Introduction
The cloud is the future, but there’s a problem. We just don’t know how well it will work in practice until something goes wrong.
Cloud computing has become one of the fastest growing segments of IT over the past decade. And while it has its benefits, it also comes with certain risks that can make or break a business’s success:
Cloud systems can’t handle traffic spikes as effectively as their on-premise counterparts. Trying to scale a service up or down in real time can be difficult or impossible. Failing to scale a service appropriately could mean losing your customer base (and reputation). As of yet there is no way to directly compare cloud services on their ability to scale because each provider uses different metrics for measuring scalability and elasticity — which means there’s no standard way of measuring performance across providers either!
The cloud may be the future, but it has some serious scalability issues.
Cloud computing has many advantages, but it also has a serious problem called scalability.
If you’re not familiar with cloud computing, it’s a way to use the internet instead of owning and managing servers. This can help you scale your business because it gives you access to more powerful computers than what most people have on their own computers or in their homes. The idea behind this is that if one computer goes down or gets slow because too many people are using it at once, then another computer will take over for it automatically–and this happens without any downtime for users because they don’t even notice when the switch happens!
This sounds great in theory but there are some serious issues when implementing this kind of system:
Cloud outages can take down entire websites and services for hours or days at a time.
Cloud outages can be caused by many different things. Cloud providers are not immune to outages, and they’re not exempt from the risks of hardware failure, software bugs, human error and even natural disasters. Outages can be caused by a single cloud provider or multiple cloud providers at once.
It’s important to note that while these incidents are rare–and most organizations have backup plans in place should something go wrong–they do happen on occasion. It’s also worth noting that while Amazon Web Services suffered a major outage last year (caused by human error), it was able to recover within hours without any data loss or downtime for its customers
Cloud systems can’t handle traffic spikes as effectively as their on-premise counterparts.
The problem with cloud systems is that they don’t scale as well as on-premise counterparts. Cloud systems are designed to handle a steady stream of traffic, not sudden spikes. When there’s a sudden spike in traffic, the system can’t handle it because it doesn’t have the resources to do so–and if you add more resources at this point, they’ll just be wasted when demand drops off again. To make matters worse, this happens often enough that even if your site does happen to survive these spikes (which is unlikely), it will likely suffer from performance degradation as its resources are stretched thin or overloaded by too many users simultaneously accessing them at once.
Trying to scale a service up or down in real time can be difficult or impossible.
Scaling a service up or down in real time can be difficult or impossible.
Because cloud computing is based on virtualization, the resources it provides are not always available as needed. When you increase the number of instances of an application, you may find yourself waiting for more capacity to become available before scaling up again–and this delay might cause problems for your users if they’re expecting instant responses from your application. If you want to scale down quickly, there may not be enough capacity left over after scaling up has completed; this means that some users will have their requests rejected until other instances are terminated (which takes time). In these cases, manual intervention is necessary: someone has to manually terminate instances before they run out of money at each stage of their life cycle–this can take anywhere between minutes and hours depending on how long it takes those instances’ owners’ billing systems or administrators’ approval processes
Failing to scale a service appropriately could mean losing your customer base.
If you don’t scale your service to meet demand, you will lose customers. This is a simple fact of life in the cloud computing world. If your service isn’t able to handle the load of new users or increased traffic from existing users, then customers will find another provider who can provide them with what they need.
This is why scalability is so important: if you fail at scaling up (or down) quickly enough when needed, then eventually everyone will leave your service behind and go somewhere else where they can get better performance and reliability – not just because they want better performance but also because they have no other choice since all other providers are doing exactly what their competitors are doing right now too!
As of yet, there is no way to directly compare cloud services on their ability to scale.
As of yet, there is no way to directly compare cloud services on their ability to scale.
There are no standards for measuring scalability and elasticity in the industry. Cloud providers use different metrics for measuring scalability and elasticity that may not be comparable across providers. This makes it hard for customers to know how well any single cloud provider will perform at any given moment until the disaster happens.
To make things worse, each cloud provider uses different metrics for measuring scalability and elasticity.
The problem with these metrics is that they’re not standardized. Each cloud provider uses different metrics for measuring scalability and elasticity, so it’s hard to compare the services on their ability to scale.
For example, Amazon Web Services (AWS) uses “instances” as its unit of measurement while Google Cloud Platform (GCP) uses “vms.” An AWS instance has a specific amount of RAM, CPU cores and storage space attached to it; whereas a GCP vm can have any combination of those resources plus additional features like GPUs or FPGAs–and they’re all priced differently based on what options you choose at creation time.
There is no standard way of measuring the performance of cloud computing services like AWS, Microsoft Azure or Google Cloud Platform (GCP).
There is no standard way of measuring the performance of cloud computing services like AWS, Microsoft Azure or Google Cloud Platform (GCP).
Cloud providers use different metrics for measuring scalability and elasticity.
There’s no way to know how well any single cloud provider will perform at any given moment until the disaster happens
The problem is that there’s no way to know how well any single cloud provider will perform at any given moment until the disaster happens. These companies are not transparent about their ability to scale, and they don’t publish performance metrics or SLAs (service level agreements).
That’s why we need a new generation of cloud computing platforms that can automatically scale up or down depending on demand.
Conclusion
The cloud is not a perfect system, and it’s certainly not immune from outages and other issues. But it has proven its worth as an alternative to on-premise solutions in many cases. The problem, however, is that there’s no way to know how well any single cloud provider will perform at any given moment until the disaster happens.
