Spot Instance Instability Heatmaps
A system collects data about spot instances across different regions and different cloud service providers and analyzes the collected data to identify any spot instances that have experienced disruptive failures, such as interruptions and failure to provision resources. The system evaluates how stable the spot instances are in each region for each cloud service provider based on the disruptive failures occurred on the spot instances and creates an interactive visual representation of the stability of these spot instances across different regions and cloud service providers.
1 . A method for visualizing stability of spot instances in a cloud computing environment, comprising:
collecting data on spot instances in a plurality of regions across a plurality of cloud service providers;
processing the collected data to identify spot instances experiencing disruptive failures;
determining stability levels of spot instances in the plurality of regions for each of the plurality of cloud service providers based on the identified spot instances experiencing disruptive failures;
generating a visualization of the stability levels of spot instances in the plurality of regions for a given cloud service provider of the plurality of cloud service providers; and
displaying metrics related to stability levels of spot instances in response to user interaction with the visualization.
2 . The method of claim 1 , wherein disruptive failures comprise interruptions or failures in provision requests.
3 . The method of claim 1 , wherein collecting data on spot instances comprises:
deploying an agent to on each of the spot instances on the plurality of cloud service providers; and
instructing the agent to collect corresponding data on a corresponding spot instance and send the collected data to an aggregator of the collected data.
4 . The method of claim 1 , wherein processing the collected data comprises:
extracting features associated with disruptive failures from the collected data; and
filtering the extracted features to identify spot instances experiencing interruptions or failures in provisioning requests.
5 . The method of claim 4 , wherein the features associated with interruptions comprise a timestamp, a spot instance associated with the interruption, and a reason for the interruption.
6 . The method of claim 4 , wherein the features associated with failures in provisioning requests comprise: a timestamp of a refusal of a provisioning request, a reason for the refusal of a provisioning request, and a resource type associated with the provisioning request.
7 . The method of claim 1 , further comprising:
applying a machine-learning model to the data collected during a historical time period to predict a capacity of spot instances in a region of a cloud service provider during a future time period, wherein the machine-learning model is trained over historical data associated with spot instances.
8 . The method of claim 1 , further comprising recommending a particular region for a particular cloud service provider for spot instances based on the determined stability levels.
9 . The method of claim 1 , wherein analyzing the collected data comprises:
periodically analyzing data collected during a rolling window to determine updated stability levels of spot instances across a plurality of regions for each of the plurality of cloud service providers; and
updating the visualization based on the updated stability levels.
10 . The method of claim 1 , further comprising:
collecting real time data associated with availability and stability of GPU instances across a plurality of regions of a plurality of cloud service providers; and
determining a GPU availability ratio and interruption frequency for each of the plurality of regions of the plurality of cloud service providers based on the collected real time data.
11 . The method of claim 10 , further comprising:
generating a visualization of GPU availability ratio and interruption frequency associated with GPU instances in the plurality of regions for a given cloud service provider of the plurality of cloud service providers; and
displaying GPU availability ratio and interruption frequency of a region in response to user interaction with the region in the visualization.
12 . A computing system comprising:
one or more processors; and
a non-transitory computer-readable medium coupled with the one or more processors and comprising stored instructions, the instructions when executed by the one or more processors causes the one or more processors to:
collect data on spot instances in a plurality of regions across a plurality of cloud service providers;
process the collected data to identify spot instances experiencing disruptive failures;
determine stability levels of spot instances in the plurality of regions for each of the plurality of cloud service providers based on the identified spot instances experiencing disruptive failures;
generate a visualization of the stability levels of spot instances in the plurality of regions for a given cloud service provider of the plurality of cloud service providers; and
display metrics related to stability levels of spot instances in response to user interaction with the visualization.
13 . The computing system of claim 12 , wherein disruptive failures comprise interruptions or failures in provision requests.
14 . The computing system of claim 12 , wherein collecting data on spot instances comprises:
deploying an agent to on each of the spot instances on the plurality of cloud service providers; and
instructing the agent to collect corresponding data on a corresponding spot instance and send the collected data to an aggregator of the collected data.
15 . The computing system of claim 12 , wherein processing the collected data comprises:
extracting features associated with disruptive failures from the collected data; and
filtering the extracted features to identify spot instances experiencing interruptions or failures in provisioning requests.
16 . The computing system of claim 12 , wherein the instructions further comprise instruction causing the one or more processors to:
apply a machine-learning model to the data collected during a historical time period to predict a capacity of spot instances in a region of a cloud service provider during a future time period, wherein the machine-learning model is trained over historical data associated with spot instances.
17 . The computing system of claim 12 , wherein the instructions further comprise instruction causing the one or more processors to:
collect real time data associated with availability and stability of GPU instances across a plurality of regions of a plurality of cloud service providers; and
determine a GPU availability ratio and interruption frequency for each of the plurality of regions of the plurality of cloud service providers based on the collected real time data.
18 . The computing system of claim 17 , wherein the instructions further comprise instruction causing the one or more processors to:
generate a visualization of GPU availability ratio and interruption frequency associated with GPU instances in the plurality of regions for a given cloud service provider of the plurality of cloud service providers; and
display a GPU availability ratio or an interruption frequency of a region in response to user interaction with the region in the visualization.
19 . A non-transitory computer-readable medium, comprising stored instructions, the instructions when executed by one or more processors causes the one or more processors to:
collect data on spot instances in a plurality of regions across a plurality of cloud service providers;
process the collected data to identify spot instances experiencing disruptive failures;
determine stability levels of spot instances in the plurality of regions for each of the plurality of cloud service providers based on the identified spot instances experiencing disruptive failures;
generate a visualization of the stability levels of spot instances in the plurality of regions for a given cloud service provider of the plurality of cloud service providers; and
display metrics related to stability levels of spot instances in response to user interaction with the visualization.
20 . The non-transitory computer-readable medium of claim 19 , wherein the instructions further comprise instruction causing the one or more processors to:
collect real time data associated with availability and stability of GPU instances across a plurality of regions of a plurality of cloud service providers; and
determine a GPU availability ratio and interruption frequency for each of the plurality of regions of the plurality of cloud service providers based on the collected real time data.