APIs for Hybrid Cloud Deployment
In the years leading up to the mainstream adoption of cloud computing, businesses were faced with a choice depending on the type of data being handled — whether to migrate to a public cloud or a private cloud. In many cases, a hybrid cloud deployment model can offer best of both worlds. API design can have a major impact on hybrid cloud deployment options and efficiencies. It always needs to address the questions of optimizing the component model and workflow, deciding what parameters to pass, and managing state and workload. To build APIs optimum for hybridization, an apt hybrid cloud model must be applied to meet goals and inventory application workflows, and design APIs to facilitate state management and load balancing.
Opting the Right Cloud Model
While embracing hybrid clouds, there are multiple hybridization models to consider, each with their own special API issues. Selecting the right cloud model depends on factors like control, security, scalability and flexibility, and management. Based on these factors, the following models can be adopted:
Web front-end model: Using this model, the user access portion of the application is migrated to the public cloud, but accepted transactions are passed to the data center for processing. This model is adopted by most of the companies looking for hybrid cloud solution.
Cloud bursting model: In this model, public cloud resources are expected to supplement data center resources during heavy load or in time of failure. Here, an application running on a private cloud or data center bursts into a public cloud when the demand for computing capacity spikes.
Offloaded analytics model: This model is mainly embraced by cloud applications that are used to analyze historical data, including big data. Using this model, organizations can keep their data for longer and can even mine and analyze the data for more advanced and impactful insights.
It is important to know which models need to be supported and how a company prioritizes its interest among them. While the hybrid models being targeted should be kept in mind, it's best to start with the most important one, setting up overall API strategy.
Workflow and API design choices
One of the first decisions when designing API is the data format that will represent the API and assess the workflows of the applications involved. While implementing Web front-end model, application should be framed with the assumption that the user interactions and GUI will be compatible with cloud technology. Database context will be established when the Web front end will pass data to an application on-ramp component. This component will run in-house for applications using work-flow critical core databases. Similar approach can be used for the analytics model of hybridization. In most cases, the cloud portion of the application accepts and validates the query before dispatching it to the application on-ramp where actual access to the database occurs. The separation of queries between the cloud DBMS and the core DBMS is done by the cloud portion of the analytics application, if an abstracted or summarized database in the cloud is hosted for some inquiries.
RESTful (Representational State Transfer) APIs and the on-ramp components are required to support everything in cloud where the full user requests are sent and results are returned via the on-ramp component. After reaching on-ramp component, two strategies can be implemented—
Passing entire transaction: RESTful APIs can be used throughout the workflow, although it may be beneficial to consider the SOA model for better documentation of the data needs of each component if specific data elements per component are to be supported,
Passing selective portions: In order to make data is available later, a cache DBMS will be needed to store the model.
As cloud bursting model presumes that components would move into and out of the cloud based on the current workload and the state of data center resources, the cloud bursting model of hybridization is considered the most complicated solution. This area is critical for state control, and the available transaction data model can be used to manage state and also to serve as a means of distributing work among multiple component instances. With application agility and resource efficiency as the main focus, flexible solutions works best, and a single transaction data model may be a good way to approach that goal of flexibility and reduce the risk.