Predictive Auto-Scaling for QoS-Aware Cloud Service Workloads
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Solution Overview
Problem
Existing auto-scaling policies in cloud computing systems rely on real-time state metrics, leading to inefficiencies such as delayed instance launches, unnecessary resource consumption, and increased network latency due to independent scaling of services, which do not accurately reflect service-level agreements (SLA) and fail to adapt to predicted workloads.
Innovation Solution
Implement a system that predicts workload using hierarchical time-series forecasting and queuing models to dynamically adjust computing resource instances based on quality of service (QoS) metrics, utilizing regression models like Histogram-Based Gradient Boosting Regression Tree (HGBR) and search algorithms to optimize instance numbers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If real-time state metrics are used for auto-scaling, then the system can respond to current workload conditions, but the scaling decisions are delayed and do not account for predicted future workloads
Solution Approach 1:
The system performs preliminary actions by predicting future workload using time-series forecasting models before the actual workload arrives. This allows the auto-scaling system to proactively adjust computing resource instances in advance, eliminating scaling delays and ensuring resources are ready when needed.
Solution Approach 2:
The system implements feedback by continuously monitoring real-time state metrics (CPU utilization, queue depth) and comparing them with predicted workload values. This feedback loop enables the system to refine scaling decisions by combining historical patterns with current conditions, improving both responsiveness and accuracy.
2Productivity
If computing resource instances are scaled independently based on real-time metrics, then each service can be managed separately, but unnecessary resource consumption occurs and network latency increases
Solution Approach 1:
The system merges independent service scaling decisions into a coordinated multi-service scaling strategy. By analyzing workload patterns across multiple services and their interdependencies, the system consolidates scaling actions, reducing redundant resource provisioning and minimizing network latency between services.
Solution Approach 2:
The system changes scaling parameters by transitioning from threshold-based reactive scaling to prediction-based proactive scaling. Using time-series forecasting, the system adjusts scaling parameters (number of instances) based on predicted workload trends, optimizing resource consumption while maintaining service productivity.
3Reliability
If the number of computing resource instances is increased to meet QoS requirements, then service level agreements are complied with, but resource costs increase
Solution Approach 1:
The system performs preliminary scaling actions based on predicted workload peaks before they occur. By using forecasting models to anticipate increased demand, the system provisions additional computing resource instances in advance, ensuring QoS compliance during high-demand periods without permanently maintaining excess resources that would increase ongoing costs.
Solution Approach 2:
The system implements dynamic scaling by continuously adjusting the number of computing resource instances based on real-time workload conditions and predictions. This dynamic approach allows the system to maintain QoS compliance during peak periods while reducing resource allocation during low-demand periods, optimizing the balance between reliability and resource quantity.
Data Source
AI summary
Methods and systems are provided for auto-scaling computing resource instances of a service for a predicted workload. In embodiments described herein, a predicted incoming data processing workload for a service of a cloud computing system is determined based on incoming data traffic. A predicted quality of service (QOS) metric of the service that is below a QoS metric threshold is determined based on the predicted incoming data processing workload and a number of computing resource instances of a current configuration of the service. A new number of computing resource instances of the service where a corresponding predicted QoS metric is above the QoS metric threshold is determined based on applying the predicted incoming data processing workload and the QoS metric threshold to a search algorithm. The service is auto-scaled based on the new number of computing resource instances.