Serverless presentation

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Cette session vous propose de plonger dans le monde des mental models, leurs liens avec le personal mastery. Il y a 25 ans, Peter Senge formalisait les cinq disciplines des « learning organizations ».

The Architecture of Huawei Cloud Serverless Solution. Introduction to Serverless Computing. Language specific function runtime library (public) Customer function.

The first question is strictly related to how many calls succeeded versus how many failed. 9% of calls failing. This table takes a holistic view of the calls as a collection of data points and asks a few simple questions. From this perspective, Microsoft is at a disadvantage with 3. I found this odd, and after running the test again I wasn’t able to produce the same rate of failure, so the odds are that this just happened to be an aberration when I ran my test (likely due to my Azure Function having been in the wild for less than an hour), so this result is probably not as interesting as it seems at first. As we’re working with HTTP communications, we have the potential for a request to disappear into the Internet backbone, either timing out or never returning.

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serverless presentation

The Y axis is a simple count of the functions calls that fell into each bucket for each provider. The yellow bars represent Microsoft Azure, the red Google Cloud, and the blue AWS Lambda. Let’s take a look at each provider in isolation. Already we can draw some observations – for example, it’s obvious that the Microsoft Azure runtime histogram shows better performance over both Google and AWS, and that while Google and AWS share similar performance characteristics, AWS has tighter grouping around its modes while the Google runtimes are a bit more evenly distributed. In the above graph, the X axis defines a series of run-time buckets (in seconds).

Given that the numbers are so close, though, and that Google had such higher variance between its modes, we can only use these as observation points rather than scientific proof. The result of this comparison is that Google Cloud Functions has about a 40 millisecond lead time on AWS Lambda when it comes to comparing the machine instance ramp-up time. Using the histograms, I determined approximate modes based upon the runtimes recorded, and then calculated the difference between them. This is quite a small difference, but you need to consider the impact in aggregate – if I have written a serverless application that makes 25 serverless calls during a page load, my worst-case performance difference between AWS and Google will be a full second. Finally, we’ll take a look at the data point that originally kicked off my performance comparison – hot versus cold performance.

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I enjoyed this fabulous conference a lot. Highlights were the the Serverless presentations, the istio presentation by. Devoxx 2017 in Casablanca has come to an end.

Alex Ellis: OpenFaaS or Functions as a Service is a Cloud Native framework for building serverless functions (as popularised. Cloud Native community cast.

As such, I quickly realized that a straight apples-to-apples comparison wasn’t going to be strictly possible. Each platform offers disparate sets of functionality, different user interfaces, and different runtime environments – even when working within the same language. This meant focusing on what I consider the most interesting metric – round-trip HTTP performance time. I, therefore, looked at focusing my testing on the aspects that I could reasonably compare on a 1:1 basis. Having decided on which providers to compare, the next task was to decide how to compare them.

You can use AWS Lambda and Amazon Kinesis to process real-time streaming data for application activity tracking, transaction order processing, click stream analysis, data cleansing, metrics generation, log filtering, indexing, social media analysis, and IoT device data telemetry and metering.

serverless presentation

4 thoughts on “Serverless presentation

  1. Fitter_Happier Post authorReply

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    1 Installing Git for Linux; 2 Configuring GitHub; 3 Creating a local repository; 4 Creating a README file to describe the repository; 5 Adding repository files to an index; 6 Committing changes made to the index; 7 Creating a repository on GitHub; 8 Pushing files in local repository to GitHub repository; Links. GitHub is a  .

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