Multi-Tenant Profiling Tool for Cluster-Wide Software Debugging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current software performance debugging tools are cumbersome and inefficient for on-demand systems, particularly in multi-tenant cloud computing environments, as they lack the ability to perform holistic sampling and profiling across all application servers in a cluster simultaneously.
Innovation Solution
The development of profiling tools that utilize configuration files to distribute sampling uniformly across a cluster of servers, allowing for selective profiling of threads based on user context and aggregating performance data to generate comprehensive call graphs and backtrace graphs, reducing sampling overhead and noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional debugging tools are used in multi-tenant environments, then individual server debugging is possible, but holistic profiling across all application servers cannot be performed
Solution Approach 1:
The debugging tool is designed to function across multiple servers simultaneously, providing universal profiling capability that works in multi-tenant cloud computing environments. The system can profile individual servers or aggregate data from all servers in the cluster, making it adaptable to different debugging needs without requiring separate tools for each scenario.
Solution Approach 2:
The system segments profiling data by tenant and server, allowing selective aggregation. Configuration files enable the tool to process and filter data from specific tenants or servers independently, then combine results as needed. This segmentation approach manages complexity by organizing multi-server data into manageable, identifiable units.
2Measurement precision
If sampling is performed across all servers simultaneously, then comprehensive performance data is obtained, but processing overhead increases
Solution Approach 1:
The system performs partial profiling by allowing users to select specific tenants, servers, or time periods for analysis. Configuration files enable selective sampling rather than mandatory full-cluster profiling, reducing processing overhead when comprehensive analysis is not required. The tool can profile exactly what is needed - no more, no less - optimizing the balance between data comprehensiveness and resource consumption.
Solution Approach 2:
The debugging tool uses periodic sampling intervals to collect performance data across servers. Instead of continuous monitoring, the system samples at configured intervals, reducing processing overhead while maintaining adequate measurement precision. Configuration files allow adjustment of sampling frequency to balance accuracy requirements against available processing resources.
3Quantity of substance
If traditional profiling methods are used, then individual server data is collected, but noise and redundancy increase
Solution Approach 1:
The system merges profiling data from multiple servers into aggregated views that eliminate redundancy. By combining data at the tenant level or cluster level, the tool presents consolidated performance information that removes duplicate entries and highlights meaningful patterns. Configuration files control the aggregation logic, allowing users to merge data by tenant, server group, or time period to reduce noise while preserving relevant performance signals.
Data Source
AI summary
A method for software debugging in a multi-tenant database network system is provided. When an exception occurs the stack frames in temporary working memory are dumped into long term memory for later analysis. A stack trace is performed from which performance information may be derived, which may aid in discovering the cause of the exception.