Multi-tenant Interference Model for Container Autoscaling
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Solution Overview
Problem
Container orchestration systems, such as Kubernetes, lack the ability to detect instances of multi-tenant interference, which can lead to adverse impacts on service level agreements (SLAs) and result in slower response times for users.
Innovation Solution
An autoscaling framework with a multi-tenant interference model that selectively scales resources in container orchestration systems based on estimated and actual performance metrics, as well as a violation count, to mitigate response time issues and ensure SLA compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If container orchestration systems use resource-based scaling (CPU utilization), then resource allocation efficiency is improved, but the system cannot detect multi-tenant interference and SLA violations
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring response times and SLA metrics, using this information to dynamically adjust scaling decisions. The system feeds back actual performance data to the scaling controller, which modifies resource allocation based on detected multi-tenant interference patterns, thereby maintaining SLA compliance while improving resource efficiency.
Solution Approach 2:
The patent introduces an intermediary component (scaling controller with multi-tenant interference detection) that sits between the resource management system and the container orchestration system. This intermediary detects multi-tenant interference and translates it into appropriate scaling actions, resolving the contradiction by adding a layer of intelligence that considers both resource efficiency and SLA requirements.
2Use of energy by moving object
If the system scales containers based on traditional metrics, then resource utilization is optimized, but response time violations occur due to undetected multi-tenant interference
Solution Approach 1:
The patent applies preliminary action by proactively detecting multi-tenant interference patterns before they cause SLA violations. The system monitors for early signs of interference and pre-scales resources accordingly, preventing response time violations before they occur. This anticipatory approach maintains both resource efficiency and response time requirements.
Solution Approach 2:
The system implements feedback loops that continuously measure response times and feed this information back to the scaling mechanism. When response times approach violation thresholds, the feedback triggers additional scaling actions, ensuring response time requirements are met while optimizing resource utilization through data-driven decisions.
3Device complexity
If no multi-tenant interference detection is implemented, then system complexity is reduced, but service level agreements cannot be enforced
Solution Approach 1:
The patent segments the scaling system into distinct modular components: a monitoring module that collects metrics, a detection module that identifies multi-tenant interference patterns, and an execution module that performs scaling actions. This segmentation manages complexity by creating independent, well-defined interfaces between components while enabling comprehensive SLA enforcement through coordinated operation of the segments.
Data Source
AI summary
Methods, systems, and computer-readable storage media for an autoscaling framework that uses a multi-tenant interference model for selectively scaling resources in container orchestration systems based on an estimated performance, an actual performance, and a violation count of each resource unit of a plurality of resource units. In some implementations, the autoscaling framework of the present disclosure mitigates length of response times in applications composed of a set of services.