Using TACT to Build High Performance and Highly Available Internet Services

DETAILS:
April 29, 10-11:30AM NSH 3305ABSTRACT:
System designers wishing to build fast and highly available
Internet services must contend with network congestion, latency, and
unpredictable failures. Replication is a key approach for improving service
performance and availability. Unfortunately, the benefits of
replication are limited by the overhead of maintaining consistency across the
wide area. While many Internet services do not require
strong consistency, existing optimistic consistency models allow replicas to
become arbitrarily stale.
This talk describes TACT, a continuous consistency model that allows replicas to
bound their divergence from strong consistency. A
spanning set of metrics---Numerical Error, Order Error, and Staleness---captures
the consistency semantics for a broad range of
network services. Using these metrics, applications can dynamically trade
consistency for performance and availability based on changing client, network,
and service characteristics. Fundamentally, we aim to determine upper bounds on
the performance and availability of replicated services as a function of
workload, faultload, and consistency.
After presenting the motivation and design of TACT, I will describe the results
of a number of experiments quantifying the tradeoff
between performance, availability, and consistency for a number of sample
Internet services running across the wide area. Next, I
present a technique for calculating tight upper bounds on the availability of
replicated services as a function of workload, fault
load, and desired level of consistency and compare the achieved availability of
replicated services using existing consistency
protocols relative to this upper bound. Simple optimizations to these protocols
allow services to approach our calculated upper bound for a number of measured
faultloads.
More information is available at
http://www.cs.duke.edu/~vahdat
BIOGRAPHY:
Amin Vahdat is an Assistant Professor of Computer Science at Duke University. He received his PhD in Computer Science from UC Berkeley in 1998 under the supervision of Thomas Anderson and joined the faculty at Duke University the same year. He received the NSF CAREER award in 2000 and the Alfred P. Sloan Fellowship in 2003. Amin has won awards for teaching as well as best paper awards at conferences such as USENIX and OSDI.