28,901 research outputs found
Fault-Tolerant Adaptive Parallel and Distributed Simulation
Discrete Event Simulation is a widely used technique that is used to model
and analyze complex systems in many fields of science and engineering. The
increasingly large size of simulation models poses a serious computational
challenge, since the time needed to run a simulation can be prohibitively
large. For this reason, Parallel and Distributes Simulation techniques have
been proposed to take advantage of multiple execution units which are found in
multicore processors, cluster of workstations or HPC systems. The current
generation of HPC systems includes hundreds of thousands of computing nodes and
a vast amount of ancillary components. Despite improvements in manufacturing
processes, failures of some components are frequent, and the situation will get
worse as larger systems are built. In this paper we describe FT-GAIA, a
software-based fault-tolerant extension of the GAIA/ART\`IS parallel simulation
middleware. FT-GAIA transparently replicates simulation entities and
distributes them on multiple execution nodes. This allows the simulation to
tolerate crash-failures of computing nodes; furthermore, FT-GAIA offers some
protection against byzantine failures since synchronization messages are
replicated as well, so that the receiving entity can identify and discard
corrupted messages. We provide an experimental evaluation of FT-GAIA on a
running prototype. Results show that a high degree of fault tolerance can be
achieved, at the cost of a moderate increase in the computational load of the
execution units.Comment: Proceedings of the IEEE/ACM International Symposium on Distributed
Simulation and Real Time Applications (DS-RT 2016
Fault Tolerant Adaptive Parallel and Distributed Simulation through Functional Replication
This paper presents FT-GAIA, a software-based fault-tolerant parallel and
distributed simulation middleware. FT-GAIA has being designed to reliably
handle Parallel And Distributed Simulation (PADS) models, which are needed to
properly simulate and analyze complex systems arising in any kind of scientific
or engineering field. PADS takes advantage of multiple execution units run in
multicore processors, cluster of workstations or HPC systems. However, large
computing systems, such as HPC systems that include hundreds of thousands of
computing nodes, have to handle frequent failures of some components. To cope
with this issue, FT-GAIA transparently replicates simulation entities and
distributes them on multiple execution nodes. This allows the simulation to
tolerate crash-failures of computing nodes. Moreover, FT-GAIA offers some
protection against Byzantine failures, since interaction messages among the
simulated entities are replicated as well, so that the receiving entity can
identify and discard corrupted messages. Results from an analytical model and
from an experimental evaluation show that FT-GAIA provides a high degree of
fault tolerance, at the cost of a moderate increase in the computational load
of the execution units.Comment: arXiv admin note: substantial text overlap with arXiv:1606.0731
Automating Fault Tolerance in High-Performance Computational Biological Jobs Using Multi-Agent Approaches
Background: Large-scale biological jobs on high-performance computing systems
require manual intervention if one or more computing cores on which they
execute fail. This places not only a cost on the maintenance of the job, but
also a cost on the time taken for reinstating the job and the risk of losing
data and execution accomplished by the job before it failed. Approaches which
can proactively detect computing core failures and take action to relocate the
computing core's job onto reliable cores can make a significant step towards
automating fault tolerance.
Method: This paper describes an experimental investigation into the use of
multi-agent approaches for fault tolerance. Two approaches are studied, the
first at the job level and the second at the core level. The approaches are
investigated for single core failure scenarios that can occur in the execution
of parallel reduction algorithms on computer clusters. A third approach is
proposed that incorporates multi-agent technology both at the job and core
level. Experiments are pursued in the context of genome searching, a popular
computational biology application.
Result: The key conclusion is that the approaches proposed are feasible for
automating fault tolerance in high-performance computing systems with minimal
human intervention. In a typical experiment in which the fault tolerance is
studied, centralised and decentralised checkpointing approaches on an average
add 90% to the actual time for executing the job. On the other hand, in the
same experiment the multi-agent approaches add only 10% to the overall
execution time.Comment: Computers in Biology and Medicin
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