EC28-234 Nebraska System for Tattooing Hogs for Identification after Slaughter

A plan to identify the individual farm upon which hogs reaching markets are produced has been developed in connection with the efforts toward eradicating tuberculosis among farm animals. While primarily intended as a means of tracing back to determine sources of disease infection, the system of tattooing which is being developed has other significant possibilities. With the growing emphasis on quality products in the market, it is only fair that the producers of high quality commodities receive the premiums paid by processors and consumers. Health of farm animals is a quality factor. The producer of healthy hogs should be rewarded. Likewise, the producer of diseased hogs profits from knowledge that his animals are infected and can institute efforts to control that source fo loss. This 1928 extension circular covers what each letter of a tattoo stands for, where it is to be placed on an animal, and material used in tattooing.

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.