Cloud Visibility via Reference Server Correlation Profiles
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Solution Overview
Problem
Conventional cloud visibility systems lack granular monitoring tools to provide deep insights into cloud-based environments due to heterogeneous software layers and multiple component and network configurations, resulting in coarse and uncorrelated measurements.
Innovation Solution
A reference server with hardware and software measurement components is configured to execute workloads and gather granular measurements, which are then used to generate correlation profiles indicating the effects of workload configurations on hardware and software performance, enabling more precise cloud visibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional cloud visibility systems are used, then system simplicity is maintained, but measurement precision and insight granularity are insufficient
Solution Approach 1:
The system segments cloud monitoring into multiple hierarchical levels: infrastructure layer (power, cooling), hardware layer (CPU, memory, storage), software layer (operating system, applications), and workload layer. Each layer has dedicated measurement tools and correlation profiles, allowing precise granular measurements without requiring a single complex monolithic system.
Solution Approach 2:
The patent introduces correlation profiles as intermediary data structures that link workload configurations to hardware and software measurements. These profiles act as mediators that translate raw measurements into meaningful insights about workload behavior, enabling precise measurement interpretation without directly complexifying the monitoring infrastructure.
2Loss of information
If heterogeneous software layers and multiple component configurations are monitored, then cloud visibility insight is improved, but system complexity increases
Solution Approach 1:
The system employs universal correlation profiles that can accommodate multiple software layers (hypervisors, operating systems, applications) and various component configurations (CPU types, memory configurations, storage setups) through a unified framework. This universal approach enables comprehensive cloud visibility across heterogeneous environments without requiring separate monitoring systems for each configuration type.
Solution Approach 2:
The patent utilizes parameter changes in workload configurations as the basis for creating correlation profiles. By measuring how changes in workload parameters (CPU allocation, memory allocation, storage I/O patterns) affect hardware and software measurements, the system builds adaptive correlation models that handle heterogeneity without increasing structural complexity.
3Measurement precision
If centralized granular monitoring tools are implemented, then measurement precision is improved, but ease of operation and deployment difficulty increase
Solution Approach 1:
The system performs preliminary actions by pre-generating correlation profiles that capture the relationships between workload configurations and measurements before actual cloud operations begin. These pre-established profiles enable automated, precise measurement interpretation during runtime without requiring manual configuration or complex real-time analysis, significantly improving ease of operation.
Data Source
AI summary
Methods, systems, and computer readable media for providing cloud visibility are disclosed. According to one method, the method includes obtaining at least one correlation profile for indicating effects of workload configurations on hardware and/or software measurements of a reference server, wherein the reference server includes components for measuring hardware and/or software performance. The method also includes configuring the reference server, wherein the reference server is one of a plurality of servers in a cloud-based environment for executing a workload, wherein each of the plurality of servers executes at least a portion of the workload. The method further includes receiving, from the reference server, measurements associated with executing at least the portion of the workload. The method also includes generating information regarding the cloud-based environment using the measurements from the reference server and the at least one correlation profile.