Model-driven performance evaluation for service engineering

Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Software quality aspects such as performance are of central importance for the integration of heterogeneous, distributed service-based systems. Empirical performance evaluation is a process of measuring and calculating performance metrics of the implemented software. We present an approach for the empirical, model-based performance evaluation of services and service compositions in the context of model-driven service engineering. Temporal databases theory is utilised for the empirical performance evaluation of model-driven developed service systems

Estimation of Sounding Uncertainty from Measurements of Water Mass Variability

Analysis techniques are introduced that allow for estimation of potential sounding uncertainty due to water mass variability from reconnaissance campaigns in which oceanographic parameters are measured at a high temporal and spatial resolution. The analysis techniques do not require sounding data, thus analyses can be tailored to match any survey system; this allows for pre-analysis campaigns to optimize survey instrumentation and sound speed profiling rates such that a desired survey specification can be maintained. Additionally, the output of the analysis methods can potentially provide a higher fidelity estimation of sounding uncertainty due to water mass variability than uncertainty models in common use

Decrypting The Java Gene Pool: Predicting Objects' Lifetimes with Micro-patterns

Pretenuring long-lived and immortal objects into infrequently or never collected regions reduces garbage collection costs significantly. However, extant approaches either require computationally expensive, application-specific, off-line profiling, or consider only allocation sites common to all programs, i.e. invoked by the virtual machine rather than application programs. In contrast, we show how a simple program analysis, combined with an object lifetime knowledge bank, can be exploited to match both runtime system and application program structure with object lifetimes. The complexity of the analysis is linear in the size of the program, so need not be run ahead of time. We obtain performance gains between 6-77% in GC time against a generational copying collector for several SPEC jvm98 programs