Kernel-Level-Implementation-of-Hadoop :R&D
Hadoop is an Apache open source framework written in Java that allows distributed processing of large datasets across clusters of computers using simple programming models in a distributed computing environment. The kernel is the essential center of a computer operating system. It acts as an interface between the user applications and the hardware. The sole aim of the kernel is to manage the communication between the software (user level applications) and the hardware (CPU, disk memory etc.).
As known Hadoop works on application layer so the motive of this research is to find:
1. Why it cannot be implemented at kernel level
2. What are the difficulties faced?
3. And try to implement it on android platform.
4. Finding solution to the encounter problems if possible.
Methodology
The Research and Development of this project will be done as Applied Research as this project is directed toward gaining knowledge or understanding necessary for determining the means by which recognized and specific needs can be met. This topic Kernel Level Implementation of Hadoop is mostly theoretical and not much knowledge can be gained about it. Kernel Level Implementation of Hadoop is almost next to impossible till now.
Listed below are some of the possible ways proposed by researchers as of now:
- Virtual Hadoop: Implementation of Hadoop in Virtual Environment using Cloud Stack KVM
- Offloading computational intensive kernels of machine learning algorithms to a heterogeneous CPU+FPGA platform enhances the performance.
- XConveryer: Guarantee Hadoop Throughput via Lightweight OS-level Virtualization
- Hadoop MapReduce for Mobile Clouds -Migration of Hadoop To Android Platform Using ‘Chroot’
YouTube Link
Find the YouTube link below for understanding problem statement and suggested solution:
Link : [https://youtu.be/SbWkJk_0BXc]