## Understanding Workloads 

Because computing requirements are varied, so too are the workloads. Whether you’re using an IaaS for infrastructure or you’re developing SaaS applications using a PaaS, here are some of the kinds of workloads you’re likely to find in a cloud environment: 

✓ Batch workload:  These workloads operate in the background and are rarely time sensitive. Batch workloads typically involve processing large volumes of data on a predictable schedule (for example, daily, monthly, and quarterly). 

✓ Database workload:  These are the most common type of workload, and they affect almost every environment in the data center and the cloud. A database workload must be tuned and managed to support the service that is using the data. A database workload tends to use a lot of Input/Output (I/O) cycles. 

✓ Analytic workload:  Organizations may want to use analytic services in a cloud environment to make sense of the vast amounts of data across a complex hybrid environment. In an analytics workload, the emphasis is on the ability to holistically analyze the data embedded in these workloads across public websites, private clouds, and the data warehouse. A social media analytics workload is a good example of this. These kinds of workloads tend to require real-time capabilities. 

✓ Transactional workload:  These are the automation of business processes such as billing and order processing. Traditionally, transactional workloads were restricted to a single system. However, with the increasing use of electronic commerce that reaches across partners and suppliers, transactional workloads must be managed across various partners’ computing environments. These workloads are both compute and storage intensive. Depending on the cost-benefit analysis, it’s likely that complex transactional workloads are best suited to a private cloud. 

