System-level Diagnosis Algorithm Research Articles

Distributed Diagnosis of Dynamic Events in Partitionable Arbitrary Topology Networks

This work introduces the Distributed Network Reachability (DNR) algorithm, a distributed system-level diagnosis algorithm that allows every node of a partitionable arbitrary topology network to determine which portions of the network are reachable and unreachable. DNR is the first distributed diagnosis algorithm that works in the presence of network partitions and healings caused by dynamic fault and repair events. Both crash and timing faults are assumed, and a faulty node is indistinguishable of a network partition. Every link is alternately tested by one of its adjacent nodes at subsequent testing intervals. Upon the detection of a new event, the new diagnostic information is disseminated to reachable nodes. New events can occur before the dissemination completes. Any time a new event is detected or informed, a working node may compute the network reachability using local diagnostic information. The bounded correctness of DNR is proved, including the bounded diagnostic latency, bounded startup and accuracy. Simulation results are presented for several random and regular topologies, showing the performance of the algorithm under highly dynamic fault situations.

A fast pessimistic diagnosis algorithm for generalized hypercube multicomputer systems

The reliability of processors is an important issue for designing a massively parallel processing system for which fault-tolerant computing is crucial. In order to achieve high system reliability and availability, a faulty processor (node) when found should be replaced by a fault-free processor. Within a multiprocessor system, the technique of identifying faulty nodes by constructing tests on the nodes and inter- preting the test outcomes is known as system-level diagnosis. The topological struc- ture of a multicomputer system can be modeled by a graph of which the vertices and edges correspond to nodes and links of the system, respectively. This work presents a system-level diagnosis algorithm for a generalized hypercube which is an attractive variance of a hypercube. The proposed algorithm is based on the PMC model and can isolate all faulty nodes to within a set which contains at most one fault-free node. If the total number of nodes to be diagnosed in a generalized hypercube is N ,t he proposed algorithm can run in O(N log N) time, and being superior to Yang's algo- rithm proposed in 2004, it can diagnose not only a hypercube but also a generalized hypercube.

A linear time pessimistic one-step diagnosis algorithm for hypercube multicomputer systems

This paper presents a system-level diagnosis algorithm for hypercube multicomputer systems based on the PMC model. The algorithm can isolate all faulty processors to a set with at most one fault-free processor. The breadth-first search (BFS) is applied in the new algorithm. Let N denote the total number of processors in a hypercube system. With the help of properties of hypercube tree defined in this paper and a new tree-decomposition technique, the algorithm runs in O( N) time if log 2 N ⩾ 10; whereas the best-known diagnosis algorithm runs in O( N log 2 N) time if log 2 N ⩾ 19.

A generalized model for distributed comparison-based system-level diagnosis

This work introduces a new system-level diagnosis model and an algorithm based on this model: Hi-Comp (Hierarchical Comparison-based Adaptive Distributed System-Level Diagnosis algorithm). This algorithm allows the diagnosis of systems that can be represented by a complete graph. Hi-Comp is the first diagnosis algorithm that is, at the same time, hierarchical, distributed and comparison-based. The algorithm is not limited to crash fault diagnosis, because its tests are based on comparisons. To perform a test, a processor sends a task to two processors of the system that, after executing the task, send their outputs back to the tester. The tester compares the two outputs; if the comparison produces a match, the tester considers the tested processors fault-free; on the other hand, if the comparison produces a mismatch, the tester considers that at least one of the two tested processors is faulty, but can not determine which one. Considering a system of N nodes, it is proved that the algorithm's diagnosability is (N-1) and the latency is log2N testing rounds. Furthermore, a formal proof of the maximum number of tests required per testing round is presented, which can be O(N³). Simulation results are also presented.

A generalized model for distributed comparison-based system-level diagnosis

Abstract This work introduces a new system-level diagnosis model and an algorithm based on this model: Hi-Comp (Hierarchical Comparison-based Adaptive Distributed System-Level Diagnosis algorithm). This algorithm allows the diagnosis of systems that can be represented by a complete graph. Hi-Comp is the first diagnosis algorithm that is, at the same time, hierarchical, distributed and comparison-based. The algorithm is not limited to crash fault diagnosis, because its tests are based on comparisons. To perform a test, a processor sends a task to two processors of the system that, after executing the task, send their outputs back to the tester. The tester compares the two outputs; if the comparison produces a match, the tester considers the tested processors fault-free; on the other hand, if the comparison produces a mismatch, the tester considers that at least one of the two tested processors is faulty, but can not determine which one. Considering a system of N nodes, it is proved that the algorithm’s diagnosability is (N-1) and the latency is log2N testing rounds. Furthermore, a formal proof of the maximum number of tests required per testing round is presented, which can be O(N3). Simulation results are also presented.

A fast pessimistic one-step diagnosis algorithm for hypercube multicomputer systems

This paper describes a system-level diagnosis algorithm for hypercube multicomputer systems. The algorithm is based on the PMC model and can isolate all faulty processors to within a set that contains at most one fault-free processor. If we denote by N the total number of processors in a hypercube system to be diagnosed, then, based on the judiciously designed data structures, the algorithm can run in O( Nlog 2 N) time; whereas the best-known diagnosis algorithm, the YML algorithm, runs in O( N 2.5) time. Consequently, the new algorithm is remarkably superior to the YML algorithm in terms of the time cost.

An Isochronous Testing Strategy for Hierarchical Adaptive Distributed System-Level Diagnosis

Distributed System-level diagnosis allows the fault-free components of a fault-tolerant distributed system to determine which components of the system are faulty and which are fault-free. The time it takes for nodes running the algorithm to diagnose a new event is called the algorithm's latency. In this paper we present a new distributed system-level diagnosis algorithm which presents a latency of O(log N) testing rounds, for a system of N nodes. A previous hierarchical distributed system-level diagnosis algorithm, Hi-ADSD, presents a latency of O(log2 N) testing rounds. Nodes are grouped in progressively larger logical clusters for the purpose of testing. The algorithm employs an isochronous testing strategy that forces all fault-free nodes to execute tests on clusters of the same size each testing round. This strategy is based on two main principles: a tested node must test its tester in the same rounds a node only accepts tests according to a lexical priority order. We present formal proofs that the algorithm's latency is at most 2log N – 1 testing rounds and that the testing strategy of the algorithm leads to the execution of isochronous tests. Simulation results are shown for systems of up to 64 nodes.

A hierarchical adaptive distributed system-level diagnosis algorithm

Consider a system composed of N nodes that can be faulty or fault-free. The purpose of distributed system-level diagnosis is to have each fault-free node determine the state of all nodes of the system. This paper presents a Hierarchical Adaptive Distributed System-level Diagnosis (Hi-ADSD) algorithm, which is a fully distributed algorithm that allows every fault-free node to achieve diagnosis in, at most, (log/sub 2/ N)/sup 2/ testing rounds. Nodes are mapped into progressively larger logical clusters, so that tests are run in a hierarchical fashion. Each node executes its tests independently of the other nodes, i.e., tests are run asynchronously. All the information that nodes exchange is diagnostic information. The algorithm assumes no link faults, a fully-connected network and imposes no bounds on the number of faults. Both the worst-case diagnosis latency and correctness of the algorithm are formally proved. As an example application, the algorithm was implemented on a 37-node Ethernet LAN, integrated to a network management system based on SNMP (Simple Network Management Protocol). Experimental results of fault and repair diagnosis are presented. This implementation by itself is also a significant contribution, for, although fault management is a key functional area of network management systems, currently deployed applications often implement only rudimentary diagnosis mechanisms. Furthermore, experimental results are given through simulation of the algorithm for large systems of 64 nodes and 512 nodes.

A distributed system-level diagnosis algorithm for arbitrary network topologies

A distributed algorithm is described for detecting and diagnosing faulty processors in an arbitrary network. Fault free processors perform simple periodic tests on one another; when a fault is detected or a newly repaired processor joins the network, this new information is disseminated in parallel throughout the network. It is formally proven that the algorithm is correct, and it is also shown that the algorithm is optimal in terms of the time required for all of the fault free processors in the network to learn of a new event. Simulation results are given for arbitrary network topologies. >

Implementation of online distributed system-level diagnosis theory

The practical application and implementation of online distributed system-level diagnosis theory is documented. Proven distributed diagnosis algorithms are shown to be impractical in real systems due to high resource requirements. A distributed system-level diagnosis algorithm called Adaptive DSD is shown to minimize network resources and has resulted in a practical implementation. Adaptive DSD assumes a distributed network, in which network nodes can test other nodes and determine them to be faulty or fault-free. Tests are issued from each node adaptively and depend on the fault situation of the network. Test result reports are generated from test results and forwarded between nodes in the network. Adaptive DSD is proven correct in that each fault-free node reaches an accurate independent diagnosis of the fault conditions of the remaining nodes. No restriction is placed on the number of faulty nodes; any fault situation with any number of faulty nodes is diagnosed correctly. An implementation of the Adaptive DSD algorithm is described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>