Cloud Infrastructure Scaling via Kalman Filter Parameter Learning
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Solution Overview
Problem
Cloud computing users face challenges in dynamically resizing their deployments to meet changing workload demands due to lack of control and visibility into user-space applications, requiring expert knowledge and sophisticated modeling, which is impractical for small and medium businesses.
Innovation Solution
The Dependable Compute Cloud (DC2) service employs Kalman filtering to automatically learn system parameters and proactively scale cloud infrastructure based on resource and application-level metrics, eliminating the need for offline profiling or expert knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If cloud computing provides flexible resource allocation, then resource cost is reduced, but the burden of dynamic scaling falls on the user requiring expert knowledge
Solution Approach 1:
The system performs self-service by automatically monitoring application performance metrics and resource utilization, then autonomously determining and executing scaling decisions without requiring user intervention or expert knowledge. The cloud platform independently manages the scaling process based on observed workload patterns and performance guarantees.
Solution Approach 2:
The system implements continuous feedback loops where application performance metrics and resource utilization data are monitored, analyzed, and used to trigger automated scaling actions. This closed-loop feedback mechanism enables the system to adapt dynamically to changing workload conditions and maintain performance guarantees while optimizing resource consumption.
2Reliability
If users manually determine scaling triggers and parameters, then control over infrastructure is maintained, but complexity and time consumption increase
Solution Approach 1:
The system automatically infers scaling parameters and triggers by analyzing application performance metrics and resource utilization patterns. Instead of requiring users to manually configure scaling policies, the system self-determines appropriate scaling actions based on observed workload characteristics and performance guarantees, significantly reducing configuration complexity.
Solution Approach 2:
The system dynamically adjusts scaling parameters such as threshold values, scaling rates, and trigger conditions based on real-time analysis of application behavior and workload patterns. This adaptive parameter adjustment allows the system to optimize scaling decisions automatically without requiring manual reconfiguration when workload characteristics change.
3Reliability
If cloud infrastructure is scaled proactively, then service level agreement violations are avoided, but resource consumption increases
Solution Approach 1:
The system performs preliminary analysis of workload patterns and performance trends to predict future resource requirements before SLA violations occur. By proactively identifying upcoming scaling needs based on historical data and performance guarantees, the system can prepare and execute scaling actions in advance, avoiding both SLA violations and unnecessary resource consumption.
Solution Approach 2:
The system optimizes scaling parameters by analyzing the relationship between resource allocation and performance outcomes. It adjusts scaling thresholds, response times, and resource allocation rates dynamically to achieve the minimum necessary resource consumption that guarantees SLA compliance, avoiding both over-provisioning and under-provisioning.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
DC2 effectively avoids service level agreement violations and minimizes resource consumption by dynamically adjusting infrastructure to meet performance guarantees, demonstrating superiority over rule-based approaches in experimental results.
Implementation Method 1
The embodiments of the present invention employ Kalman filtering to automatically learn the (possibly changing) system parameters for each application, allowing for proactively scaling the infrastructure to meet performance guarantees.
Data Source
AI summary
A method for scaling a cloud infrastructure, comprises receiving at least one of resource-level metrics and application-level metrics, estimating parameters of at least one application based on the received metrics, automatically and dynamically determining directives for scaling application deployment based on the estimated parameters, and providing the directives to a cloud service provider to execute the scaling.