NSDI '08 – Abstract
Pp. 407–421 of the Proceedings
DieCast: Testing Distributed Systems with an Accurate Scale Model
Diwaker Gupta, Kashi V. Vishwanath, and Amin Vahdat, University of California, San Diego
Abstract
Large-scale network services can consist of tens of thousands of
machines running thousands of unique software configurations
spread across hundreds of physical networks. Testing such services
for complex performance problems and configuration errors remains a difficult problem.
Existing testing techniques, such as simulation or
running smaller instances of a service, have limitations in
predicting overall service behavior.
Although technically and economically infeasible at this time, testing
should ideally be performed at the same scale and with the same
configuration as the deployed service. We present DieCast, an approach to
scaling network services in which we multiplex all of the nodes in a
given service configuration as virtual machines (VM) spread across a much
smaller number of physical machines in a test harness.
CPU, network, and disk are then accurately scaled to provide the illusion that
each VM matches a machine from the original service in terms of both available
computing resources and communication behavior to remote service nodes.
We present the architecture and evaluation of a system to support such
experimentation and discuss its limitations. We show that for a
variety of services--including a commercial, high-performance, cluster-based file
system--and resource utilization levels, DieCast matches the behavior of
the original service while using a fraction of the physical
resources.
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