Arctic Report Card 2007: Tracking Recent Environmental Changes

Collectively, the observations indicate that the overall warming of the Arctic system continued in 2007. There are some elements that are stabilizing or returning to climatological norms. These mixed tendencies illustrate the sensitivity and complexity of the Arctic System. Atmosphere: Hot spot shifts toward Europe Ocean: North Pole Temperatures at depth returning to 1990s values Sea Ice: Summer extent at record minimum Greenland: Recent warm temperatures associated with net ice loss Biology: increasing tundra shrub cover and variable treeline advance; up to 80% declines in some caribou herds while goose populations double Land: Increase in permafrost temperatures The Arctic Report Card 2007 is introduced as a means of presenting clear, reliable and concise information on recent observations of environmental conditions in the Arctic, relative to historical time series records. It provides a method of updating and expanding the content of the State of the Arctic Report, published in fall 2006, to reflect current conditions. Material presented in the Report Card is prepared by an international team of scientists and is peer-reviewed by topical experts nominated by the US Polar Research Board. The audience for the Arctic Report Card is wide, including scientists, students, teachers, decision makers and the general public interested in Arctic environment and science. The web-based format will facilitate future timely updates of the content.

Sofya: Supporting Rapid Development of Dynamic Program Analyses for Java

Dynamic analysis is an increasingly important means of supporting software validation and maintenance. To date, developers of dynamic analyses have used low-level instrumentation and debug interfaces to realize their analyses. Many dynamic analyses, however, share multiple common high-level requirements, e.g., capture of program data state as well as events, and efficient and accurate event capture in the presence of threading. We present SOFYA – an infra-structure designed to provide high-level, efficient, concurrency-aware support for building analyses that reason about rich observations of program data and events. It provides a layered, modular architecture, which has been successfully used to rapidly develop and evaluate a variety of demanding dynamic program analyses. In this paper, we describe the SOFYA framework, the challenges it addresses, and survey several such analyses.

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.