Cloud Anomaly Detection via Vector Clustering
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Solution Overview
Problem
Large-scale cloud computing environments face performance issues due to server-side errors caused by anomalous requests, which are difficult to identify and manage effectively in real-time.
Innovation Solution
The system generates vectors encapsulating request text and performance metrics, using k-means clustering to classify requests into normal and abnormal clusters, allowing for early identification and handling of anomalous requests through text conversion via word embedding algorithms and normalization of combined vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional error handling is used in cloud computing environments, then system simplicity is maintained, but anomalous requests cannot be identified early enough, leading to performance degradation
Solution Approach 1:
The system performs preliminary clustering analysis on historical request data to establish normal and abnormal request patterns before actual requests are processed. This pre-computed clustering model enables early anomaly detection without adding complexity to the real-time request handling path.
Solution Approach 2:
A vector representation layer is introduced as an intermediary between raw request data and clustering analysis. Requests are converted to vectors that encapsulate both text semantics and performance metrics, enabling sophisticated anomaly detection while maintaining system modularity and manageable complexity.
2Measurement precision
If all request data is analyzed in detail, then detection precision is improved, but processing time increases
Solution Approach 1:
The system merges multiple data sources (request text, performance metrics, historical patterns) into a unified vector representation. This consolidation enables comprehensive anomaly detection by analyzing all relevant factors simultaneously rather than sequentially, reducing overall processing time while maintaining high detection precision.
Solution Approach 2:
The system transforms qualitative request attributes into quantitative vector parameters that can be efficiently processed by clustering algorithms. This parameter transformation enables rapid mathematical operations on request data compared to traditional text-based analysis methods.
3Reliability
If performance metrics are monitored for every request, then anomaly detection capability is improved, but system overhead increases
Solution Approach 1:
The system applies performance metric monitoring selectively based on request characteristics and cluster assignments. Rather than uniformly monitoring all requests, the system focuses detailed analysis on requests that exhibit anomalous patterns, reducing overall system overhead while maintaining reliable anomaly detection capability.
Data Source
AI summary
Requests are received for handling by a cloud computing environment which are then executed by the cloud computing environment. While each request is executing, performance metrics associated with the request are monitored. A vector is subsequently generated that encapsulates information associated with the request including the text within the request and the corresponding monitored performance metrics. Each request is then assigned (after it has been executed) to either a normal request cluster or an abnormal request cluster based on which cluster has a nearest mean relative to the corresponding vector. In addition, data can be provided that characterizes requests assigned to the abnormal request cluster. Related apparatus, systems, techniques and articles are also described.