Speaker
I. Legrand
(CALTECH)
Description
The design and optimization of the Computing Models for the future LHC experiments,
based on the Grid technologies, requires a realistic and effective modeling and
simulation of the data access patterns, the data flow across the local and wide area
networks, and the scheduling and workflow created by many concurrent, data intensive
jobs on large scale distributed systems.
This paper presents the latest generation of the MONARC (MOdels of Networked Analysis
at Regional Centers) simulation framework, as a design and modelling tool for large
scale distributed systems applied to HEP experiments. A process-oriented approach for
discrete event simulation is used for describing concurrent running programs, as well
as the stochastic arrival patterns that characterize how such systems are used. The
simulation engine is based on Threaded Objects, (or Active Objects) which offer great
flexibility in simulating the complex behavior of distributed data processing
programs. The engine provides an appropriate scheduling mechanism for the Active
Objects with efficent support for interrupts.
The framework provides a complete set of basic components (processing nodes, data
servers, network components) together with dynamically loadable decision units
(scheduling or data replication modules) for easily building complex Computing Model
simulations.
Examples of simulating complex data processing systems, specific for the LHC
experiments (production tasks associated with data replication and interactive
analysis on distributed farms) are presented, and the way the framework is used to
compare different decision making algorithms or to optimize the overall Grid
architecture.
Primary authors
H. Newman
(Caltech)
I. Legrand
(CALTECH)
