The work reported in this paper is motivated towards validating an alternative approach for fault tolerance over traditional methods like checkpointing that constrain efficacious fault tolerance. Can agent intelligence be used to achieve fault tolerant parallel computing systems? If so, "What agent capabilities are required for fault tolerance?", "What parallel computational tasks can benefit from such agent capabilities?" and "How can agent capabilities be implemented for fault tolerance?" need to be addressed. Cognitive capabilities essential for achieving fault tolerance through agents are considered. Parallel reduction algorithms are identified as a class of algorithms that can benefit from cognitive agent capabilities. The Message Passing Interface is utilized for implementing an intelligent agent based approach. Preliminary results obtained from the experiments validate the feasibility of an agent based approach for achieving fault tolerance in parallel computing systems.
Research on multi-agent based fault tolerance is reported in @cite_4 @cite_10 @cite_5 @cite_0 @cite_14 . Though research has been pursued on multi-agents focusing on fault tolerance, it is surprising that there has been little effort towards extending and implementing such ideas for large scale parallel computing systems.
@cite_14 @cite_4 @cite_0 @cite_5 @cite_10
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Multi agent systems (MAS) are expected to be involved in futuristic technologies. Agents require some execution environment in which they can publish their service interfaces and can provide services to other agent. Such execution environment is called agent platform (AP). From a technical point of view any abnormal behavior of platform can distress agents residing on that platform. That's why it is necessary to provide a suitable architecture for the AP which should not only provide fault tolerance but also scalability features. There also exist some management components within the platform, which provide services to application agents. All the agents within MAS are managed by agent management system (AMS) which is the mandatory supervisory authority of any AP. To be more scalable, a single agent platform can be distributed over several machines which not only provides load balancing but also fault tolerance depending upon the distributed architecture of the AP. In existing systems, AMS is centralized i.e. it exists on one machine. With centralized AMS, this infrastructure lacks fault tolerance, which is a key feature of high assurance. Absence of fault tolerance is the main reason for the small number of deployments of MAS. Failure of AMS leads towards abnormal behavior in the distributed platform. This paper proposes virtual agent cluster (VAC) paradigm which strongly supports decentralized AMS to achieve fault tolerance in distributed AP. VAC provides fault tolerance by using separate communication layers among different machines. Experiments show that it improves performance, brings autonomy and supports fault recovery along with load balancing in distributed AP. We propose the robust middleware approach to transparent fault tolerance in parallel and distributed systems. The proposed approach inserts a robust middleware between algorithms programs and system architecture hardware. With the robust middleware, hardware faults are transparent to algorithms programs so that ordinary algorithms programs developed for fault-free networks can run on faulty parallel distributed systems without modifications. Moreover, the robust middleware automatically adds fault tolerance capability to ordinary algorithms programs so that no hardware redundancy or reconfiguration capability is required and no assumption is made about the availability of a complete subnetwork (at a lower dimension or smaller size). We also propose nomadic agent multithreaded programming as a novel fault-aware programming paradigm that is independent of network topologies and fault patterns. Nomadic agent multithreaded programming is adaptive to fault traffic workload patterns, and can take advantages of various components of the robust middleware, including the fault tolerance features and multiple embeddings, without relying on specialized robust algorithms Fault tolerant algorithms for the numerical approximation of elliptic partial differential equations on modern supercomputers play a more and more important role in the future design of exa-scale enabled iterative solvers. Here, we combine domain partitioning with highly scalable geometric multigrid schemes to obtain fast and fault-robust solvers in three dimensions. The recovery strategy is based on a hierarchical hybrid concept where the values on lower dimensional primitives such as faces are stored redundantly and thus can be recovered easily in case of a failure. The lost volume unknowns in the faulty region are re-computed approximately with multigrid cycles by solving a local Dirichlet problem on the faulty subdomain. Different strategies are compared and evaluated with respect to performance, computational cost, and speed up. Especially effective are strategies in which the local recovery in the faulty region is executed in parallel with global solves and when the local recovery is additionally accelerated. This results in an asynchronous multigrid iteration that can fully compensate faults. Excellent parallel performance on a current peta-scale system is demonstrated. The growing importance of multi-agent applications and the need for a higher quality of service in these systems justify the increasing interest in fault-tolerant multi-agent systems. In this article, we propose an original method for providing dependability in multi-agent systems through replication. Our method is different from other works because our research focuses on building an automatic, adaptive and predictive replication policy where critical agents are replicated to avoid failures. This policy is determined by taking into account the criticality of the plans of the agents, which contain the collective and individual behaviors of the agents in the application. The set of replication strategies applied at a given moment to an agent is then fine-tuned gradually by the replication system so as to reflect the dynamicity of the multi-agent system.