This dissertation introduces, implements, and evaluates the novel concept of
"Service Paths", which are system-level abstractions that capture and describe
the dynamic dependencies between the different components of a distributed
enterprise application. Service paths are dynamic because they capture the
natural interactions between application services dynamically composed to offer
some desired end user functionality. Service paths are distributed because such
sets of services run on networked machines in distributed enterprise data
centers. Service paths cross multiple levels of abstraction because they link
end user application components like web browsers with system services like
http providing communications with embedded services like hardware-supported
data encryption. Service paths are system-level abstractions that are created
without end user, application, or middleware input, but despite these facts,
they are able to capture application-relevant performance metrics, including
end-to-end latencies for client requests and the contributions to these
latencies from application-level processes and from software/hardware resources
like protocol stacks or network devices.
Beyond conceiving of service paths and demonstrating their utility, this thesis
makes three concrete technical contributions. First, we propose a set of signal
analysis techniques called ``E2Eprof' that identify the service paths taken
by different request classes across a distributed IT infrastructure and
the time spent in each such path. It uses a novel algorithm called ``pathmap'
that computes the correlation between the message arrival and departure
timestamps at each participating node and detect dependencies among them. A
second contribution is a system-level monitoring toolkit called ``SysProf',
which captures monitoring information at different levels of granularity,
ranging from tracking the system-level activities triggered by a single system
call, to capturing the client-server interactions associated with a service
paths, to characterizing the server resources consumed by sets of clients or
client behaviors.
The third contribution of the thesis is a publish-subscribe based monitoring
data delivery framework called ``QMON'. QMON offers high levels of
predictability for service delivery and supports utility-aware monitoring
while also able to differentiate between different levels of service
for monitoring, corresponding to the different classes of SLAs maintained for
applications.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/16171 |
| Date | 09 July 2007 |
| Creators | Agarwala, Sandip |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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