Cloud Resource Allocation Service Using End-User Workflow Metrics
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Solution Overview
Problem
In provider network environments, application vendors face challenges in determining optimal resource allocations for their applications due to varying end-user workloads, leading to potential under-provisioning or over-provisioning, which can result in unsatisfactory user experiences or excessive costs, as existing methods lack effective analysis of end-user workflow metrics.
Innovation Solution
Implementing a resource allocation service that collects and analyzes end-user workflow metrics to dynamically adjust resource allocations, using a resource allocation manager that correlates performance metrics with end-user workflow goals, identifies root causes of performance issues, and implements changes to ensure performance requirements are met, such as reallocating resources or consolidating application components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resource allocation is increased to meet varying end-user workloads, then user experience is improved, but costs increase due to over-provisioning
Solution Approach 1:
The system dynamically adjusts resource allocation based on real-time end-user workflow metrics rather than using static provisioning. The resource allocation manager continuously monitors performance metrics and automatically scales resources up or down to match actual workload demands, eliminating both over-provisioning and under-provisioning scenarios.
Solution Approach 2:
The system implements a feedback loop where end-user workflow metrics are collected, analyzed, and used to trigger resource allocation changes. The resource allocation manager receives performance data, evaluates whether thresholds are met, and automatically adjusts resources accordingly, creating a closed-loop control system that optimizes both user experience and costs.
2Loss of energy
If resource allocation is decreased to reduce costs, then costs are optimized, but user experience deteriorates due to under-provisioning
Solution Approach 1:
The system uses dynamic resource allocation that responds to actual workload conditions. When end-user workflow metrics indicate lower demand, the system automatically reduces resource allocation to optimize costs while maintaining sufficient capacity to meet user experience requirements. This prevents both over-provisioning and under-provisioning.
Solution Approach 2:
The feedback mechanism ensures that resource allocation is continuously optimized based on actual performance data. The system monitors end-user workflow metrics and only reduces resources when metrics confirm that lower allocation will not degrade user experience, thereby achieving cost optimization without compromising reliability.
3Device complexity
If static resource allocation is used, then device complexity is reduced, but adaptability to changing workloads deteriorates
Solution Approach 1:
The resource allocation manager implements self-service automation by automatically monitoring end-user workflow metrics, analyzing performance data, and adjusting resource allocation without manual intervention. The system evaluates metrics against thresholds and triggers resource changes autonomously, providing adaptability to changing workloads while keeping operational complexity low for users.
Solution Approach 2:
The system transitions from static to dynamic resource allocation by continuously adapting resource levels based on real-time workload conditions. The resource allocation manager automatically scales resources up or down in response to end-user workflow metrics, enabling the system to adapt to varying demands without requiring complex manual configuration or management.
Data Source
AI summary
At least one workflow comprising end-user interactions with an application implemented using provider network resources is identified by a resource allocation service of the provider network. The service collects performance metrics associated with the end-user workflow. If a performance metric meets a threshold criterion, a re-evaluation of the resources assigned to the application is initiated. Configuration changes to modify the set of provider network resources assigned to the application are implemented in accordance with a result of the resource re-evaluation.