Proactive Infrastructure Resource Management for Application Clusters
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Solution Overview
Problem
Cloud computing faces challenges in efficiently scaling resources to match changing application demands, leading to resource waste or delay, as existing methods rely on reactive reallocation and fail to accurately predict future resource needs across multiple application clusters.
Innovation Solution
A system that collects real-time business and cluster metrics to predict future resource requirements, allowing for automatic, real-time adjustments in infrastructure resource allocations across multiple application clusters, using machine learning and historical data to correlate business process levels with resource needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reactive reallocation of resources is used, then resource allocation can respond to changed requirements, but it creates either waste (because too many resources are assigned) or undesirable delay (because a user must wait for needed resources)
Solution Approach 1:
The system performs preliminary actions by proactively predicting future resource requirements based on historical data and performance patterns before actual demand occurs. This allows resources to be pre-provisioned in advance, eliminating the delay associated with reactive reallocation while avoiding waste through accurate prediction-based allocation.
Solution Approach 2:
The system implements dynamic resource allocation by continuously monitoring performance metrics, updating predictions, and adjusting resource provisioning in real-time. This dynamic approach allows the system to adapt to changing requirements without the delays of static or purely reactive methods, optimizing the balance between responsiveness and efficiency.
2Measurement precision
If performance-metric time series extension is used for forecasting, then future resource requirements can be predicted, but it is an expensive calculation and does not well inform decisions to upgrade or downgrade a deployment
Solution Approach 1:
The system changes parameters by using multiple performance metrics simultaneously (CPU utilization, disk TO, memory usage, network traffic) rather than extending single time series. This multi-parameter approach provides more accurate forecasting information for upgrade or downgrade decisions while distributing the computational load across multiple data sources, reducing the expense of calculations.
Solution Approach 2:
The system creates simplified predictive models that capture essential patterns from historical performance data without requiring expensive full-scale simulations. These predictive models copy the behavior patterns of complex systems in a computationally efficient manner, enabling accurate forecasting at lower computational cost.
3Measurement precision
If application-specific performance patterns are used for prediction, then accurate predictions can be made for individual applications, but it becomes extremely difficult to predict how other applications will perform when deployed on different resource types
Solution Approach 1:
The system implements universal predictive models that can handle multiple applications and resource types simultaneously. By identifying common performance patterns and relationships across different applications, the system maintains prediction accuracy for individual applications while also being adaptable to predict performance of other applications on different resource types, eliminating the need for application-specific customization.
Data Source
AI summary
A system including: a processor; and a memory storing computer program code that controls the processor to: collect real-time business process metrics; collect real-time cluster metrics for a plurality of application clusters indicative of a required allotment of infrastructure resources for a given business process level; estimate a predicted future business process level; based on the estimated predicted future business process level and the real-time cluster metrics, predict a future infrastructure resource requirement of each of the plurality of application clusters; compare the predicted future infrastructure resource requirement of each of the plurality of application clusters to a current dedication of each of the plurality of application clusters; automatically adjust, in real-time and based on the comparison, respective allotments of infrastructure resources for each of the plurality of application clusters; receive an actual business process; and process the business process across the plurality of application clusters.