Following the cloud concept section discussed earlier, you may already be thinking of some of your own uses that you could use the public cloud for. However, in this section, I just want to cover some of the common use cases of why organisations are adopting and implementing cloud computing.

Migration of production services. So, I want to start by touching on one of the bigger use cases where people migrate production services from their existing on-premise solution to the cloud. We have discussed the benefits of the cloud, and so with all of those benefits, many businesses are choosing to do just this, migrate their existing production services to the public cloud. Some companies even move their entire infrastructure to the cloud. Data storage and backup. Cloud services, like Amazon S3, Google Cloud Storage, and Microsoft Azure Blob Storage provide a scalable storage solution for data. These services are designed to grow seamlessly with an organisation's needs allowing for easy scaling up or down based on demand. They offer high availability through geographically distributed data centres, ensuring that data is accessible even in the event of a localised failure. Additionally, built-in redundancy means that data is replicated across multiple locations to protect against hardware failures and data loss. Robust security measures, including encryption and fine-grained access controls, ensure that data is protected against unauthorised access and breaches.

Traffic bursting. As an example, you may experience times within the year, perhaps for predicted seasonal circumstances, where your infrastructure takes a heavier load impact than that of other times of the year. Perhaps you are in the retail business and over the Christmas holiday period demand increases on your infrastructure significantly. In a classic data centre environment, you would need to provision compute, storage, and network capacity to reflect this and have it take up space, power, and calling (ph 01.49) all the time. This is not an effective method for scaling. Not only this, but there will be additional costs for this extra infrastructure to obtain, maintain, and operate and you may only use it for a couple of months of the year. A far better method of handling this peak traffic load would be to look at cloud computing. The public cloud can be used to scale your network and resources to manage and handle this additional traffic over your peak season. When the traffic has reduced, you can then terminate your infrastructure within the cloud and stop paying for it. Remember, you only pay for what you use when you use it.

Backup and disaster recovery. Due to the public cloud's built-in resiliency and durability, this makes way for a great solution for your backup requirements. To a degree, you have access to unlimited storage space with built-in data management life cycle policies, allowing you to make use of even cheaper storage. For example, using AWS, you could implement a policy to archive any data that's over 30 days old to AWS Glacier, which is a cold storage service with an even lower storage cost. The data will be available as long as you have access to the internet. These storage services are often replicated by the vendor to ensure its durability. Couple that with the very low cost of storage and you can see why more and more enterprises are adopting cloud computing for this very reason. Machine learning and artificial intelligence. Cloud providers offer robust platforms and services for developing, training, and deploying machine learning models, making these technologies accessible to more users and organisations.

Services like AWS SageMaker, Google Cloud AI, and Azure Machine Learning provide tools that streamline the machine learning lifecycle from the data preparation to model deployment. These platforms leverage the cloud's computational power, enabling the processing of large datasets and complex models and offer scalability to handle varying workloads. Cloud-based machine learning and artificial intelligence services integrate seamlessly with other cloud offerings, facilitating efficient workflows and data management. They provide prebuilt algorithms and customisable models, reducing the time and expertise needed to develop advanced AI solutions. And these platforms also support collaboration with tools for version control, experiment tracking, and model sharing. Moreover, cloud providers continuously update their AI and ML services with the latest advancements, allowing users to stay current without new hardware or software investments. They also implement stringent security measures to protect data and models, ensuring compliance with industry standards. So, these features make cloud computing an ideal choice for organisations looking to leverage machine learning and artificial intelligence for insights, automation, and innovation.

IoT, or Internet of Things. So, people use Cloud computing for IoT because it provides scalable and secure infrastructure to connect, manage, and analyse data from a vast array of IoT devices. Platforms like AWS IoT Core, Azure IoT Hub, and Google Hub IoT offer reliable communication protocols, robust security measures, and seamless integration with cloud services for real-time data processing and analytics. These cloud-based solutions simplify IoT application development, enable remote device management, and support advanced capabilities such as predictive maintenance and machine learning on IoT data. By leveraging cloud computing for IoT, businesses can enhance operational efficiency, accelerate innovation, and derive actionable insights from their IoT deployments without the complexity and overhead of managing on-premises infrastructure. Web hosting.

Many organisations choose to host their web services on the cloud due to its ability to load balance across multiple servers as well as scale up and down quickly and automatically as traffic increases and decreases with demand. The ability to provision and implement automatic scaling simplifies the whole process and takes out much of the administrative input and maintenance required. Not only can their web applications and database servers be enhanced by design, but they can also make use of other services such as a content delivery network, a CDN, and domain name services, DNS. Remember earlier when we were talking about selecting a geographic region for your instance depending on where your end users are? Well, what if you have end users all over the world? A CDN is a set of systems which redirects traffic to the closest caching server which can deliver the content much faster. As a result, a CDN can reduce the latency of a website for users across the globe if there are sufficient caching servers in place. DNS services can help manage demand on your web servers by redirecting any requests to a load balancer first. This load balancer can then evenly distribute the request to multiple web servers that you may have, therefore reducing demand on a particular web server.

Test and development. Similarly to our first point of traffic bursting, you may not have the capacity to host lots of servers and storage in your data centre for test and development purposes, and from a financial perspective, this would be a huge expense. Using the public cloud allows you to spin up servers as and when you need them and then shut them down at the end. This allows you to provision the size and capacity of your compute resources, so if you just need a high-end powerful server for your testing for an hour you can have it. It would not be financially viable for you to have this wide range of compute resources within your own data centre. Proof of concept. The cloud allows you to implement a proof of concept design and ideas to help to bring them to life at a fraction of the cost for the reasons I've already covered in the previous points, including hosting costs and only paying for what you use. The results of your proof of concept can help you build a successful business case when presenting to senior management. Data analytics and data manipulation.

The cloud also makes it easier and cheaper to manage data for similar reasons to the benefits of the cloud elsewhere. Maintaining and implementing compute resources to handle huge datasets can be expensive and complicated. By using cloud resources, you have the ability to use only the resources you need to analyse data when you need to. Some public cloud vendors offer specialised managed data analytics services which gives you a managed resource infrastructure and framework to run your workloads on in addition to allowing for scalability, scheduling, monitoring, etc. Having some of these elements managed by the vendor allows you to focus on the data and processing and not worry about the maintenance of the underlying architecture.

There are many more use cases of services within the cloud computing space and you'll more than likely have a few ideas or requirements of your own. Whatever you choose to do, you will have the benefits of the concepts discussed earlier at your disposal.

Common use Cases of Cloud Computing | ITL3 A3.1 |

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