Centralized Logging Server for Cloud Scalability
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Solution Overview
Problem
Current cloud computing platforms face scalability challenges due to the lack of centralized logging tools, which hinder effective monitoring and management of health, security, and compliance, especially in environments with numerous accounts or log groups, leading to difficulties in realizing the benefits of on-demand resource allocation and dynamic flexibility.
Innovation Solution
A centralized logging system that consolidates data streams into a single stream, utilizes data partitioning, and applies transformation functions to decompress and re-compress logging data, appending identifiers for routing to specific object storage containers, thereby reducing latency and improving data management and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single destination, data stream, and delivery stream is required for each individual log group, then logging coverage is complete, but scalability deteriorates significantly when enterprises utilize significant numbers of accounts or log groups
Solution Approach 1:
The patent merges multiple individual log group destinations, data streams, and delivery streams into a single centralized logging infrastructure. Instead of maintaining separate logging resources for each log group, the system consolidates them into one unified destination that can handle logs from multiple accounts and log groups, thereby improving scalability while maintaining complete logging coverage.
Solution Approach 2:
The centralized logging destination is designed to serve multiple functions simultaneously - it can receive, process, and store logs from numerous different accounts and log groups through a single interface. This multi-functional approach eliminates the need for separate dedicated resources for each log group, resolving the contradiction between complete coverage and scalability.
2Reliability
If centralized logging covers hundreds or thousands of entities, then monitoring coverage is comprehensive, but effective management of such entities becomes challenging or impossible
Solution Approach 1:
The patent segments the management of numerous logging entities by introducing a hierarchical structure with centralized logging agents that operate autonomously at distributed locations. Each agent manages local log collection and preprocessing, while a central server handles aggregation and analysis. This segmentation reduces the management burden on centralized systems and makes it feasible to handle hundreds or thousands of entities.
Solution Approach 2:
The logging agents are designed to autonomously perform log collection, filtering, and preprocessing tasks without requiring manual intervention for each entity. This self-service capability allows the system to scale to thousands of entities while maintaining ease of operation, as the agents automatically adapt to their local environments and manage their own log streams.
3Ease of manufacture
If known cloud computing platform logging services are used, then logging implementation is straightforward, but significant scalability challenges arise for enterprises with significant numbers of accounts or log groups
Solution Approach 1:
The patent introduces centralized logging agents as intermediary components between the cloud computing platforms and the final logging destination. These agents simplify implementation by providing a standardized interface for log collection and preprocessing, while their distributed architecture enables the system to scale to enterprises with numerous accounts and log groups, overcoming the scalability limitations of direct logging services.
Data Source
AI summary
A logging management server is provided for enhanced centralized monitoring of cloud computing platforms. The processor is configured to receive logging data sub-streams from the cloud computing platform. Each of the logging data sub-streams includes compressed logging data. The processor is also configured to apply a transformation function to each of the logging data sub-streams to obtain a transformed centralized logging data stream. The processor is further configured to transmit the transformed centralized logging data stream to write to a centralized object storage container. The processor is also configured to decompress a portion of the compressed logging data of the centralized logging data stream. The processor is further configured to identify the appended account identifier and the appended log group associated with the decompressed portion of logging data. The processor is also configured to route the decompressed portion of logging data to a sorted object storage container.