PROJECT TITLE:

Adaptive Algorithms for Diagnosing Large-Scale Failures in Computer Networks - 2015

ABSTRACT:

We tend to propose a greedy algorithm, Cluster-MAX-COVERAGE (CMC), to efficiently diagnose large-scale clustered failures. We have a tendency to primarily address the challenge of determining faults with incomplete symptoms. CMC makes novel use of both positive and negative symptoms to output a hypothesis list with a low variety of false negatives and false positives quickly. CMC requires reports from about 0.5 as many nodes as alternative existing algorithms to determine failures with one hundred percent accuracy. Moreover, CMC accomplishes this gain significantly faster (typically by 2 orders of magnitude) than an algorithm that matches its accuracy. When there are fewer positive and negative symptoms at a reporting node, CMC performs a lot of higher than existing algorithms. We have a tendency to conjointly propose an adaptive algorithm known as Adaptive-MAX-COVERAGE (AMC) that performs efficiently during both independent and clustered failures. Throughout a series of failures that embody both independent and clustered, AMC leads to a reduced range of false negatives and false positives.