Server Group Manager for Location-Based Health Monitoring
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Solution Overview
Problem
Information handling systems, particularly server groups, lack resources to provide location-based information and make location-dependent determinations, leading to challenges in identifying and managing health parameters effectively.
Innovation Solution
The system receives health status data and location data from group members, generates a location-status display by comparing distance-proxy data, and uses this information to predict status changes, enabling proactive or corrective management actions through a decision tree methodology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If servers lack location-based resources, then device complexity is reduced, but the ability to identify and manage location-dependent health parameters deteriorates
Solution Approach 1:
The patent introduces a group manager as an intermediary component that collects, processes, and analyzes location data and health status data from multiple servers. This mediator enables location-based health parameter identification without requiring each individual server to have complex location-based resources, thus resolving the contradiction between device complexity and information capability
Solution Approach 2:
The group manager serves multiple functions: it acts as a centralization point for location data collection, health status monitoring, predictive analytics, and management operations. By making the group manager multi-functional, the system achieves comprehensive location-based health management without adding complexity to individual servers
2Measurement precision
If comprehensive health status data and location data are collected, then measurement precision of health parameters is improved, but device complexity increases
Solution Approach 1:
The patent segments the health monitoring system into distinct components: servers that generate health status data, location services that provide location data, and a group manager that processes and analyzes the data. This segmentation allows each component to focus on specific tasks, improving measurement precision while managing complexity through modular architecture
Solution Approach 2:
The group manager acts as an intermediary that receives and processes both health status data and location data from multiple sources. It performs predictive analytics and generates management operations without requiring complex data collection infrastructure at each server, thus improving measurement precision while controlling system complexity
3Measurement precision
If location-status display is generated by comparing distance-proxy data, then measurement precision of relative distances is improved, but loss of time for data processing increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing distance-proxy data from multiple member-pairs in advance. When location-status display is needed, the pre-collected data can be quickly retrieved and processed, reducing the time required for distance determination while maintaining high measurement precision through comprehensive data comparison
Data Source
AI summary
A method of managing a server group comprising a plurality of group members in a server group may include receiving, from a group member, health status data and obtaining location data. The health status data may indicate a group member's status with respect to a health parameter. The set of health parameter states may include a compliant, borderline, and non-compliant state defined by one or more thresholds. The location information may indicate locations of the group members relative to one another. A status-location operation may be performed in accordance with the health status and location data to generate a display including, for each of the group members, a data point indicating a status for a particular health parameter and a location of the applicable group member relative to other group members. Historical status change data may be maintained and used to predict a next status change expected.