A Machine Learning Technique for Abstraction of Modules in Legacy System and Assigning them on Multicore Machines Using and Controlling p-threads

Hardware and Software technology has undergone a sea-of-change in recent past. Hardware technology has moved from single-core to multi-core machine, thus capable of executing multi-task at the same time. But traditional software’s (Legacy system) are still in use today in business world. It is not easy to replace them with new software system as they carry loads of knowledge, business value with them. Also, to build new software system by taking the requirements afresh involves lot of resources in terms of skilled human resources, time and financial resources. At last the customer may not have confidence in this new software. Instead of building a new software, an attempt is made to develop a semi-automated methodology by learning about the program itself (machine learning about the program) to abstract the independent modules present in the same abstraction level (implementation level) and recode the legacy program (single threaded program) into multi-threaded parallel program. A case study program is considered and execution time is noted and analyzed for both the original program and reengineered program on a multi-core machine

IOT future in Edge Computing

With the advent of Internet of Things (IoT) and data convergence using rich cloud services, data computing has been pushed to new horizons. However, much of the data generated at the edge of the network leading to the requirement of high response time. A new computing paradigm, edge computing, processing the data at the edge of the network is the need of the time. In this paper, we discuss the IoT architecture, predominant application protocols, definition of edge computing and its research opportunities