Chad Verbowski

Summary-based routing for content-based event distribution networks

Providing scalable distributed Web-based eventing services has been an important research topic. It is desirable to have an effective mechanism for the servers to summarize their filters for in-network preprocessing in order to optimize system performance. In this paper, we propose a summary-based routing mechanism and introduce the notion of imprecise summaries to provide a trade-off between routing overhead and event traffic. Our system uses similarity-based filter clustering to reduce overall event traffic and performs self-tuning summary precision selection to optimize throughput. We have implemented summary-based routing on top of an XML-based infrastructure that closely follows the proposed Web services standards. Measurements from the actual implementation validate our analytical and simulation results, and demonstrate the practical benefits of the proposed techniques.

Towards a self-managing software patching process using black-box persistent-state manifests

We describe an approach to self-managing software patching. We identify visibility into patch impact as the key missing component in automating the current patching process, and we present a suite of components that provides this visibility by constructing black-box persistent-state manifests through self-monitoring of dependencies. Additionally, we use the component suite to measure the actual impact of recent patches on several important commercial applications.

Persistent-state checkpoint comparison for troubleshooting configuration failures

Application failures characterized by the phrases, “it worked yesterday, but it doesn’t work today” and “it worked on that machine, but it doesn’t work on this machine” are a major source of computer user frustration and a major component in the total cost of ownership. The typical symptom-based troubleshooting approach relies too much on creative thinking and may lead users or support technicians in directions far from the actual root cause. In this paper, we propose a state-based troubleshooting approach for configuration failures that aims at making the diagnostic process as mechanical as possible. In the narrow-down phase, we use checkpoint comparison and application tracing to determine which pieces of persistent state have changed and are affecting current application execution; ongoing self-monitoring of persistent-state changes by the machine is used to help eliminate false positives. In the solution-query phase, state-to-task mapping and searches of online databases are used to translate low-level state information into highlevel user interfaces and articles. We describe the design and implementation of a troubleshooter that uses this state-based approach and present preliminary results to demonstrate its effectiveness in diagnosing several actual configuration failures.

Analyzing persistent state interactions to improve state management

A primary challenge to building reliable and secure computer systems is managing the persistent state of the system: all the executable files, configuration settings and other data that govern how a system functions. The difficulty comes from the sheer volume of this persistent state, the frequency of changes to it, and the variety of workloads and requirements that require customization of persistent state. The cost of not managing a system‘s persistent state effectively is high: configuration errors are the leading cause of downtime at Internet services, troubleshooting configuration problems is a leading component of total cost of ownership in corporate environments, and malware— effectively, unwanted persistent state—is a serious privacy and security concern on personal computers. In this paper, we analyze how computer systems dynamically interact with files and configuration settings in an attempt to gain insights into the problem of persistent state management. We analyze over 3648 machine days of these persistent state interactions, collected over an 8 month period from 193 machines. These machines are under real workloads and include Internet servers, corporate desktops, and home machines. We characterize the scope and magnitude of the persistent state management problem today, measuring not only the gross characteristics of persistent state, but also analyzing how it is used by applications, and when administrators and users modify it. We find that monitoring persistent state interactions provides important visibility and show how it can be used as a foundation for building better persistent state management tools.