Hypervisor Noisy-Neighbor Score for VM Placement
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Solution Overview
Problem
In cloud computing environments, the 'noisy-neighbor' problem occurs where a virtual machine heavily utilizes system resources, leading to performance degradation for other tenants sharing the same hypervisor sockets.
Innovation Solution
A system and method for estimating performance impact on a hypervisor socket using a noisy-neighbor score, calculated based on compute usage and a machine learning model developed from controlled studies, to predict performance drops and optimize virtual machine placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple virtual machines are placed on the same hypervisor socket to increase resource utilization, then productivity is improved, but performance degradation occurs due to noisy-neighbor effects
Solution Approach 1:
The system performs preliminary assessment of noisy-neighbor scores for each hypervisor socket before placing virtual machines. By evaluating the predicted performance impact in advance and selecting sockets with lower scores, the system proactively prevents performance degradation rather than reacting to it after occurrence, thus maintaining both high resource utilization and stable performance
Solution Approach 2:
The system implements a feedback mechanism where noisy-neighbor scores are continuously calculated based on compute usage patterns and machine learning models. This feedback information is used to dynamically adjust virtual machine placement decisions, creating a closed-loop system that adapts to changing workload conditions and maintains optimal performance
2Reliability
If virtual machines are isolated on separate hypervisor sockets to prevent noisy-neighbor effects, then performance stability is improved, but resource utilization decreases
Solution Approach 1:
Instead of uniformly isolating all virtual machines on separate sockets, the system applies differentiated placement strategies based on local conditions. By evaluating noisy-neighbor scores for each specific socket and workload combination, the system allows multiple VMs to share sockets when conditions permit while isolating them when necessary, achieving optimal balance between resource utilization and performance stability
3Reliability
If noisy-neighbor scoring is implemented to predict performance impact, then performance stability is improved, but system complexity increases
Solution Approach 1:
The system implements self-service through automated noisy-neighbor score calculation and virtual machine placement optimization. The machine learning models automatically assess compute usage patterns and predict performance impact without requiring manual intervention, making the complex evaluation process transparent and eliminating the need for operators to understand the underlying complexity
Data Source
AI summary
In accordance with an embodiment, described herein is a system and method for use with a cloud computing environment, for estimation of performance impact upon a hypervisor provided within such environments, and the use of such estimation in placing virtual machines within the environment. A noisy-neighbor score value, generated for a particular hypervisor socket on a multi-core processor architecture, provides a predicted measure of performance drop which affected virtual machines placed on that hypervisor socket may experience at a particular point in time, due to activity of neighboring virtual machines on one or more sockets of the hypervisor. The predicted measure of performance drop can be automatically calculated by the system based on the amount and nature of compute usage on the hypervisor, and data defining a machine learning model developed from or through the operation and assessment of controlled noisy-neighbor studies over a collection of compute and memory-intensive workloads.