Mobile Grid and Cloud Computing Lab at Hankuk University of Foreign Studies is seeking MS and PhD students to research Edge Computing and Networking Systems.
REQUIREMENTS
Applicants should have an undergraduate or graduate degree in computer science, engineering, or a related field, and a strong background in distributed computing and networking. Preference will be given to applicants who have experience in C programming and a good knowledge of machine learning and network simulation tools such as NS3.
FINANCIAL SUPPORT
PhD: Full tuition fee scholarship + 1,000,000 Korean Won for 3 years
MS: Full tuition fee scholarship + 800,000 Korean Won for 2 years
Financial assistance for attending reputable conferences and workshops
Application Deadline: April 30, 2022
Start Date: September 1, 2022
PROCESS
Step 1: Submit the following documents to Chhattan Shah at shah@hufs.ac.kr
a) Motivation letter
b) Curriculum vitae
c) A research proposal on any topic related to distributed computing and networking
Shortlisted applicants will be asked to apply for admission to HUFS graduate school
Step 2: Apply to HUFS graduate school http://www.hufs.ac.kr/gra
For admission process and required documents, visit https://builder.hufs.ac.kr/user/graeng/index.html
Application Period: 2022.5.9 ～ 2022.5.18
Step 3: Applicants will be interviewed by HUFS Admission Committee
Step 4: Selected applicants will be interviewed by MGC Lab Team (https://mgclab.com/team.php)
Additional Information:
Mobile Grid and Cloud Computing Lab: http://www.mgclab.com
Research at Mobile Grid and Cloud Computing Lab: http://www.mgclab.com/research.php
Supervisor: https://mgclab.com/professor.php
Co-supervisor: https://mgclab.com/professorbilal.php
HUFS Graduate School: http://www.hufs.ac.kr/gra
HUFS Graduate School Admission: http://builder.hufs.ac.kr/user/graeng/
Hankuk University of Foreign Studies: http://www.hufs.ac.kr/user/hufsenglish/
Project Overview
This research aims to develop an intelligent middleware platform that efficiently exploits the characteristics and address the challenges of heterogeneous computing and network environment to (1) efficiently manage heterogeneous compute and network resources and (2) provide task processing, data collection, data storage, and caching services to support emerging resource-intensive and non-resource intensive smart city and 5G network applications. The new platform will consist of two layers: a software-defined network (SDN) and machine learning-based multi-network management layer and a machine learning-based resource management layer. The multi-network management layer will (i) use capabilities of machine learning and software-defined network to improve network and application performance, (ii) provide serial and parallel data transmission services across multiple heterogeneous networks, (iii) support dynamic allocation of network interfaces, and (iv) employ new machine learning-based link quality and Markov-chain-based link lifetime estimation techniques to reduce communication and energy consumption costs. The resource management layer will (i) leverage regression analysis and reinforcement learning methods to efficiently allocate heterogeneous computing and network resources to application tasks, (ii) use container and device virtualization technologies to address problems related to heterogeneous execution environments and IoT devices, and (iii) use parallel transmission techniques, dynamic interface allocation techniques, and network-level parameters to address diverse application requirements. The specific objectives of this research are as follows.
1) Design of a machine learning-based intelligent middleware platform for a heterogeneous private edge cloud system. This objective is measured by the successful delivery of a new SDN and machine learning-based multi-network management layer and a new machine learning-based resource management layer.
2) Development of machine learning-based intelligent middleware platform based on the design in objective 1. This objective is measured by the successful delivery of a prototype of the new SDN and machine learning-based multi-network management layer, a prototype of the new machine learning-based resource management layer, and a prototype of the new platform.
3) Simulation and performance analysis of new machine learning-based intelligent middleware platform developed in objective 2. This objective is measured by the successful delivery of a performance analysis report of the new SDN and machine learning-based multi-network management layer and a performance analysis report of the new machine-learning-based resource management layer.
The project is funded by National Research Foundation of Korea.