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SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
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Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
Brittle Metrics in Operating Systems Research
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USITS'99 Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems - Volume 2
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Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
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IEEE Transactions on Mobile Computing
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IEEE/ACM Transactions on Networking (TON)
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ICACT'09 Proceedings of the 11th international conference on Advanced Communication Technology - Volume 1
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WiOPT'09 Proceedings of the 7th international conference on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
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GPC'08 Proceedings of the 3rd international conference on Advances in grid and pervasive computing
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NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
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USENIX'09 Proceedings of the 2009 conference on USENIX Annual technical conference
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IEEE Communications Magazine
DieCast: Testing Distributed Systems with an Accurate Scale Model
ACM Transactions on Computer Systems (TOCS)
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NSDI'07 Proceedings of the 4th USENIX conference on Networked systems design & implementation
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RAID'05 Proceedings of the 8th international conference on Recent Advances in Intrusion Detection
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Computer Networks: The International Journal of Computer and Telecommunications Networking
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The performance of popular Internet Web services is governed by a complex combination of server behavior, network characteristics and client workload-all interacting through the actions of the underlying transport control protocol (TCP). Consequently, even small changes to TCP or to the network infrastructure can have significant impact on end-to-end performance, yet at the same time it is challenging for service administrators to predict what that impact will be. In this paper we describe the implementation of a tool called Monkey that is designed to help address such questions. Monkey collects live TCP trace data near a server, distills key aspects of each connection (e.g., network delay, bottleneck bandwidth, server delays, etc.) and then is able to faithfully replay the client workload in a new setting. Using Monkey, one can easily evaluate the effects of different network implementations or protocol optimizations in a controlled fashion, without the limitations of synthetic workloads or the lack of reproducibility of live user traffic. Using realistic network traces from the Google search site, we show that Monkey is able to replay traces with a high degree of accuracy and can be used to predict the impact of changes to the TCP stack.