Dynamic Cloud Workload Scaling via Multi-Configuration Analysis
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Solution Overview
Problem
Cloud computing environments face challenges in dynamically scaling workloads to optimize resource allocation and performance, as existing methods often rely on consistent scaling strategies that do not account for varying resource needs of different application components.
Innovation Solution
An approach that analyzes workload performance using multiple resource configurations, comparing test results to select the optimal configuration for scaling up or out, and dynamically adjusts resource allocation across virtual machines to create an optimized workload scaling profile.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If consistent scaling strategies are used for all application components, then implementation simplicity is maintained, but resource allocation optimization deteriorates
Solution Approach 1:
The patent applies local quality by transitioning from uniform scaling strategies to component-specific scaling approaches. Each application component (database, cache, web server, etc.) is analyzed individually and assigned tailored scaling strategies based on its unique resource requirements and performance characteristics, thereby optimizing resource allocation while maintaining manageable complexity through systematic analysis
Solution Approach 2:
The patent implements dynamics by introducing adaptive scaling that responds to actual workload conditions. The system continuously monitors application performance metrics and dynamically adjusts resource allocation in real-time, allowing scaling strategies to evolve from static consistent approaches to dynamic optimized configurations based on current system state
2Productivity
If multiple resource configurations are tested, then optimal performance is achieved, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by conducting comprehensive testing of multiple resource configurations in advance during the scaling decision process. The system evaluates different scaling options (scale up, scale out, scale in) with various resource allocations before implementing changes, allowing optimal performance to be determined through pre-analysis rather than trial-and-error in production
Solution Approach 2:
The patent implements self-service by creating an automated scaling decision system that independently analyzes workload requirements, tests configurations, and determines optimal scaling strategies without manual intervention. The system uses machine learning models and performance metrics to self-determine the best resource allocation, reducing the complexity burden on operators while achieving optimal performance
Data Source
AI summary
An approach is provided for an information handling system to scale a workload being executed in a cloud computing environment. In the approach, performance of the workload is analyzed by using more than one resource configuration. The resource configurations use different resources that are available in the cloud computing environment. The analysis of the workload results in test results. At least one of the resource configurations is directed to a scaling up of resources that alters resources assigned to a first virtual machine (VM) running the workload. Another resource configuration is directed to a scaling out of resources that adds one or more VMs to a second VM creating a number of VMs running the workload. The test results are compared and an optimal test result is selected. A workload scaling profile is optimized according to the selected optimal test result.