Modeling TCP throughput: a simple model and its empirical validation
Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
On the predictability of large transfer TCP throughput
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Shrink: a tool for failure diagnosis in IP networks
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
Interpreting the data: Parallel analysis with Sawzall
Scientific Programming - Dynamic Grids and Worldwide Computing
Predicting short-transfer latency from TCP arcana: a trace-based validation
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
Democratizing content publication with coral
NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
IP fault localization via risk modeling
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
A machine learning approach to TCP throughput prediction
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Towards highly reliable enterprise network services via inference of multi-level dependencies
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Answering what-if deployment and configuration questions with wise
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
WebProphet: automating performance prediction for web services
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
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Designers of content distribution networks (CDNs) often need to determine how changes to infrastructure deployment and configuration affect service response times when they deploy a new data center, change ISP peering, or change the mapping of clients to servers. Today, the designers use coarse, back-of-the-envelope calculations or costly field deployments; they need better ways to evaluate the effects of such hypothetical "what-if" questions before the actual deployments. This paper presents What-If Scenario Evaluator (WISE), a tool that predicts the effects of possible configuration and deployment changes in content distribution networks. WISE makes three contributions: 1) an algorithm that uses traces from existing deployments to learn causality among factors that affect service responsetime distributions; 2) an algorithm that uses the learned causal structure to estimate a dataset that is representative of the hypothetical scenario that a designer may wish to evaluate, and uses these datasets to predict hypothetical response-time distributions; 3) a scenario specification language that allows a network designer to easily express hypothetical deployment scenarios without being cognizant of the dependencies between variables that affect service response times. Our evaluation, both in a controlled setting and in a real-world field deployment on a large, global CDN, shows that WISE can quickly and accurately predict service response-time distributions for many practical what-if scenarios.