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Zenodo
2021
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| Online-Zugang: | https://doi.org/10.5281/zenodo.18902004 |
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| _version_ | 1866901598442094592 |
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| author | Boddupally, Hema Latha |
| author_facet | Boddupally, Hema Latha |
| contents | <p>Modern enterprise systems increasingly rely on distributed, service-oriented, and cloud-native<br>architectures composed of multiple interacting layers including infrastructure, platforms,<br>applications, and user-facing services where failures often propagate across components in nonlinear and time-dependent ways. While these architectures deliver scalability, resilience, and<br>rapid innovation, they also introduce significant challenges for fault diagnosis and operational<br>troubleshooting due to high system cardinality, dynamic service dependencies, frequent<br>deployments, and heterogeneous telemetry sources such as logs, metrics, and traces. Traditional<br>root cause analysis (RCA) approaches, which depend heavily on manual inspection, static<br>topology assumptions, or rule-based heuristics, are increasingly inadequate in this environment,<br>as they struggle to distinguish true causal signals from correlated noise and cascading effects. This<br>article examines intelligent root cause analysis techniques for multi-layer enterprise systems,<br>focusing on three foundational pillars: distributed tracing to capture end-to-end execution context,<br>data-driven anomaly correlation to identify statistically significant failure indicators, and graphbased dependency modeling to represent and reason about system structure and failure<br>propagation. Drawing on established research and industry practices published between 2000 and<br>2021, we synthesize how these complementary techniques collectively enable faster, more<br>accurate, and increasingly automated root cause identification, reducing mean time to resolution<br>and improving operational reliability in complex enterprise environments.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18902004 |
| institution | Zenodo |
| language | |
| publishDate | 2021 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | TOWARD INTELLIGENT ROOT CAUSE ANALYSIS IN MULTI-LAYER ENTERPRISE SYSTEMS: TRACING, STATISTICAL INFERENCE, AND DEPENDENCY-GRAPH REASONING Boddupally, Hema Latha Root Cause Analysis, Distributed Systems, Enterprise Systems, Observability, AIOps, Distributed Tracing, Dependency Graphs, Log Analytics, Anomaly Detection, Knowledge Graphs <p>Modern enterprise systems increasingly rely on distributed, service-oriented, and cloud-native<br>architectures composed of multiple interacting layers including infrastructure, platforms,<br>applications, and user-facing services where failures often propagate across components in nonlinear and time-dependent ways. While these architectures deliver scalability, resilience, and<br>rapid innovation, they also introduce significant challenges for fault diagnosis and operational<br>troubleshooting due to high system cardinality, dynamic service dependencies, frequent<br>deployments, and heterogeneous telemetry sources such as logs, metrics, and traces. Traditional<br>root cause analysis (RCA) approaches, which depend heavily on manual inspection, static<br>topology assumptions, or rule-based heuristics, are increasingly inadequate in this environment,<br>as they struggle to distinguish true causal signals from correlated noise and cascading effects. This<br>article examines intelligent root cause analysis techniques for multi-layer enterprise systems,<br>focusing on three foundational pillars: distributed tracing to capture end-to-end execution context,<br>data-driven anomaly correlation to identify statistically significant failure indicators, and graphbased dependency modeling to represent and reason about system structure and failure<br>propagation. Drawing on established research and industry practices published between 2000 and<br>2021, we synthesize how these complementary techniques collectively enable faster, more<br>accurate, and increasingly automated root cause identification, reducing mean time to resolution<br>and improving operational reliability in complex enterprise environments.</p> |
| title | TOWARD INTELLIGENT ROOT CAUSE ANALYSIS IN MULTI-LAYER ENTERPRISE SYSTEMS: TRACING, STATISTICAL INFERENCE, AND DEPENDENCY-GRAPH REASONING |
| topic | Root Cause Analysis, Distributed Systems, Enterprise Systems, Observability, AIOps, Distributed Tracing, Dependency Graphs, Log Analytics, Anomaly Detection, Knowledge Graphs |
| url | https://doi.org/10.5281/zenodo.18902004 |