Abstract
The purpose of distributed system-level diagnosis is to have each fault-free nodes determine the state of all nodes of system. The paper presents a Multi-level distributed system-level diagnosis, which considers the problem of achieving scalability and performance tuning for distributed diagnosis. Existing work is aimed to reduce either diagnosis latency or network utilization but scales poorly. A diagnosis algorithm, called Multi-level DSD, is presented to provide scalability, which controls both latency and network utilization in fully connected networks. The algorithm is scalable in the sense that it is possible to diagnose system with large number of processing elements (nodes) by tuning diagnosis parameters. The diagnosis algorithm allows tuning of diagnosis performance to lever latency message cost trade-off. Multi-level DSD divides system in clusters of nodes, where each cluster is either a single node or a group of clusters. Cluster diagnoses itself by running a cluster diagnosis algorithm between its sub clusters. Clusters at each level runs same cluster diagnosis algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chandrapal, P., Kumar, P. (2005). A Scalable Multi-level Distributed System-Level Diagnosis. In: Chakraborty, G. (eds) Distributed Computing and Internet Technology. ICDCIT 2005. Lecture Notes in Computer Science, vol 3816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11604655_23
Download citation
DOI: https://doi.org/10.1007/11604655_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30999-4
Online ISBN: 978-3-540-32429-4
eBook Packages: Computer ScienceComputer Science (R0)