Symbiosis in scale out networking and data management

Abstract

This talk highlights the symbiotic relationship between data management and networking through a study of two seemingly independent trends in the traditionally separate communities: large-scale data processing and software defined networking. First, data processing at scale increasingly runs across hundreds or thousands of servers. We show that balancing network performance with computation and storage is a prerequisite to both efficient and scalable data processing. We illustrate the need for scale out networking in support of data management through a case study of TritonSort, currently the record holder for several sorting benchmarks, including GraySort and JouleSort. Our TritonSort experience shows that disk-bound workloads require 10 Gb/s provisioned bandwidth to keep up with modern processors while emerging flash workloads require 40 Gb/s fabrics at scale. We next argue for the need to apply data management techniques to enable Software Defined Networking (SDN) and Scale Out Networking. SDN promises the abstraction of a single logical network fabric rather than a collection of thousands of individual boxes. In turn, scale out networking allows network capacity (ports, bandwidth) to be expanded incrementally, rather than by wholesale fabric replacement. However, SDN requires an extensible model of both static and dynamic network properties and the ability to deliver dynamic updates to a range of network applications in a fault tolerant and low latency manner. Doing so in networking environments where updates are typically performed by timer-based broadcasts and models are specified as comma-separated text files processed by one-off scripts presents interesting challenges. For example, consider an environment where applications from routing to traffic engineering to monitoring to intrusion/anomaly detection all essentially boil down to inserting, triggering and retrieving updates to/from a shared, extensible data store.