Cloud Computing: 3 serverless downsides your cloud provider won’t mention

Cloud Computing: 3 serverless downsides your cloud provider won’t mention


Serverless is getting more popular as enterprises rush to the cloud, but some drawbacks are almost never discussed

Serverless may be a game-changer. As we glance to accelerate the post-pandemic movement to cloud, we might like to remove the step of sizing the cloud resources we expect the workloads will need.

Serverless automatically provisions the cloud resources needed, like storage and compute, then deprovisions them once the workloads are through processing. Although some call this a lazy person’s cloud platform service, removing the necessity to guess about provisioning the right number of resources will keep you out of trouble lately.

Cold starts, which are caused by running a serverless function during a virtual private cloud, may end in a lag or a chilly start time. If you’re remembering starting your mom’s Buick in high school, you’re shortly off.

Moreover, different languages have different lags. If you benchmark them, you’ll get interesting results, like Python being the fastest and .NET and Java being the slowest (just an example). you'll use tools to research the lag durations and determine the impact on workloads. If you’re in the least in serverless, I suggest you check out those tools.

Distance latency is how distant the serverless function is from the last word users. this could be sensitive, but I see companies run serverless functions in Asia when the bulk of users are within us. the idea is that bandwidth isn't a problem, in order that they search for convenience rather than utility, and do not consider the impacts, like the admin being located in Asia.

Another distance issue comes into play when the info is found during a different region from the core serverless function that uses the info. Again, this bad decision is usually made around process distribution on a public cloud. it's great on PowerPoint but isn't pragmatic.

Finally, underpowered runtime configurations are often overlooked. Serverless systems have a predefined list of memory and compute configurations, with things like memory running from 64MB to 3008MB. CPUs are allocated around a correlation algorithm that supported the quantity of memory leveraged. A lower memory setting is usually less costly, but there's a performance trade-off if the serverless system shortchanges you on both memory and CPU.

Nothing is ideal, and while there are many upsides to leveraging serverless systems, you would like to think about the downsides also. Having a practical understanding of issues allows you to figure around them effectively.




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