Logmon Zone Management for Cloud Data Logging Optimization
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Solution Overview
Problem
Current data logging and monitoring in cloud-based networked computing environments are resource-intensive, requiring improvements to efficiently utilize storage and network bandwidth while effectively monitoring application behavior.
Innovation Solution
The implementation of logmon zones with customizable policies for storage limits, priority, periodicity, retention time, and other parameters allows tenants to manage and bill data logging and monitoring based on specific application needs, enabling flexible and efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data logging and monitoring are implemented in cloud-based networked computing environments, then system integrity and application functionality can be verified, but resource consumption (storage and network bandwidth) increases
Solution Approach 1:
The patent segments the cloud environment into multiple logmon zones based on tenant requirements, application types, and data sensitivity. Each zone has customized logging policies that control what data is collected, how it is stored, and how it is monitored. This segmentation allows critical systems to receive intensive logging while less critical systems use minimal logging, resolving the contradiction between verification reliability and resource consumption.
Solution Approach 2:
The patent implements local quality by applying different logging and monitoring policies to different zones, tenants, and applications based on their specific needs. Each logmon zone has customized parameters including storage limits, priority levels, periodicity, and retention times. This localized approach ensures that resources are allocated efficiently to where they are most needed while minimizing waste in less critical areas.
2Loss of information
If comprehensive data logging is performed to track all interactions, then audit trails can be established and suspicious activities identified, but storage requirements and network bandwidth usage increase
Solution Approach 1:
The patent changes parameters such as retention time, storage limits, periodicity, and data sampling frequency based on tenant preferences and application requirements. For example, critical security events may be logged with long retention times while routine operations use shorter retention. This parameter customization allows complete audit trails for important data while reducing storage requirements for less critical information.
Solution Approach 2:
The patent applies partial action by selectively logging only the necessary interactions for each application and tenant rather than comprehensively logging all system activities. The logging scope is adjusted to match the actual monitoring needs, avoiding excessive data collection that would unnecessarily consume storage resources while still maintaining complete audit trails for relevant operations.
3Reliability
If logging and monitoring resources are allocated to all cloud applications, then effective monitoring is achieved, but resource allocation efficiency decreases
Solution Approach 1:
The patent implements dynamic resource allocation where logging and monitoring resources are automatically adjusted based on changing tenant needs, application performance, and system conditions. The system can dynamically modify logging levels, zone assignments, and resource allocation without manual intervention. This dynamic approach ensures effective monitoring is maintained while optimizing resource utilization efficiency as conditions change.
Solution Approach 2:
The patent creates a universal logmon zone management system that serves multiple tenants and applications with diverse requirements through a single standardized framework. The system handles different logging needs, security requirements, and monitoring preferences of various tenants while maintaining efficient resource allocation. This multi-functional approach allows one system to effectively monitor all applications without requiring separate resource allocations for each.
Data Source
AI summary
Embodiments of the present invention provide systems and methods for organization of data logging in a networked computing environment. A plurality of logging and monitoring zones, referred to as “logmon” zones are defined. Each zone is associated with one or more policies. The policies specify various parameters such as storage limits, priority, periodicity, and retention time, among others. A networked application operating in a cloud (networked) environment is associated with a zone. The tenant for the application can be billed according to the zone.