Fault Localization in Cloud using Centrality Measures
This addresses fault tolerance in distributed cloud systems, but appears incremental as it modifies existing graph-based methods.
The paper tackles fault localization in cloud environments by modeling faults as a weighted graph and modifying graph optimization approaches with centrality measures, achieving optimal and accurate fault localization.
Fault localization is an imperative method in fault tolerance in a distributed environment that designs a blueprint for continuing the ongoing process even when one or many modules are non-functional. Visualizing a distributed environment as a graph, whose nodes represent faults (fault graph), allows us to introduce probabilistic weights to both edges and nodes that cause the faults. With multiple modules like databases, run-time cloud, etc. making up a distributed environment and extensively, a cloud environment, we aim to address the problem of optimally and accurately performing fault localization in a distributed environment by modifying the Graph optimization approach to localization and centrality, specific to fault graphs.