A Scalable Approach to Automated Service Dependency Modeling in Heterogeneous Environments
EDOC '01 Proceedings of the 5th IEEE International Conference on Enterprise Distributed Object Computing
Pinpoint: Problem Determination in Large, Dynamic Internet Services
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
Performance debugging for distributed systems of black boxes
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Forensic Analysis of File System Intrusions Using Improved Backtracking
IWIA '05 Proceedings of the Third IEEE International Workshop on Information Assurance
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Capturing, indexing, clustering, and retrieving system history
Proceedings of the twentieth ACM symposium on Operating systems principles
A Framework for Failure Impact Analysis and Recovery with Respect to Service Level Agreements
SCC '05 Proceedings of the 2005 IEEE International Conference on Services Computing - Volume 02
PPPJ '06 Proceedings of the 4th international symposium on Principles and practice of programming in Java
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Zodiac: efficient impact analysis for storage area networks
FAST'05 Proceedings of the 4th conference on USENIX Conference on File and Storage Technologies - Volume 4
Impact Analysis of Faults and Attacks in Large-Scale Networks
IEEE Security and Privacy
Detecting application-level failures in component-based Internet services
IEEE Transactions on Neural Networks
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Performing impact analysis involves determining which users are affected by system resource failures. Understanding when users are actually using certain resources allows system administrators to better assess the impact on enterprise operations. This is critical to prioritizing system repair and restoration actions, and allowing users to modify their plans proactively. We present an approach that combines traditional dependency analysis with resource usage information to improve the operational relevance of these assessments. Our approach collects data from end-user systems using common operating system commands, and uses this data to generate dependency and usage pattern information. We tested our approach in a computer lab running applications at various levels of complexity, and demonstrate how our framework can be used to assist system administrators in providing clear and concise impact assessments to executive managers.