Session Analyzer for HTTP Workload Characterization
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Solution Overview
Problem
In complex web server environments, predicting and managing HTTP session workloads to ensure adequate resources and maintain server availability is challenging due to fluid client-server interactions and evolving usage patterns, leading to potential resource inefficiencies and functionality gaps.
Innovation Solution
A method involving a session analyzer that extracts data from a database of redundant session-persistent data to characterize HTTP session workloads, using data mining and intelligent inferencing to provide insights for resource planning and tuning, while maintaining session redundancy and server availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If session-persistent data is maintained in a database for redundancy, then server availability and reliability are improved, but device complexity and data management overhead increase
Solution Approach 1:
A session analyzer component is introduced as an intermediary between the database and the server systems. This analyzer extracts and characterizes workload information from session data, enabling centralized monitoring and analysis without requiring each server to independently manage complex session state tracking, thus reducing overall system complexity while maintaining reliability
Solution Approach 2:
The patent extracts workload characterization information from the session-persistent data stored in the database. By separating the analysis function from the storage function, the system can maintain redundant session data for reliability while deriving workload insights without increasing the complexity of data management operations
2Measurement precision
If data mining and analysis are performed on session data, then resource planning accuracy is improved, but processing time and computational overhead increase
Solution Approach 1:
The session analyzer performs preliminary characterization of workload patterns by extracting key metrics from session data stored in the database. By analyzing historical session information in advance, the system prepares workload profiles that can be quickly referenced for resource planning decisions, reducing the need for time-consuming real-time analysis
Solution Approach 2:
The patent applies partial action by selecting and analyzing only the most relevant session data characteristics needed for workload characterization. Rather than processing all session data comprehensively, the system focuses on key metrics such as session duration, request patterns, and resource consumption, achieving sufficient accuracy while minimizing processing time
3Productivity
If multiple server systems are used to handle HTTP sessions, then system capacity and redundancy are improved, but coordination complexity and communication overhead increase
Solution Approach 1:
Multiple server systems are merged into a unified cluster that shares a common database for session-persistent data. This consolidation allows servers to work independently while automatically sharing session state information, increasing overall processing capacity without proportionally increasing coordination complexity, as the database serves as a centralized coordination point
Data Source
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AI summary
A web server comprising multiple computer systems includes at least one system which maintains a database of redundant session-persistent data on behalf of other systems which process HTTP session workloads for clients. A session analyzer extracts (707,711) information from this redundancy database to characterize HTTP session workloads. Preferably, session workloads are processed by middleware comprising an application server and one or more applications. The application server maintains session-persistent data as data objects, and automatically updates the database of redundant data which responses are transmitted to a clients. The session analyzer independently extracts data from the redundant database to characterize the session workloads. Optionally, the analyzer includes an intelligent inferencing function (716) which can make tuning recommendations (717) from the extracted data.