Distributed Statistical Tool for Networked Computer Platforms
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Solution Overview
Problem
Traditional tools for application profiling and measuring system performance are inadequate for complex distributed systems, which require mechanisms to profile applications and measure performance effectively.
Innovation Solution
A method involving statistical servers on nodes to maintain local application-related data, a centralized data manager, and an intermediary server to process requests for statistical data from nodes, enabling efficient data collection and reporting across a networked computer system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional tools are used for application profiling and measuring system performance, then the system structure remains simple, but the tools are inadequate for complex distributed systems
Solution Approach 1:
The monitoring system is segmented into multiple statistical servers distributed across different nodes, each maintaining local statistical data independently. This segmentation allows the system to handle complex distributed environments by dividing the monitoring function into manageable, distributed units rather than using a single centralized tool.
Solution Approach 2:
An intermediary server is introduced as a mediator between client applications and the distributed statistical servers. This intermediary processes incoming requests, gathers statistical data from relevant nodes, and returns comprehensive results, thereby enabling traditional client applications to access distributed system statistics without direct complexity.
2Loss of information
If statistical data is collected from multiple nodes, then comprehensive performance insights are obtained, but data collection and processing complexity increases
Solution Approach 1:
The intermediary server acts as a centralized coordination point that manages data collection from multiple statistical servers. It receives requests from clients, identifies which nodes are relevant to the requested application execution, gathers data from those specific nodes, and consolidates the results. This mediates the complexity of multi-node data collection while ensuring comprehensive data coverage.
Solution Approach 2:
Statistical servers on each node continuously maintain and update local statistical data in advance of any queries. This preliminary action ensures that when the intermediary server needs data, it is already available and current, eliminating the need for complex real-time data gathering mechanisms and reducing the complexity of comprehensive data collection.
3Ease of operation
If centralized statistical data is stored and processed, then comprehensive reporting is enabled, but the system requires more complex infrastructure
Solution Approach 1:
The intermediary server provides a simplified interface for clients to obtain comprehensive statistical reports without requiring them to directly interact with multiple distributed statistical servers. It handles the complexity of querying, gathering, and consolidating data from various nodes, thereby enabling ease of operation for reporting while managing the underlying infrastructure complexity centrally.
Data Source
AI summary
A statistic tool is provided for use in relation with a group of nodes in which each node has a statistic server configured to maintain application-related statistical data on local execution in the node. The statistic tool comprises a statistic manager for storing centralized statistical data obtained from the nodes and an intermediary server for processing a request from another computer system concerning the execution of an application by gathering statistical data received from those of the nodes that intervene in the execution of the application.