What is cloud computing?

Cloud computing can be described in simple terms as on-demand access to near-infinite computing and data services, from anywhere on the internet. The cloud can accelerate discovery by providing you with almost every piece of computing and data capability that you may require for your research.

Whether it’s a computer with more memory, a cluster with thousands of cores, a big data platform, an Internet of Things solution, or open-source machine learning at scale, you can achieve more using the cloud.

Why Microsoft Azure for research?

Microsoft Azure lets Researchers be Researchers. We have a deep understanding of the key challenges faced by researchers and our cloud platform addresses those needs to accelerate discovery.  We provide an open, flexible, global platform that supports multiple programming languages, tools, and frameworks. That means that research breakthroughs can happen more quickly by giving you access to near-infinite computing and data services, from wherever you are.

Learn more about how researchers around the world are using Azure today and read more in our cloud computing guide for researchers here.

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Top Research Workloads:

Microsoft Research has worked with hundreds of researchers in every discipline to explore how cloud computing can be best used.

We see the following research scenarios again and again that cover most of the different situations researchers find themselves in so have built solutions to help you get the most from your research. Here are just some of the top research workloads we can support you with;

Beyond the desktop:

Would you be able to be more effective if you had a bigger desktop machine or access to a bigger server? One of the things cloud computing does is bring the power and data processing ability of huge machines to any researcher’s desk, quickly.

Azure Virtual Machines gives you the flexibility of virtualization for a wide range of computing solutions with support for Linux, Windows Server, SQL Server, Oracle, IBM, SAP, and more. All current generation Virtual Machines include load balancing and auto-scaling, for free. Find out more here.

Watch this video to learn more about how Microsoft Azure accelerated research at the University of Stirling to model the complex data from many airports worldwide to reduce delays, save money and reduce environmental impacts.


Computing at Scale:

As a researcher, you may want to run lots of computations. Running a high-performance computing simulation that needs high-bandwidth, low-latency supercomputer networking to scale to hundreds of cores is uniquely possible on Microsoft Azure.

Azure Batch is even more powerful, delivering a true HPC-as-a-Service model, where you can wrap your application with a simple template, and then run your HPC job without worrying about cluster management. Azure Batch makes it easy to create many machines to simultaneously run your jobs, so you can get your results in few hours or days.

Watch this overview on Azure HPC here

See how WorldPop researchers are using Azure HPC to give them true supercomputing performance to count every person on Earth, helping to eradicate poverty and empower women. Read about how Simon O’Hanlon at Imperial College London was able to speed up his genomics research with Microsoft Azure.

Data science, big data and machine learning:

Microsoft Azure is the ideal platform for exploring big data, as it provides just about everything researchers need, avoiding much of the complexity of setting up systems.

Azure Machine Learning is an integrated, end-to-end data science and advanced analytics solution. It enables data scientists to prepare data, develop experiments, and deploy models at cloud scale significantly accelerating data science project development and deployment.

Read our cloud computing guide for researchers on how to get started with AI and Machine Learning.

Read more about how, at the University of Oxford, Cortana Intelligence has allowed the REACH team to take their machine learning from the lab, and deploy at it scale easily to help provide water for thousands across Africa and Asia. See how the Alan Turing Institute, the UK’s national centre for data science research, is changing the world with data science.


Internet of Things:

Researchers often spend a lot of time figuring out how to deploy and manage devices and gather data from them. The real value from IoT comes with making sense of the data, and that’s where Azure Machine Learning and Stream Analytics come in.

We also provide an out-of-the-box solution to help you called Azure IoT Suite.  Easily scale from just a few sensors to millions of simultaneously connected devices and rely on the global availability of Azure—no matter how large or small your project. IoT Central provides a full-managed IoT SaaS (Software-as-a-Service) solution, making it even easier to deploy your research quickly, securely, and at any scale.

Try these starter kits and check out this pre-configured remote monitoring solution that deploys an end-to-end system with a single mouse-click to help get you started.

Whether you’re deploying sensors in the Brazilian rainforest, or on a linear accelerator, cloud computing means you can concentrate on your research.


Research data sharing and collaboration:

Research is a collaborative endeavour, but it’s not always easy to share data, workflows and software with others in the lab, research group, or around the world. Cloud computing makes this much easier, by being able to host data, workbooks, and computing together in one place. As a researcher, you can share as much data as you like, and it’s as easy as using Azure Storage Explorer, Python, or command line tools, as explained in this video, and this detailed walkthrough on Github.

Read more about how the University of Texas used Microsoft Azure to collaborate with others to help predict and prevent flooding disasters.