JVM-based Techniques for Improving Java Observability

Observability measures the support of computer systems to accurately capture, analyze, and present (collectively observe) the internal information about the systems. Observability frameworks play important roles for program understanding, troubleshooting, performance diagnosis, and optimizations. However, traditional solutions are either expensive or coarse-grained, consequently compromising their utility in accommodating today’s increasingly complex software systems. New solutions are emerging for VM-based languages due to the full control language VMs have over program executions. Existing such solutions, nonetheless, still lack flexibility, have high overhead, or provide limited context information for developing powerful dynamic analyses. In this thesis, we present a VM-based infrastructure, called marker tracing framework (MTF), to address the deficiencies in the existing solutions for providing better observability for VM-based languages. MTF serves as a solid foundation for implementing fine-grained low-overhead program instrumentation. Specifically, MTF allows analysis clients to: 1) define custom events with rich semantics ; 2) specify precisely the program locations where the events should trigger; and 3) adaptively enable/disable the instrumentation at runtime. In addition, MTF-based analysis clients are more powerful by having access to all information available to the VM. To demonstrate the utility and effectiveness of MTF, we present two analysis clients: 1) dynamic typestate analysis with adaptive online program analysis (AOPA); and 2) selective probabilistic calling context analysis (SPCC). In addition, we evaluate the runtime performance of MTF and the typestate client with the DaCapo benchmarks. The results show that: 1) MTF has acceptable runtime overhead when tracing moderate numbers of marker events; and 2) AOPA is highly effective in reducing the event frequency for the dynamic typestate analysis; and 3) language VMs can be exploited to offer greater observability.

Simulations of Ground-Water Flow, Transport, Age, and Particle Tracking near York, Nebraska, for a Study of Transport of Anthropogenic and Natural Contaminants (TANC) to Public-Supply Wells

The U.S. Geological Survey (USGS) is committed to providing the Nation with credible scientific information that helps to enhance and protect the overall quality of life and that facilitates effective management of water, biological, energy, and mineral resources (http://www.usgs.gov/). Information on the Nation’s water resources is critical to ensuring long-term availability of water that is safe for drinking and recreation and is suitable for industry, irrigation, and fish and wildlife. Population growth and increasing demands for water make the availability of that water, now measured in terms of quantity and quality, even more essential to the long-term sustainability of our communities and ecosystems. The USGS implemented the National Water-Quality Assessment (NAWQA) Program in 1991 to support national, regional, State, and local information needs and decisions related to water-quality management and policy (http://water.usgs.gov/nawqa). The NAWQA Program is designed to answer: What is the condition of our Nation’s streams and ground water? How are conditions changing over time? How do natural features and human activities affect the quality of streams and ground water, and where are those effects most pronounced? By combining information on water chemistry, physical characteristics, stream habitat, and aquatic life, the NAWQA Program aims to provide science-based insights for current and emerging water issues and priorities. From 1991-2001, the NAWQA Program completed interdisciplinary assessments and established a baseline understanding of water-quality conditions in 51 of the Nation’s river basins and aquifers, referred to as Study Units (http://water.usgs.gov/nawqa/studyu.html).