Cloud Computing Resources Utilization and Cost Optimization for Processing Cloud Assets

In the field of Cloud Content Platform, where a user can store varieties of cloud assets (e.g. PDF documents) and can access them on any other platform such as Web, Desktop or Mobile. A creative content application may create different types of assets and store them on cloud content platform. A corresponding micro-service or cloud-worker should be present to handle asset processing request. Multiple cloud-workers should be present to handle different kind of asset processing requests. Most of the times, cloud computing resources are under utilized and cost spent on them for handling such requests is not optimized. Also, for a large and complex asset, the overall response time may increase due to high asset processing time. In this paper, we present novel method and system to predict cloud computing resources to serve input asset processing request. The method and system are designed in such a way to maximize resource utilization, minimize cost spent and minimize processing time by such computing resources. The method includes training of supervised learning based predictive model with historic data which includes asset processing requests, asset properties and their corresponding cloud computing resource utilization. The method also includes greedy approach to identify appropriate number of cloud computing resources required to process input asset processing job requests.