Ian T. Foster

LDV: Light-weight database virtualization

We present a light-weight database virtualization (LDV) system that allows users to share and re-execute applications that operate on a relational database (DB). Previous methods for sharing DB applications, such as companion websites and virtual machine images (VMIs), support neither easy and efficient re-execution nor the sharing of only a relevant DB subset. LDV addresses these issues by monitoring application execution, including DB operations, and using the resulting execution trace to create a lightweight re-executable package. A LDV package includes, in addition to the application, either the DB management system (DBMS) and relevant data or, if the DBMS and/or data cannot be shared, just the application-DBMS communications for replay during re-execution. We introduce a linked DB-operating system provenance model and show how to infer data dependencies based on temporal information about the DB operations performed by the applications process(es). We use this model to determine the DB subset that needs to be included in a package in order to enable re-execution. We compare LDV with other sharing methods in terms of package size, monitoring overhead, and re-execution overhead. We show that LDV packages are often more than an order of magnitude smaller than a VMI for the same application, and have negligible re-execution overhead.

Numerical and Implementation Issues in Food Quality Modeling for Human Diseases Prevention.

Monitoring nearshore sea water pollution using connected smart devices could be nowadays impracticable due to the aggressive saline environment, the network availability and the maintain and calibration costs. Accurate forecast of marine pollution is most needed to evaluate the adverse effects on coastal inhabitants’ health when fishes and mussels farming economically characterizes the local social background. In an operational context, numerical simulations are performed routinely on a dedicated computational infrastructure producing space and temporal high-resolution predictions of weather and marine conditions of the Bay of Naples. In this paper we present our results in developing a community open source Lagrangian pollutant transport and dispersion model, leveraging on hierarchical parallelism implying distributed memory, shared memory and GPGPUs. Some numerical details are also discussed. This system has been used to develop an alarm system to help local authorities in making decisions regarding the collection of mussels. The model setup and the simulation results will be improved using FairWind, an under development system dedicated to coastal marine crowdsourced data gathering and sharing, based on smart devices and Internet of Things afloat.