29,834 research outputs found
Volume 71, Number 21, March 21, 1952
14th Turkish Symposium on Artificial Intelligence and Neural Networks -- JUN 16-17, 2005 -- Izmir, TURKEYWOS: 000239585200025Replication of data or processes is an effective way to provide enhanced performance, high availability and fault tolerance in distributed systems. For instance, in systems based on the client-server model, a server may serve many clients and because of heavy loads, the server cannot respond to the requests on time. In such a case, replicating data or servers may improve performance. Moreover, data and processes can be replicated to protect against failures. However, this is a very complex procedure. In this paper, I propose a method, to make systems fault tolerant based on replication, by way of exploiting the use of collaborative agents. This method is also used to improve fault tolerance in multi-agent systems.Izmir Inst Technol, EE & CE Depts, Turkish Sci & Res Council, Izmir Branch Chamber Elect & Elect Engineer
Using mobility and exception handling to achieve mobile agents that survive server crash failures
Mobile agent technology, when designed and used effectively, can minimize bandwidth consumption and autonomously provide a snapshot of the current context of a distributed system. Protecting mobile agents from server crashes is a challenging issue, since developers normally have no control over remote servers. Server crash failures can leave replicas, instable storage, unavailable for an unknown time period. Furthermore, few systems have considered the need for using a fault tolerant protocol among a group of collaborating mobile agents. This thesis uses exception handling to protect mobile agents from server crash failures. An exception model is proposed for mobile agents and two exception handler designs are investigated. The first exists at the server that created the mobile agent and uses a timeout mechanism. The second, the mobile shadow scheme, migrates with the mobile agent and operates at the previous server visited by the mobile agent. A case study application has been developed to compare the performance of the two exception handler designs. Performance results demonstrate that although the second design is slower it offers the smaller trip time when handling a server crash. Furthermore, no modification of the server environment is necessary. This thesis shows that the mobile shadow exception handling scheme reduces complexity for a group of mobile agents to survive server crashes. The scheme deploys a replica that monitors the server occupied by the master, at each stage of the itinerary. The replica exists at the previous server visited in the itinerary. Consequently, each group member is a single fault tolerant entity with respect to server crash failures. Other schemes introduce greater complexity and performance overheads since, for each stage of the itinerary, a group of replicas is sent to servers that offer an equivalent service. In addition, future research is established for fault tolerance in groups of collaborating mobile agents
Distributed Adaptive Fault-Tolerant Control of Uncertain Multi-Agent Systems
This paper presents an adaptive fault-tolerant control (FTC) scheme for a
class of nonlinear uncertain multi-agent systems. A local FTC scheme is
designed for each agent using local measurements and suitable information
exchanged between neighboring agents. Each local FTC scheme consists of a fault
diagnosis module and a reconfigurable controller module comprised of a baseline
controller and two adaptive fault-tolerant controllers activated after fault
detection and after fault isolation, respectively. Under certain assumptions,
the closed-loop system's stability and leader-follower consensus properties are
rigorously established under different modes of the FTC system, including the
time-period before possible fault detection, between fault detection and
possible isolation, and after fault isolation
An approach to rollback recovery of collaborating mobile agents
Fault-tolerance is one of the main problems that must be resolved to improve the adoption of the agents' computing paradigm. In this paper, we analyse the execution model of agent platforms and the significance of the faults affecting their constituent components on the reliable execution of agent-based applications, in order to develop a pragmatic framework for agent systems fault-tolerance. The developed framework deploys a communication-pairs independent check pointing strategy to offer a low-cost, application-transparent model for reliable agent- based computing that covers all possible faults that might invalidate reliable agent execution, migration and communication and maintains the exactly-one execution property
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
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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