Cloud Service Scaling Rule Based on Resource Utilization and Budget
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Solution Overview
Problem
Cloud service providers face challenges in maintaining optimal resource allocation for services on cloud networks, leading to unnecessary scaling costs due to inefficient resource utilization and quality of service fluctuations, which can result in excessive resource provisioning during non-peak times.
Innovation Solution
Implementing a scale rule-based system that monitors resource utilization, quality of service, and workload volume to dynamically adjust cloud resources, ensuring that scaling actions are taken only when specific conditions are met, including resource utilization, quality, workload, and budget thresholds, with a duration rule to prevent false positives and negatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud resources are scaled up to handle peak demand, then service quality and reliability are improved, but resource waste and cost increase during non-peak times
Solution Approach 1:
The patent implements dynamic resource scaling where cloud resources are automatically adjusted based on real-time demand patterns. The system transitions from static resource allocation to dynamic provisioning, enabling the cloud infrastructure to expand during peak demand and contract during non-peak times, thus resolving the contradiction between maintaining service quality and avoiding resource waste.
Solution Approach 2:
The patent employs feedback mechanisms where resource utilization metrics, service quality parameters, and demand patterns are continuously monitored and fed back into the scaling decision process. This feedback loop enables intelligent adjustment of resource allocation, allowing the system to distinguish between temporary spikes and sustained demand, thereby preventing resource waste while maintaining reliability.
2Reliability
If resource provisioning is increased to prevent service degradation, then quality of service is improved, but unnecessary scaling costs are incurred
Solution Approach 1:
The patent changes the parameters of resource allocation from fixed, over-provisioned defaults to dynamic, demand-driven values. By monitoring service quality metrics and demand patterns, the system adjusts resource provisioning parameters in real-time, ensuring that resources are allocated only when and where needed, thus preventing unnecessary scaling costs while maintaining quality of service.
Solution Approach 2:
The patent performs preliminary analysis of demand patterns and service quality thresholds before implementing scaling actions. By predicting future demand based on historical data and pre-establishing scaling policies, the system avoids reactive over-provisioning and implements only necessary resource allocation, reducing unnecessary scaling costs.
3Measurement precision
If continuous monitoring of service metrics is implemented, then accurate scaling decisions are improved, but system complexity and overhead increase
Solution Approach 1:
The patent extracts and focuses monitoring on the most critical service quality metrics and demand patterns rather than continuously monitoring all possible parameters. By identifying and concentrating on key metrics such as request rates, response times, and resource utilization thresholds, the system achieves accurate scaling decisions with reduced monitoring overhead and lower system complexity.
Solution Approach 2:
The patent applies partial monitoring by selectively observing only the essential metrics needed for scaling decisions rather than comprehensive monitoring of all system parameters. This partial action approach maintains sufficient measurement precision for accurate scaling while significantly reducing the complexity and overhead associated with continuous comprehensive monitoring.
4Speed
If aggressive scaling thresholds are used to respond quickly to demand changes, then service responsiveness is improved, but false scaling decisions and operational instability increase
Solution Approach 1:
The patent implements beforehand cushioning by establishing buffer zones and hysteresis thresholds in the scaling decision process. Instead of triggering scaling at every minor demand fluctuation, the system requires demand to exceed thresholds by a certain margin or sustain elevated levels for a minimum duration, thereby cushioning against false scaling decisions while maintaining quick response to genuine demand changes and preserving operational stability.
Data Source
AI summary
Maintaining a service on a cloud network may include receiving a set of status data associated with the service and performing a scale action on the cloud network based on a scale rule applied to the set of status data. The set of status data may be related to a set of resources utilized by the service, a performance level of the service, and a workload volume of the service. The scale rule may include a utilization condition, a quality condition, a workload condition, and a budget condition.