CloudPD: Problem determination and diagnosis in shared dynamic clouds

In this work, we address problem determination in virtualized clouds. We show that high dynamism, resource sharing, frequent reconfiguration, high propensity to faults and automated management introduce significant new challenges towards fault diagnosis in clouds. Towards this, we propose CloudPD, a...

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Bibliographic Details
Main Authors: Sharma, Bikash, Jayachandran, Praveen, Verma, Akshat, Das, Chita R.
Format: Conference Proceeding
Language:English
Subjects:
Biological system modeling
Cloud
Context
Correlation
Engines
Fault Diagnosis
Hadoop MapReduce
Measurement
Monitoring
Problem Determination
Servers
Virtualization
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Summary:In this work, we address problem determination in virtualized clouds. We show that high dynamism, resource sharing, frequent reconfiguration, high propensity to faults and automated management introduce significant new challenges towards fault diagnosis in clouds. Towards this, we propose CloudPD, a fault management framework for clouds. CloudPD leverages (i) a canonical representation of the operating environment to quantify the impact of sharing; (ii) an online learning process to tackle dynamism; (iii) a correlation-based performance models for higher detection accuracy; and (iv) an integrated end-to-end feedback loop to synergize with a cloud management ecosystem. Using a prototype implementation with cloud representative batch and transactional workloads like Hadoop, Olio and RUBiS, it is shown that CloudPD detects and diagnoses faults with low false positives (
ISSN:1530-0889
2158-3927
DOI:10.1109/DSN.2013.6575298