Auto-Selecting Virtualized Applications by Resource Capacity
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Solution Overview
Problem
Enterprise IT systems face challenges in optimizing resource utilization and user experience due to the lack of integrated solutions for capacity planning and proactive maintenance, often requiring separate servers and local support, which can be resource-intensive and inefficient.
Innovation Solution
An appliance marketplace system that uses aggregate data from multiple customers to determine resource requirements, offering targeted service offerings, proactive capacity planning, and auto-selection of applications based on resource capacity, with the option to migrate workload to the cloud for enhanced scalability and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate servers are deployed for each IT function, then reliability and functionality are improved, but device complexity and resource consumption increase
Solution Approach 1:
The patent combines multiple IT functions (compute, storage, network, management) into a single hyper-converged appliance that uses virtualization technology to provide all capabilities in one integrated system, eliminating the need for separate physical servers for each function
Solution Approach 2:
The hyper-converged appliance is designed as a universal platform that can perform multiple IT functions simultaneously through virtualization, allowing a single device to replace several specialized servers while maintaining all required functionalities
2Ease of operation
If local support and separate servers are used, then system functionality is maintained, but ease of operation and maintenance efficiency deteriorate
Solution Approach 1:
The system continuously monitors resource capacity and application performance, automatically collecting usage data and providing feedback to the application selection process, enabling proactive capacity planning and simplified maintenance through data-driven decisions
Solution Approach 2:
The system performs self-analysis of resource capacity and automatically selects appropriate applications based on monitored metrics, reducing the need for manual intervention and local support while improving maintenance efficiency
3Reliability
If resource capacity is over-provisioned, then application support reliability is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The system dynamically adjusts resource allocation based on actual usage patterns and performance metrics, transitioning from static over-provisioning to adaptive resource management that optimizes both reliability and utilization efficiency in real-time
Solution Approach 2:
The system changes resource allocation parameters based on monitored capacity utilization and application requirements, adjusting compute, storage, and network resources dynamically to match actual demand while maintaining reliable application support
Data Source
AI summary
Usage and performance data from a plurality of installed appliances is received via a network, a different corresponding subset of said appliances being associated with each of a plurality of customers. A set of one or more appliance resources required to support a virtualized application workload is determined based at least in part on usage and performance data from the plurality of installed appliances. An amount of unutilized capacity available with respect to said one or more appliance resources is determined based at least in part on appliance usage and performance data received from a given customer. A determination is made, based at least in part on said amount of unutilized capacity available with respect to said one or more appliance resources for the given customer and said set of one or more appliance resources required to support said virtualized application workload, as to whether the given customer's currently available resources are sufficient to support said virtualized application workload.