Thread Diaphragm Resource Model for Dynamic Cloud Pool Sizing
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Solution Overview
Problem
In cloud computing environments, resource pools often become exhausted due to static allocation methods, leading to queuing of requests and inefficient resource utilization, as existing technologies fail to dynamically adjust to changing demand and avoid allocation of unused resources.
Innovation Solution
A computer-implemented method and system that analyzes log events and telemetry data to derive a model of resource request frequency and service times, generating an access matrix to arbitrate resource access and a density distribution for dynamic resizing of resource pools to their optimal size, ensuring real-time access control and efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static allocation methods are used for resource pools, then resource pool size is fixed and simple to manage, but resource exhaustion occurs and requests are queued leading to inefficient resource utilization
Solution Approach 1:
The patent implements dynamic resource pool sizing by continuously monitoring resource request patterns and adjusting the pool size accordingly. The system transitions from static allocation to dynamic allocation where the resource pool size adapts to changing workload demands, preventing both exhaustion and over-provisioning.
Solution Approach 2:
The system employs feedback mechanisms by analyzing log events and telemetry data to monitor resource pool activities. This feedback loop enables the system to detect resource exhaustion patterns and adjust pool size in response to actual usage patterns, improving utilization efficiency while maintaining manageable complexity through automated control.
2Productivity
If dynamic resizing of resource pools is implemented, then resource utilization efficiency improves, but system complexity increases due to modeling and access arbitration requirements
Solution Approach 1:
The system implements self-service by enabling resource pools to automatically adjust their own size based on monitored usage patterns. The resource pool management system autonomously analyzes its own activities and makes resizing decisions without requiring complex external control mechanisms, thereby improving efficiency while limiting complexity growth.
Solution Approach 2:
The patent uses probability distributions and access matrices as simplified representations (copies) of complex resource request patterns. Instead of managing full complexity of individual request dynamics, the system works with statistical models that capture essential patterns, reducing the complexity of access control while maintaining effective resource allocation.
3Productivity
If access arbitration is implemented to control resource access, then resource allocation efficiency improves, but processing overhead increases due to model analysis and matrix generation
Solution Approach 1:
The system performs preliminary analysis by pre-processing log events and telemetry data to build probability distributions and access matrices before they are needed for resource allocation decisions. This advance preparation reduces the processing overhead during actual resource requests, as the arbitration system can directly query pre-computed models rather than analyzing raw data in real-time.
Solution Approach 2:
The patent replaces direct mechanical analysis of resource requests with probabilistic models and access matrices. Instead of evaluating complex request patterns on-the-fly, the system substitutes these with pre-computed probability distributions that capture resource usage patterns, significantly reducing processing time while maintaining allocation efficiency.
Data Source
AI summary
Aspects of the present disclosure relate generally to generating a model of resource pool activities for resource pools in a computing environment and, more particularly, to systems, computer program products, and methods of real-time access control and dynamic sizing of resource pools in cloud computing environments. For example, a computer-implemented method includes: analyzing, by a processor set, log events of activities of a resource pool of computing resources; deriving, by the processor set, a model of frequency of resource requests for the resource pool from the activities of the resource pool; generating, by the processor set, an access matrix as output of the model that arbitrates access to resources of the resource pool among new requests for a resource from the resource pool; and arbitrating, by the processor set, the access to the resources of the resource pool among the new requests for a resource from the resource pool.