Hybrid Cloud Resource Management via Wear-Based Optimization
Solution Overview
Problem
In hybrid cloud environments, organizations face challenges in efficiently managing and optimizing the use of both private and public cloud resources, particularly in balancing workload demands and minimizing costs, as private cloud resources are often underutilized while public resources are used only during spikes, leading to inefficient resource allocation and increased costs due to wear and tear on active components.
Innovation Solution
A method that monitors usage metrics in a hybrid computing environment to determine when to activate inactive components in the private cloud and deactivate active ones based on usage criteria, using a learning model to optimize resource allocation and shift workload to public clouds when costs are higher, thereby minimizing warranty charges and ensuring efficient resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If private cloud resources are kept active to handle workload demands, then system performance and availability are improved, but subscription costs and wear on components increase
Solution Approach 1:
The system dynamically adjusts the state of computing resources between active and inactive based on real-time workload monitoring. When workload demands are met by active resources, excess resources are deactivated to reduce costs. When additional capacity is needed, inactive resources are activated, creating a dynamic optimization loop that balances availability and cost.
Solution Approach 2:
The system implements continuous monitoring of workload demands and resource usage metrics, using this feedback to make intelligent decisions about resource activation and deactivation. The monitoring component tracks usage patterns and feeds this information back to the resource management system, which adjusts resource states accordingly to optimize the trade-off between availability and cost.
2Productivity
If more computing resources are activated to handle workload demands, then productivity is improved, but wear on active components increases leading to higher warranty costs
Solution Approach 1:
The system employs periodic monitoring of workload demands and usage metrics, activating or deactivating resources in periodic cycles based on observed patterns. This periodic action allows the system to anticipate workload fluctuations and adjust resource states proactively, reducing unnecessary wear from continuously running resources while ensuring capacity is available when needed.
Solution Approach 2:
The system changes the operational state parameter of computing resources between active and inactive based on workload requirements. By adjusting this binary parameter dynamically, the system optimizes the balance between productivity (when resources are active) and component wear (when resources are inactive), extending component lifespan while maintaining necessary capacity.
3Productivity
If inactive components are activated to reduce public cloud usage, then resource allocation efficiency is improved, but system complexity increases
Solution Approach 1:
The resource management system operates autonomously, automatically monitoring workload demands, evaluating resource states, and making activation/deactivation decisions without manual intervention. This self-service approach handles the complexity of resource orchestration internally, presenting a simplified interface to users while optimizing resource allocation efficiency through automated decision-making.
Solution Approach 2:
The system introduces a resource management intermediary layer that sits between the physical computing resources and the workload demands. This intermediary handles the complexity of coordinating between multiple inactive components, monitoring their states, and activating them when needed, thereby simplifying the overall system architecture while improving resource allocation efficiency.
Data Source
AI summary
A module optimizes active and inactive components and resources of a hybrid computing system to optimize a combination of private and public resources to minimize cost and maximize performance of the hybrid computing system. A learning model may analyze past usage and present usage metrics of one or more components with respect to performance criteria or cost criteria. Cost factors associated with components of the private system may be based on wear—the higher the wear the less desirable a component's use becomes due to lower reliability and higher warranty costs. When activating a component of the private system, a deactivation of a higher wear component may be delayed allowing time for a recently activated component to be integrated with the private system. A resource of a public system may be used while deactivation of a high wear component is delayed.