TRACTABLE DATA-FLOW ANALYSIS FOR DISTRIBUTED SYSTEMS

Automated behavior analysis is a valuable technique in the development and maintainence of distributed systems. In this paper, we present a tractable dataflow analysis technique for the detection of unreachable states and actions in distributed systems. The technique follows an approximate approach described by Reif and Smolka, but delivers a more accurate result in assessing unreachable states and actions. The higher accuracy is achieved by the use of two concepts: action dependency and history sets. Although the technique, does not exhaustively detect all possible errors, it detects nontrivial errors with a worst-case complexity quadratic to the system size. It can be automated and applied to systems with arbitrary loops and nondeterministic structures. The technique thus provides practical and tractable behavior analysis for preliminary designs of distributed systems. This makes it an ideal candidate for an interactive checker in software development tools. The technique is illustrated with case studies of a pump control system and an erroneous distributed program. Results from a prototype implementation are presented

Distributed Data Analysis in ATLAS

Data analysis using grid resources is one of the fundamental challenges to be addressed before the start of LHC data taking. The ATLAS detector will produce petabytes of data per year, and roughly one thousand users will need to run physics analyses on this data. Appropriate user interfaces and helper applications have been made available to ensure that the grid resources can be used without requiring expertise in grid technology. These tools enlarge the number of grid users from a few production administrators to potentially all participating physicists. ATLAS makes use of three grid infrastructures for the distributed analysis: the EGEE sites, the Open Science Grid, and NorduGrid. These grids are managed by the gLite workload management system, the PanDA workload management system, and ARC middleware; many sites can be accessed via both the gLite WMS and PanDA. Users can choose between two front-end tools to access the distributed resources. Ganga is a tool co-developed with LHCb to provide a common interface to the multitude of execution backends (local, batch, and grid). The PanDA workload management system provides a set of utilities called PanDA Client; with these tools users can easily submit Athena analysis jobs to the PanDA-managed resources. Distributed data is managed by Don Quixote 2, a system developed by ATLAS; DQ2 is used to replicate datasets according to the data distribution policies and maintains a central ca talog of file locations. The operation of the grid resources is continually monitored by the GangaRobot functional testing system, and infrequent site stress tests are performed using the HammerCloud system. In addition, the DAST shift team is a group of power users who take shifts to provide distributed analysis user support; this team has effectively relieved the burden of support from the developers