Manish Nigam

Over 23 years of experience in IT delivery/account management with specific focus on software delivery, managed services, operations, demand management &practice establishment and strategy for both large &established accounts as well as start-ups e.g. Digital Payment solutions, Retail & Corporate Banking, Pharmacovigilance in Life Science, Embedded and Supply-chain in Manufacturing, Pole Maintenance & Asset Analytics in Energy & Utilities. We off lately solved the challenge mentioned below: The challenge • Over time, the core system, simulation engine had started failing due to growing dataset size. The simulation engine and other services written in the R language became hard to maintain and execute. Rewriting the simulation engine on any other out-of-the-shelf cloud service or platform was not a cost-effective option which could also lead to business discontinuity. • Being a part of existing functional system, revamping existing architecture was not an economically viable solution. • The incumbent infrastructure (VMs, servers, hardware etc.) was considerably expensive and utilization of these available resources was less than 10%, except for the occasional peaks. • Carrying forward incumbent architecture considerations and constraints, the R script for simulation engine needs to be updated with minimum line of code so that it can start utilizing a cluster instead of a single machine. • The solution needs to be implemented securely within the existing network and connectivity on the Azure cloud. • After a successful POC on R script parallelization using a future package, utilizing a cluster of computers, existing system needs to be updated with so many microservices and complex architecture components.