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Open Access Publications from the University of California

Global-Scale Data Management with Strong Consistency Guarantees

  • Author(s): Nawab, Faisal
  • Advisor(s): Agrawal, Divyakant
  • El Abbadi, Amr
  • et al.
Abstract

Global-scale data management(GSDM) empowers systems by providing higher levels of fault-tolerance, read availability, and efficiency in utilizing cloud resources. This has led to the emergence of global-scale data management and event processing. However, the Wide-Area Network (WAN) latency separating datacenters is orders of magnitude larger than typical network latencies, and this requires a reevaluation of many of the traditional design trade-offs of data management systems. Therefore, data management problems must be revisited to account for the new design space.

In this dissertation, we propose theoretical foundations to understand the limits imposed by WAN latency on GSDM, and propose practical systems and protocols to minimize the overhead caused by WAN latency. The presented work spans global-scale transaction processing, communication, analytics, and machine learning. In all these directions, the focus is on the trade-off between consistency and latency, where we ask the question: what is the best performance (often latency) we can achieve without compromising the consistency and integrity of data? For transaction processing, we propose a lower-bound formulation for transaction latency that is imposed by the WAN latency. Also, we propose a new paradigm for transaction processing (proactive coordination) that inspired out two proposed protocols, Message Futures and Helios, which can achieve the lower-bound latency. We also propose a communication framework, called Chariots, to scale multi-datacenter communication. Chariots is carefully designed to allow scaling communication while providing a consistent view of the communicated information. Finally, we explore challenges in global-scale analytics and machine learning. Specifically, we propose Ogre, a scalable system for global-scale heterogeneous transactional and analytics workloads. Also, we propose COP, a system designed to speed up machine learning on globally generated data.

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