Azure Workload

Microsoft Azure on-demand cloud services is one of the best options for reliable cloud migrations. Azure offers everything organizations need to store and manage large amounts of data while also greatly improving their online security. This article will explore the nature of an Azure workload, types of virtual machines, cloud infrastructure, cloud migration to Azure, and more.

What is a workload in computing? Simply defined, computing workloads refers to the number of tasks, applications, processes, or “work” a machine can handle. What is a workload in cloud computing? Is a workload in cloud computing different from other types of workloads, such as a network workload? The answer is yes and no. The term simply refers to where the workload is taking place. A workload that takes place in the Azure cloud often contains the same type of work as a network workload. It just takes place in the cloud. A network workload is a workload that takes place on a network, typically within an organization’s IT infrastructure. That same workload could be moved to a cloud and then be called a “cloud workload.”

What is an Azure workload? Azure offers many methods of monitoring the workloads that are deployed using its virtual machines (VMs) to achieve maximum efficiency. Microsoft Azure offers multiple kinds of virtual machines that can be used for supporting and optimizing workload performance, and Azure workloads depend heavily on the type of virtual machine they are built on. 

Types of Azure virtual machines

You can find the following types of virtual machines in an Azure subscription:

General Purpose VMs: These VMs are not optimized for any specific use case. Instead, they can be used for many different purposes, such as web servers, software development, and testing, or other kinds of application deployments. There are also wider customization options available for users with General Purpose VMs, selecting from instances with various vCPU, memory, and storage configurations.

Compute-optimized VMs: Compute-optimized virtual machines are dedicated to instances that require high processing power that is relevant to the amount of memory available. These types of VMs are especially useful with machine learning applications, high-traffic web servers, or network-centric workloads. 

Memory-optimized VMs: These virtual machines place a higher priority on memory instead of other configurations, such as processing ability. These types of VMs are good for workloads that require heavy-duty content caching, database networking, and analytics that must be stored in memory. These virtual machines are built for an incredible amount of memory, with some instances offering up to 12 terabytes. 

Storage-optimized VMs: These VMs are also very memory-heavy. However, storage-optimized VMs are built for big data projects that may not require constant use. Big data projects can benefit from both memory-optimized VMs and storage-optimized VMs.

GPU-optimized VMs: These VMs are optimized to handle high-level graphics and visual workloads. Many of these workloads are extremely taxing of CPUs, and therefore GPUs are better utilized to handle processing tasks. GPU-optimized VMs are most helpful for visualization workloads, gaming, and graphics workloads.

High-performance compute VMs: These VMs are built for enterprise and above-level workloads. They are built for speed, efficiency, and power. It’s all about performance and places heavy emphasis on processing and memory bandwidth.

Cloud Infrastructure and Cloud Security

Cloud infrastructure refers to the collection of physical and virtual resources that make up a cloud computing environment, including Azure workloads. This can include everything from servers to software applications that are used to deliver cloud-based applications to end users. However, many end users never have to worry about cloud infrastructure at all, as they invest in a cloud infrastructure-as-a-service provider to take care of this on their behalf. Cloud infrastructure management ensures that end users have access to their desired amount of resources and are achieving their desired performance. 

Cloud infrastructure as a service also makes sure that end-users’ applications and deployments are easily scalable, accessible, secure, and high performing. Although end users never have to think about it, cloud infrastructure architecture involves the physical and virtual resources that are used to build and maintain an end user’s cloud connection. This includes computing resources, storage resources, networking resources, security resources, as well as management and automation tools. Skytap is a cloud infrastructure as a service purpose built to run IBM Power workloads natively in the cloud. 

Another aspect of cloud infrastructure management that cloud service providers (CSPs) handle is cloud workload security. Cloud resources and deployments may be web-based, but they also rely on physical hardware that can be at risk of damage, regular wear and tear, or attacks. To combat this, many CSPs place cloud regions into effect so that if one area needs to go down for maintenance or replacement, end users still have access to all of their information. 

Azure Cloud Migration

For organizations to utilize Azure workloads, an Azure cloud migration might be necessary to get legacy or on-premise workloads running in the cloud. However, not every cloud migration can be performed in the same way. For users on legacy systems, cloud migration can be incredibly intimidating, an endeavor that might cost significant time and money. This is when it can be highly beneficial to bring in an experienced Azure cloud partner, such as Skytap, to make the transition.

The Azure cloud migration process itself typically involves several steps. Data migration involves moving data from the legacy system to the cloud. Application migration involves adapting or rewriting legacy applications to work in the cloud environment. Skytap is built to allow users to migrate without rewriting or refactoring their systems. Skytap also features easy-to-use templates that streamline building a virtual environment in the cloud. After the migration is complete, extensive testing is performed to ensure that the migrated system is functioning as intended. 

Once the migrated system is up and running in the cloud, ongoing optimization is performed to ensure that it is running efficiently and cost-effectively. This may involve optimizing cloud resources, adjusting configurations and Azure workloads, and making other changes as needed.

Azure Workload Monitoring

Overall, cloud migration of legacy systems requires careful planning and execution, with a focus on identifying and addressing potential challenges and risks. It also requires specialized skills and expertise, as well as a willingness to adapt to new technologies and ways of working. 

Skytap offers scalable cloud options so that users never have to pay for more resources than they are using. Skytap also offers several ways for their users to conduct Azure workload monitoring, ensuring that their workload vs utilization is an efficient and good use of their resources. In order to achieve this, workload management needs to be conducted so that workload vs. application usage can be monitored. If an organization needs fewer resources, it’s just as easy to scale down as it is to scale up when they grow.

Learn more about Skytap on Azure here.