AI and its Applications in the Cloud strategy

Abstract

The fourth industrial revolution identifies cloud computing, data, and artificial intelligence (AI) as opportunity clusters with double digit growth in the next couple of years. As part of the cloud and digital transformation, the role of AI is crucial in enabling that transformation as well as creating the new breed of applications on top. AI mechanisms can help accelerate the modernization of applications, their management, and the testing on cloud architectures. I will focus on two sub-problems: 1) Refactoring of massive monolith applications using AI techniques. This problem statement is particularly relevant in understanding legacy un-optimized code and transforming them to be more cloud-ready. Microservices are indeed becoming the de-facto design choice for software architecture. It involves partitioning the software components into finer modules such that the development can happen independently [2]. It also provides natural benefits when deployed on the cloud since resources can be allocated dynamically to necessary components based on demand. We are exploring how AI can help accelerate the transformation of existing applications to microservices. 2) Detecting faults in application behavior at runtime from operational data. This problem statement is particularly relevant in understanding how to manage this new architecture of multiple microservices across the cloud stack [1], [3]. Operational data artifacts span across logs, metrics, tickets, and traces. Looking at signals across the artifacts and across the stack presents a challenging data correlation problem. AI mechanisms can help accelerate problem determination in these complex environments. I will also share my thoughts on how fundamental breakthroughs in AI Research will be needed as we address some of the core problems of cloud computing.