Serverless computing has been a revolutionary invention. When looking to fast-track the post-COVID-19 movement to the cloud environment, we would prefer to not have to size the resources that we feel our workloads will require.
The serverless technology not only provides the cloud-based resources required, like storage, but it also removes access to these resources after workflows complete processing. Some may describe this as a lazy individual’s platform as a service, but eliminating the need for guessing about providing the precise resource count will save you much hassle nowadays. Anyhow, as with any other technology, serverless computing also has some drawbacks, and we will take a look at some of these here.
Cold Start
If you run a serverless computing function in a VPN (virtual private cloud), then it might just cause a delay or cold start. This is similar to the action of starting Buick, according to individuals of a certain vintage.
Furthermore, different languages come with their own set of lags. In the event of benchmarking these, you may discover that Java and .Net are the slowest and Python is the quickest. You may utilize tools for analysing the lengths of lag and determining the effect of these on your workflows. In the event you rely on serverless computing, we would recommend utilizing the products mentioned above.
Distance Latency
The phrase ‘distance latency’ refers to what extent the function mentioned above is from its end users. We notice that businesses are running the tasks in Asia even as most users hail from the US. Bandwidth may not be thought of as a problem, but the users prefer convenience over utility. Moreover they do not think about the effects, like the administrator being situated in the subcontinent.
The data being situated somewhere different from the central serverless function, which utilizes the data, is a source of one more distance problem. This poor choice is usually based on process distribution through the public cloud. While it may appear great on Microsoft PowerPoint, it is not a pragmatic choice.
Insufficient Runtime Configurations
These configurations tend to be disregarded. There is a pre-set list of compute and memory configs for serverless systems, where things such as memory range between 64 and 3008 megabytes. Central processing units are usually allocated on the basis of the level of memory utilized. A lower computer memory environment is generally more affordable, but a performance-related compromise would be there in the event of the serverless computing provider treating you unfairly in regards to CPU and memory.
There may be several pros to using serverless systems, but you should also think about the cons. Understanding these things pragmatically will let you bypass them successfully.
