Rollback Mechanisms for Cloud Management APIs Using AI Planning

Human-induced faults play a large role in systems reliability. In cloud platforms, system administrators may inadvertently make catastrophic mistakes, like deleting a virtual disk with important data. Providing rollback for cloud operations can reduce the severity and impact of such mistakes, by all...

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Bibliographic Details
Published in:IEEE transactions on dependable and secure computing Vol. 17; no. 1; pp. 148 - 161
Main Authors: Satyal, Suhrid, Weber, Ingo, Bass, Len, Fu, Min
Format: Journal Article
Language:English
Published: Washington IEEE 01.01.2020
IEEE Computer Society
Subjects:
Aggregates
AI planning
Algorithms
Application programming interface
Artificial intelligence
Australia
Cloud computing
Cybersecurity
Human influences
Parallel processing
Planning
Reliability
system administration
System reliability
Virtual machining
Visibility
web service
ISSN:1545-5971, 1941-0018
Online Access:Get full text
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Summary:Human-induced faults play a large role in systems reliability. In cloud platforms, system administrators may inadvertently make catastrophic mistakes, like deleting a virtual disk with important data. Providing rollback for cloud operations can reduce the severity and impact of such mistakes, by allowing to revert to a known, good state. However, in the context of cloud management this is non-trivial, since cloud consumers only have limited visibility and indirect control. In this paper, we present a scalable approach to rollback operations that change the state of a system on proprietary cloud platforms. In our previous work, we provided a system that augments cloud APIs and provides rollback operation using an AI planner. In this paper, we build upon our previous work, but parallelize the rollback plan generation based on characteristics unique to rollback scenario. Furthermore, we introduce a distributed anytime algorithm that gradually improves plan quality overtime, until either an optimal plan is found or a timeout is reached. Through experimental evaluation we show that our approach scales better than a naive approach, and effectively avoids the exponential behavior of AI planning. Further, we explore the trade-offs between the quality of rollback plans and plan generation time.
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ISSN:1545-5971
1941-0018
DOI:10.1109/TDSC.2017.2729543