Resource Coordinate System for Data Center Bottleneck Diagnosis
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Solution Overview
Problem
In data centers, identifying the root cause of resource bottlenecks and performance issues is complicated due to abstraction layers of virtualization, containers, and application server instances, especially when processes compete for resources like CPU, network bandwidth, and memory, leading to slower execution or abortion of processes.
Innovation Solution
A coordinate system is implemented to assign unique identifiers to resources within a computing environment, allowing for the determination of resource locations, proximity, and constraints, enabling a central monitoring instance to analyze resource consumption and identify performance bottlenecks by comparing identifier stacks across processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If hardware utilization is increased to reduce costs, then resource efficiency is improved, but resource competition and process performance degradation worsen
Solution Approach 1:
The patent implements a monitoring system that collects resource consumption data from processes and provides feedback to identify bottlenecks. The system tracks resource usage metrics and uses this feedback to diagnose performance issues, enabling dynamic adjustment of resource allocation to maintain both efficiency and productivity.
Solution Approach 2:
The patent introduces an intermediary monitoring system that sits between the processes and the underlying hardware resources. This intermediary layer collects and analyzes resource consumption data, identifying bottlenecks without directly interfering with process execution, thus allowing high utilization while maintaining performance visibility.
2Adaptability or versatility
If virtualization layers are added to optimize hardware utilization, then resource allocation flexibility is improved, but identification of resource bottlenecks becomes more difficult
Solution Approach 1:
The patent segments the monitoring function across multiple levels of the virtualization stack. Instead of a single monolithic monitoring point, the system implements distributed monitoring agents at different virtualization layers (host, VM, container), each collecting local resource data. This segmentation maintains the flexibility of virtualization while making bottleneck identification manageable through localized measurements.
Solution Approach 2:
The patent implements a nested monitoring structure where monitoring agents are embedded at multiple hierarchical levels of the virtualization stack. Each nested layer monitors its own resource consumption and passes information upward, creating a comprehensive view that penetrates through multiple abstraction layers to identify the root cause of bottlenecks.
Data Source
AI summary
Embodiments allow identifying unique locations, proximity, and constraints of various individualization resource units in a data center. A plurality of processes may compete for available resources (e.g., CPU, network bandwidth, memory) in a shared environment, which may be virtualized (e.g., comprising host, VM, container, application server instance resource units). A scheduling infrastructure determines individualization unit locations according to an identifier reflecting resource hierarchies. For each process, the data sending infrastructure sends location data as an ID stack to a central monitoring instance for analysis. That central monitoring instance can reference the resource location information to identify process proximity and competition for resources constraining process performance. Resource location information in the form of coordinates provided by the ID stack, offer valuable information regarding resource consumption and other information relevant for process monitoring. Embodiments may be particularly useful in diagnosing bottlenecks arising from processes sharing resource units in virtualized environments.