The performance of Internet-based services depends on many
server-side, client-side, and network related factors. Often, the
interaction among the factors or their effect on service performance
is not known or well-understood. The complexity of these services
makes it difficult to develop analytical models. Lack of models
impedes network management tasks, such as predicting performance while
planning for changes to service infrastructure, or diagnosing causes
of poor performance.
We posit that we can use statistical causal methods to model
performance for Internet-based services and facilitate performance
related network management tasks. Internet-based services are
well-suited for statistical learning because the inherent variability
in many factors that affect performance allows us to collect
comprehensive datasets that cover service performance under a wide
variety of conditions. These conditional distributions represent the
functions that govern service performance and dependencies that are
inherent in the service infrastructure. These functions and
dependencies are accurate and can be used in lieu of analytical models
to reason about system performance, such as predicting performance of
a service when changing some factors, finding causes of poor
performance, or isolating contribution of individual factors in
observed performance.
We present three systems, What-if Scenario Evaluator (WISE), How to
Improve Performance (HIP), and Network Access Neutrality Observatory
(NANO), that use statistical causal methods to facilitate network
management tasks. WISE predicts performance for what-if configurations
and deployment questions for content distribution networks. For this,
WISE learns the causal dependency structure among the latency-causing
factors, and when one or more factors is changed, WISE estimates
effect on other factors using the dependency structure. HIP extends
WISE and uses the causal dependency structure to invert the
performance function, find causes of poor performance, and help
answers questions about how to improve performance or achieve
performance goals. NANO uses causal inference to quantify the impact
of discrimination policies of ISPs on service performance. NANO is the
only tool to date for detecting destination-based discrimination
techniques that ISPs may use.
We have evaluated these tools by application to large-scale
Internet-based services and by experiments on wide-area Internet.
WISE is actively used at Google for predicting network-level and
browser-level response time for Web search for new datacenter
deployments. We have used HIP to find causes of high-latency Web
search transactions in Google, and identified many cases where
high-latency transactions can be significantly mitigated with simple
infrastructure changes. We have evaluated NANO using experiments on
wide-area Internet and also made the tool publicly available to
recruit users and deploy NANO at a global scale.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/33927 |
| Date | 06 April 2010 |
| Creators | Tariq, Muhammad Mukarram Bin |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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