantees as eventual consistency in the normal operational case. This work was done as part of a larger project around Big Data management. At the end of the talk, I will provide an overview of some of my other projects and give an outline for future work.

Bio: Tim Kraska is a PostDoc in the AMPLab, which is part of the Computer Science Division at UC Berkeley. Currently his research focuses on Big Data management in the cloud and hybrid human/machine database systems. Before joining UC Berkeley, Tim Kraska received his PhD from ETH Zurich, where he worked on transaction management and stream processing in the cloud as part of the Systems Group. He received a Swiss National Science Foundation Prospective Researcher Fellowship (2010), a University of Sydney Master of Information Technology Scholarship for outstanding achievement (2005), the University of Sydney Siemens Prize (2005), and a VLDB best demo award (2011).
Tim Kraska is a faculty candidate.