Cloud Virtual Resource Grouping and Visualization
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Solution Overview
Problem
In cloud environments, managing virtual resources is challenging due to the dynamic nature of physical servers and the complexity of characterizing performance, which is exacerbated by the exponential increase in available information as the number of virtual resources grows, making it difficult for users to make informed decisions about optimizing system performance.
Innovation Solution
The development of methods and apparatus that enable the grouping of entities, such as nodes, in a cloud environment, allowing users to manually or automatically group nodes, visualize relationships, and view performance characteristics, using a user interface that includes features like flows and heatmaps, and employs a machine learning algorithm to recommend optimal node groupings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of virtual resources increases to provide more cloud services, then the cloud infrastructure capacity and service availability are improved, but the amount of performance characterization information increases exponentially making it difficult for users to make informed decisions
Solution Approach 1:
The patent segments the exponential amount of performance characterization information into hierarchical groups and categories. Virtual resources are organized into logical groupings (e.g., by function, department, or project), allowing users to navigate and analyze performance data at multiple levels of abstraction rather than being overwhelmed by individual resource details.
Solution Approach 2:
The patent introduces an intermediary layer between the raw performance data and the user interface. This intermediary processes and filters performance characterization information, presenting only the most relevant metrics to users based on their roles and needs, thereby reducing information complexity while maintaining productivity.
2Measurement precision
If detailed performance characterization information is presented to help users make optimization decisions, then the quality of decision-making is improved, but the user interface becomes overwhelming and difficult to navigate
Solution Approach 1:
The patent applies local quality by providing different levels of information detail to different users or different contexts. The interface adapts to show comprehensive performance data when needed while maintaining simplicity in normal operation modes, ensuring both measurement precision and ease of operation are satisfied in their respective contexts.
Solution Approach 2:
The patent implements a dynamic user interface that adapts the level of performance information displayed based on user interactions, roles, and current system state. Users can dynamically adjust the depth of performance characterization shown, allowing the interface to transition between simplified views and detailed analysis modes as needed.
3Extent of automation
If automated grouping algorithms are used to organize virtual resources, then the time and effort required for manual management is reduced, but the adaptability to user-specific preferences and contexts may be limited
Solution Approach 1:
The patent incorporates feedback mechanisms where automated grouping algorithms initially organize virtual resources, but user responses and adjustments are fed back into the system to refine future automated groupings. This allows the automation to learn and adapt to user-specific preferences and contexts over time, balancing automation efficiency with user adaptability.
Solution Approach 2:
The patent designs the automated grouping system with multi-functionality, enabling it to serve multiple purposes: initial resource organization, performance analysis grouping, and user-preference-based categorization. This universal approach allows the same automated system to adapt to different user needs and contexts without requiring completely separate manual processes.
Data Source
AI summary
Disclosed are methods and apparatus for implementing in an electronic device that includes a processor and memory. Virtual resources, which are associated with an execution of a user's applications in a cloud resource configuration including virtual machines, network services and storage, are identified. A first topology map of the virtual resources, including a plurality of nodes, is generated. The first topology map, including the nodes, is output. A vector, which is associated with each node, said vector including one or more features associated with each node, is generated. Based upon the vectors, a distribution of the plurality of nodes within two or more groups is determined. A second topology map, including each of the node groups in one of a collapsed format, wherein only a identifier of the node group is output or an expanded format, wherein a portion of the plurality of nodes the node group are output, is output.