Accelerating development and deployment of applications in EIT 2.0

With the rapid progress in machine learning (ML), we have noticed a paradigm shift in application development from traditional control-flow Software 1.0 to data-flow Software 2.0 programming. The usage of ML-based models has replaced customer scoring techniques for generating recommendations. A modern enterprise IT system (EIT2.0) employs data-driven models to automate some of the tasks as below:

• A data management system may use reinforcement learning or recurrent neural networks to decide the optimal plan for query execution.

• Data structures such as B-tree indexes may be replaced by models trained on data access pattern.

• Resource provisioning in a cluster may be automated by observing workload patterns and their respective resource consumptions in a model.

• Core functionalities of network management such as traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security can be automated using ML/DL algorithms.