MELODIC can be used to deploy applications in different cloud computing models and using most advanced services. It provides unique capability of deploying complex applications using the latests achievements of Cloud Computing in fully transparent and automatic way.
MELODIC is the unique platform which allows for automatic and optimized multicloud deployment. It is done fully transparent and without a need for manual changes. The application is modeled once in CAMEL language and then deployed in an automatic way to selected Cloud Providers. Without MELODIC multicloud deployment is very difficult to manage and maintain. With MELODIC multicloud deployment is fully automatic and optimized.
Hybrid cloud deployment
Hybrid cloud, the mix usage of on premises infrastructure with public cloud, is one of the specific deployment models supported by MELODIC. It is fully automatically managed and optimized based on live monitoring of application’s usage and continuous optimization. That approach allows to maximize efficiency of the on premises infrastructure and handle peaks of workload with additional cloud resources.
MELODIC is the only platform which supports optimized serveless components deployment in a transparent way. The components are deployed to selected Cloud Providers based on the one model prepared in CAMEL language. There is no need for any manual work to deploy that type of component. We are considering MELODIC as the easiest way to start multicloud serverless deployments.
For the containers based applications MELODIC allows for fully transparent and automatic deployment to various Cloud Providers. Based on one configuration, the specifc cloud deployments are done automatically without human interactions.
MELODIC gives unique ability to dynamically adjust the size of Big Data clusters Spark, to optimize usage of resources. The Big Data cluster can be increased or decreased through adding nodes, to optimize resources and fulfill business goals.
MELODIC is especially useful when using computational intensive machine learning and AI based applications. For that types of applications the ability to optimize resources in dynamic way can give significant saving both in cost of usage and time of executions.