Power vs. Spectrum 2-D Sensing in Energy Harvesting Cognitive Radio Networks

Energy harvester based cognitive radio is a promising solution to address the shortage of both spectrum and energy. Since the spectrum access and power consumption patterns are interdependent, and the power value harvested from certain environmental sources are spatially correlated, the new power dimension could provide additional information to enhance the spectrum sensing accuracy. In this paper, the Markovian behavior of the primary users is considered, based on which we adopt a hidden input Markov model to specify the primary vs. secondary dynamics in the system. Accordingly, we propose a 2-D spectrum and power (harvested) sensing scheme to improve the primary user detection performance, which is also capable of estimating the primary transmit power level. Theoretical and simulated results demonstrate the effectiveness of the proposed scheme, in term of the performance gain achieved by considering the new power dimension. To the best of our knowledge, this is the first work to jointly consider the spectrum and power dimensions for the cognitive primary user detection problem

Development of the integration of microwave technology with microfluidic systems for sensing and heating

Microfluidics-based Lab-on-a-Chip platforms have drawn ever-increasing attention from both academy and industry due to their advantages for dealing with small volume of fluids and for integrating multiple processes into one platform. These advantages are the direct benefits of miniaturization which also brings challenges, especially in sensing and heating. The challenges are augmented in the context of droplet microfluidics because of their fast motion, curved interface and reduced volume (i.e. pico- to nano-liter). Droplet microfluidics utilizes water-in-oil or oil-in-water droplets that can be generated in microchannel networks at kHz rates as mobilized test tubes. It presents tremendous potential to serve as a tool for high throughput analysis that are in high demand in many areas such as material synthesis, life science research, pharmaceutical industry and environmental monitoring. Many applications require temperature control and both fundamental and applied research need droplet sensing to assist in understanding droplet motion and developing techniques for manipulating droplets. Microwave sensing offers unique advantages by differentiating materials based on their electrical properties at high speeds. Moreover, it enables simultaneous heating of individual droplets. Previous studies demonstrated the potential of microwave resonator for point of care (POC) applications and for simultaneous sensing and heating. However, neither of them has yet be fully realized. In addition to the technical challenges such as the use of bulky and expensive vector network analyzer (VNA) for sensing that limits the potential for POC applications, fundamental understanding of microwave heating and its coupling with droplet microfluidics is lacking. This thesis is designed to fill the gap with the ultimate goal of enhancing droplet microfluidics as an enabling tool for a wide range of applications by realizing the full potential of microwave sensing and heating. With the goal of maximizing the capacity of droplet microfluidics serving as an enabling tool for many applications, this thesis focuses on exploring microwave sensing and heating for droplet microfluidics. The thesis started with the investigation of the coupling between microwave heating and droplet motion to shine light on the mechanism of microwave heating induced droplet mixing. Followed the improved understanding of microwave heating, on-demand droplet generation via microwave heating was explored and demonstrated. To realize simultaneous sensing and heating which is powerful for droplet microfluidics, two resonators need to be considered and the primary concern for two resonators in a single microfluidic chip is the crosstalk between the two resonators. The third chapter was designed to investigate the fundamental challenges of integrating two resonators within a typical microfluidic device footprint. Finally, a POC application of microwave sensing is demonstrated for real time detecting lead in drinking water system which has been one of the crisis raised recently

Online Container Scheduling for Low-Latency IoT Services in Edge Cluster Upgrade: A Reinforcement Learning Approach

In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Frequent upgrades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers and upgrade edge clusters to minimize the impact on running tasks. In this paper, we propose a low-latency container scheduling algorithm for edge cluster upgrades. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm to address this problem, considering the unique characteristics of MEC. 3) Experimental results demonstrate that our algorithm reduces total task latency by approximately 27\% compared to baseline algorithms

Heterogeneous network policy enforcement in data centers

With the emergence of network function virtualization, data center start to deploy a variety of network function boxes (NFBs) in both physical and virtual form factors in order to combines inherent efficiency offered by physical NFBs with the agility and flexibility of virtual ones. However, existing schemes are limited to exclusively consider physical or virtual NFBs, which may reduce the performance efficiency of services running atop. In this paper, we propose a Heterogeneous NetwOrk Policy Enforcement scheme (HOPE) to overcome these challenges. An efficient algorithm that can closely approximate optimal latencywise NF service chaining is proposed. The experimental results have also shown that HOPE can outperform greedy algorithm by 25% in terms of network latency and is 56x more efficient than naive depth-first search algorithm

Enforcing network policy in heterogeneous network function box environment

Data center operators deploy a variety of both physical and virtual network functions boxes (NFBs) to take advantages of inherent efficiency offered by physical NFBs with the agility and flexibility of virtual ones. However, such heterogeneity faces great challenges in correct, efficient and dynamic network policy implementation because, firstly, existing schemes are limited to exclusively physical or virtual NFBs and not a mix, and secondly, NFBs can co-exist at various locations in the network as a result of emerging technologies such as Software Defined Networking (SDN) and Network Function Virtualization (NFV). In this paper, we propose a Heterogeneous netwOrk pOlicy enforCement scheme (HOOC) to overcome these challenges. We first formulate and model HOOC, which is shown be to NP-Hard by reducing from the Multiple Knapsack Problem (MKP). We then propose an efficient online algorithm that can achieve optimal latency-wise NF service chaining amongst heterogenous NFBs. In addition, we also provide a greedy algorithm when operators prefer smaller run-time than optimality. Our simulation results show that HOOC is efficient and scalable whilst testbed implementation demonstrates that HOOC can be easily deployed in the data center environments

TCon: A transparent congestion control deployment platform for optimizing WAN transfers

Nowadays, many web services (e.g., cloud storage) are deployed inside datacenters and may trigger transfers to clients through WAN. TCP congestion control is a vital component for improving the performance (e.g., latency) of these services. Considering complex networking environment, the default congestion control algorithms on servers may not always be the most efficient, and new advanced algorithms will be proposed. However, adjusting congestion control algorithm usually requires modification of TCP stacks of servers, which is difficult if not impossible, especially considering different operating systems and configurations on servers. In this paper, we propose TCon, a light-weight, flexible and scalable platform that allows administrators (or operators) to deploy any appropriate congestion control algorithms transparently without making any changes to TCP stacks of servers. We have implemented TCon in Open vSwitch (OVS) and conducted extensive test-bed experiments by transparently deploying BBR congestion control algorithm over TCon. Test-bed results show that the BBR over TCon works effectively and the performance stays close to its native implementation on servers, reducing latency by 12.76% on average

PLAN: Joint policy- and network-aware VM management for cloud data centers

Policies play an important role in network configuration and therefore in offering secure and high performance services especially over multi-tenant Cloud Data Center (DC) environments. At the same time, elastic resource provisioning through virtualization often disregards policy requirements, assuming that the policy implementation is handled by the underlying network infrastructure. This can result in policy violations, performance degradation and security vulnerabilities. In this paper, we define PLAN, a PoLicy-Aware and Network-aware VM management scheme to jointly consider DC communication cost reduction through Virtual Machine (VM) migration while meeting network policy requirements. We show that the problem is NP-hard and derive an efficient approximate algorithm to reduce communication cost while adhering to policy constraints. Through extensive evaluation, we show that PLAN can reduce topology-wide communication cost by 38 percent over diverse aggregate traffic and configuration policies

A fine-grained and transparent congestion control enforcement scheme

In practice, a single TCP congestion control is often used to handle all TCP connections on a Web server, e.g., Cubic for Linux by default. Considering complex and ever-changing networking environment, the default congestion control algorithm may not always be the most suitable one. Adjusting congestion control usually to meet different networking scenarios requires modification of servers' TCP stacks. This is difficult, if not impossible, due to various operating systems and different configurations on the servers. In this paper, we propose Mystique, a light-weight and flexible scheme that allows administrators (or operators) to deploy any congestion control schemes transparently without changing existing TCP stacks on servers. We have implemented Mystique in Open vSwitch (OVS) and conducted extensive test-bed experiments in public cloud environments. We have extensively evaluated Mystique and the results have demonstrated that it is able to effectively adapt to varying network conditions, and can always employ the most suitable congestion control for each TCP connection. Mystique can significantly reduce latency by up to 37.8% in comparison with other congestion controls