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.