Hierarchical Resource Allocation System for Cloud Infrastructure
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Solution Overview
Problem
Cloud infrastructure systems face inefficiencies and scaling difficulties due to unpredictable resource needs, leading to overprovisioning and uneven utilization of resources, as existing systems lack proactive monitoring and management capabilities to optimize resource allocation across different levels of service providers.
Innovation Solution
A resource allocation system that uses a hierarchical infrastructure model, comprising a resource map, index processor, and allocation manager to optimize resource allocation by calculating optimization indexes based on performance metrics and reallocating computing tasks to service providers that maximize resource utilization, creating new providers if necessary, and reorganizing workloads to achieve efficient use of resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hardware resources are provisioned based on initial requests from application developers, then service availability is ensured, but resource utilization efficiency deteriorates due to overprovisioning and inflated requests
Solution Approach 1:
The system continuously monitors actual resource consumption at the application layer and feeds this information back to the platform layer. This feedback loop enables dynamic adjustment of resource allocation, allowing the system to maintain service availability while eliminating overprovisioning by allocating resources based on actual usage patterns rather than inflated initial requests
Solution Approach 2:
The patent implements dynamic resource allocation where the platform layer can add tenants to existing platform services when surplus capacity is detected. This dynamic adjustment allows the system to adapt to changing workload demands in real-time, improving resource utilization while maintaining service availability through flexible reconfiguration rather than static overprovisioning
2Loss of energy
If the platform level adds tenants to existing services, then resource utilization improves, but system complexity increases due to dynamic reconfiguration requirements
Solution Approach 1:
The patent creates a universal platform service layer that can serve multiple application tenants with different workloads. This multi-functional platform layer can dynamically allocate resources to different tenants based on current demands, improving overall resource utilization while managing complexity through a standardized interface and resource pool that serves multiple purposes
3Measurement precision
If monitoring systems track utilization and consumption, then visibility is improved, but proactive optimization capability deteriorates due to lack of actionable information
Solution Approach 1:
The monitoring system not only tracks utilization and consumption metrics but also feeds this information back to the platform layer for automated decision-making. This feedback mechanism transforms raw monitoring data into actionable intelligence, enabling the system to proactively optimize resource allocation by adding tenants to services with surplus capacity or scaling resources based on actual consumption patterns
Solution Approach 2:
The system implements self-service optimization where the platform layer automatically uses monitoring information to make allocation decisions without external intervention. The platform monitors its own capacity and consumption, then autonomously adds tenants to services with surplus capacity or provisions new services when needed, converting monitoring visibility into automated proactive optimization
Data Source
AI summary
Embodiments include a resource allocation system for managing execution of a computing task by a hierarchically-arranged computing infrastructure. In embodiments, the resource allocation system can comprise a resource map, an index processor, and an allocation manager. The resource map can include data elements that are associated with each service provider, including parent-child relationships. Workloads can be assigned to providers based on one or more optimization indexes calculated for each service provider based on a plurality of level-specific performance metrics received from one or more monitoring engines.