In this post, I will go through the process of predicting key performance characteristics and the cost of scale-per-request serverless platforms (like AWS Lambda, IBM Cloud Functions, Azure Functions, and Google Cloud Functions) with different workload intensities (in terms of requests per second) using a performance model.
Serverless Computing is the most promising trend for the future of Cloud Computing. As of 2020, all major cloud providers offer a wide variety of serverless services. Some of the FaaS offerings provided withing different cloud providers are AWS Lambda, Google Cloud Functions, Google Cloud Run, Azure Functions, and IBM Cloud Functions. If you want to use your current infrastructure, you could also use the open-source alternatives like OpenFaaS, IronFunctions, Apache OpenWhisk, Kubeless, Fission, OpenLambda, and Knative.
In a previous article, I iterated the most important autoscaling patterns used in major cloud services, along with their pros/cons. In this post, I will go through the process of predicting key performance characteristics and the cost of scale-per-request serverless platforms (like AWS Lambda, IBM Cloud Functions, Azure Functions, and Google Cloud Functions) with different workload intensities (in terms of requests per second) using a performance model. I will also include a link to a simulator that can generate more detailed insights at the end.
A performance model is “A model created to define the significant aspects of the way in which a proposed or actual system operates in terms of resources consumed, contention for resources, and delays introduced by processing or physical limitations” [source]. So using a performance model, you can “predict” how different characteristics of your service will change in different settings without needing to perform costly experiments for them.
The performance model we will be using today is from one of my recent papers called “Performance Modeling of Serverless Computing Platforms”. You can try an interactive version of my model to see what kind of information you can expect from it.
The input properties that need to be provided by the user to the performance model along with some default values.
The only system property you need to provide is the “idle expiration time” which is the amount of time the serverless platform will keep your function instance around after your last request before terminating it and freeing its resources (to know more about this, you are going to have to read my paper, especially the system description section). The good news is, this is a fixed value for all workloads which you don’t need to think about and is 10 minutes for AWS Lambda, Google Cloud Function, and IBM Cloud Functions and 20 minutes for Azure Functions.
The next thing you need is the cold/warm response time of your function. The only way you can get this value, for now, is by actually running your code on the platform and measuring the response times. Of course, there are tools that can help you with that, but I haven’t used them, so, I would be glad if you could tell me in the comments about how they were. Tools like the AWS Lambda Power Tuning can also tell you the response time for different memory settings, so you can check which one fits your QoS guarantees.
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