Resource Management and Optimization in Wireless Networks
The goal of this research is to develop fundamental theories, communication algorithms, and networking protocols for efficient allocation of spectrum, hardware, and power in high-throughput wireless networking environments. Topics of our investigation include co-operative communication, small-cell networks, interference management, stochastic optimization, and dynamic resource allocation.
Fair Resource Scheduling in Large-Scale Networked Systems
The principles of network science permeate wide-ranging applications such as communications, cloud computing, power grid management, transportation and biology. A central issue is how to effectively share network resources among competing agents. We are interested in developing new theories and practices for fair resource scheduling in large-scale networked systems. Examples of our investigation include cloud computing economics, distributed smart-grid control, and multi-resource fair scheduling.
Heterogeneous Data Communication for Mobile Cloud Computing
This research targets the large-scale heterogeneous communication and networking architecture expected to serve as the backbone of the emerging mobile cloud-computing paradigm. We envisage a seamless global system of computing, communication, and applications, supported by a synergistically operated mobile cloud-computing system, incorporating hybrid macro cloud centers, micro cloudlets, and smart mobile devices. Topics of investigation include mobile computation offloading, virtual machine placement and allocation, and co-operative joint communication-computation.
Advanced Machine Learning Techniques for Network Traffic
In this project, we apply adaptive machine learning techniques to categorize the network traffic, leveraging the availability of a vast amount of anonymous user traffic data through collaboration with a network service provider. We also take advantage of the emerging capabilities of computing at the network edge, where the network traffic is more localized with shared commonalities among local users, to improve the accuracy of traffic classification. We aim to develop practical guidelines on how to design and operate network traffic classifiers, to optimally balance the trade-off between accuracy, privacy, cost, and delay.
Distributed Machine Learning with Mobile Edge Computing
In this project, we study the framework of distributed Machine Learning (ML) within the multi-level hierarchy of Internet-of-Things (IoT) devices and Mobile Edge Computing (MEC) servers, with an aim to develop new methods and techniques for data collection, task scheduling, and system optimization. Toward this objective, we propose to develop efficient algorithms for ML task distribution among IoT devices and MEC servers, design jointly optimal task distribution and MEC resource allocation solutions, and create online scheduling and MEC resource optimization methods.