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.

Distribution of Rain Gardens in Lincoln Nebraska: Are Rain Gardens more Likely to be Built Near Bodies of Water

Abstract Rain gardens are an important tool in reducing the amount of stormwater runoff and accompanying pollutants from entering the city’s streams and lakes, and reducing their water quality. This thesis project analyzed the number of rain gardens installed through the City of Lincoln Nebraska Watershed Management’s Rain Garden Water Quality Project in distance intervals of one-eighth mile from streams and lakes. This data shows the distribution of these rain gardens in relation to streams and lakes and attempts to determine if proximity to streams and lakes is a factor in homeowners installing rain gardens. ArcGIS was used to create a map with layers to determine the number of houses with rain gardens in 1/8 mile distance increments from the city’s streams and lakes and their distances from a stream or lake. The total area, number of house parcels, and the type and location of each parcel type were also determined for comparison between the distance interval increments. The study revealed that fifty-eight percent of rain gardens were installed within a quarter mile of a stream or lake (an area covering 60% of the city and including 58.5% of the city’s house parcels), and that eighty percent of rain gardens were installed within three-eighth mile of streams or lakes (an area covering 75% of the city and 78.5% of the city’s house parcels). All parcels in the city are within 1 mile of a stream or lake. Alone the number of project houses per distance intervals suggested that proximity to a stream or lake was a factor in people’s decisions to install rain gardens. However, when compared to the number of house parcels available, proximity disappears as a factor in project participation.