Design, analysis, and implementation of a novel low complexity scheduler for joint resource allocation

Over the past decade, the problem of fair bandwidth allocation among contending traffic flows on a link has been extensively researched. However, as these flows traverse a computer network, they share different kinds of resources (e.g., links, buffers, router CPU). The ultimate goal should hence be overall fairness in the allocation of multiple resources rather than a specific resource. Moreover, conventional resource scheduling algorithms depend strongly upon the assumption of prior knowledge of network parameters and cannot handle variations or lack of information about these parameters. In this paper, we present a novel scheduler called the Composite Bandwidth and CPU Scheduler (CBCS), which jointly allocates the fair share of the link bandwidth as well as processing resource to all competing flows. CBCS also uses a simple and adaptive online prediction scheme for reliably estimating the processing times of the incoming data packets. Analytically, we prove that CBCS is efficient, with a per-packet work complexity of O(1). Finally, we present simulation results and experimental outcomes from a real-world implementation of CBCS on an Intel IXP 2400 network processor. Our results highlight the improved performance achieved by CBCS and demonstrate the ease with which it can be implemented on off-the-shelf hardware. © 2007 IEEE

Recharging of flying base stations using airborne RF energy sources

This paper presents a new method for recharging flying base stations, carried by Unmanned Aerial Vehicles (UAVs), using wireless power transfer from dedicated, airborne, Radio Frequency (RF) energy sources. In particular, we study a system in which UAVs receive wireless power without being disrupted from their regular trajectory. The optimal placement of the energy sources are studied so as to maximize received power from the energy sources by the receiver UAVs flying with a linear trajectory over a square area. We find that for our studied scenario of two UAVs, if an even number of energy sources are used, placing them in the optimal locations maximizes the total received power, while achieving fairness among the UAVs. However, in the case of using an odd number of energy sources, we can either maximize the total received power, or achieve fairness, but not both at the same time. Numerical results show that placing the energy sources at the suggested optimal locations results in significant power gain compared to nonoptimal placements

Design, analysis, and implementation of a novel multiple resource scheduler

Over the past decade, the problem of achieving fair bandwidth allocation on a link shared by multiple traffic flows has been extensively researched. However, as these flows traverse a computer network, they share many different kinds of resources, such as links, buffers, and router CPU. The ultimate goal should hence be overall fairness in the allocation of multiple resources rather than a single specific resource such as link bandwidth. In this paper, we present a novel scheduler, called prediction-based composite fair queuing (PCFQ), which jointly allocates the fair share of the link bandwidth and processing resources to all competing flows. We derive the worst-case delay bound, the work complexity, and the relative fairness bound for the PCFQ scheduler and show that it outperforms a system consisting of separate bandwidth and CPU schedulers. We further present simulation results which illustrate the improved performance characteristics achieved by PCFQ. We also demonstrate that our composite scheduler can be easily implemented on an off-the-shelf network processor such as the Intel IXP 2400. Experimental results from the IXP 2400 implementation highlight the effectiveness and high performance of this algorithm in a real-world system. © 2007 IEEE

Enabling efficient and high quality zooming for online video streaming using edge computing

High quality zooming function for online video streaming using cloud content servers remains a challenge due to the intertwined relationships among video chunk lengths, viewer's fast changing Region of Interest (RoI), and network latency. It is possible to utilize tiled Video technique and store picture tiles in separate files with their unique URLs on the media server with smaller chunk sizes, however it introduces a significant burden on the network core due to increased total video length contributed by combined non-video bits from too many smaller chunks. To overcome this, in this paper we propose the use of edge computing to achieve high quality zooming function for video steaming. Our proposal includes the system architecture using Tiled-DASH (T-DASH) video encoding on edge servers, and a novel ROI prediction method combining three different prediction models: online, offline and object-level prediction models on the client side. Our evaluations show that a high level of ROI prediction accuracy is achieved by our approach, fulfilling a core condition for making the zooming function a reality

AETD: An application-aware, energy-efficient trajectory design for flying base stations

Recent developments in consumer Unmanned Aerial Vehicles (UAVs) technology have created unprecedented opportunities for their applications in various civil domains. These ubiquitous vehicles of different shapes and sizes with easy and user-friendly configurations are favorite choices for providing different services such as wireless communications, emergency medical deliveries, disaster handling and many more. However, the limited battery life of UAVs pose a challenge to their service continuity, thus mechanisms to extend the UAVs’ battery life are required. For service delivery, UAVs consume energy for mechanical functionalities as well as for communicating with other network nodes. To reduce the mechanical energy consumption, the shortest flying path can be considered while selecting a right radio frequency level for UAV’s communications can effectively reduce the remaining required energy. In this paper, we analyze energy requirements for providing different communication services using different radio frequency bands. We propose an application-aware, energy-efficient trajectory design method which dynamically adapts the UAV’s communication radio frequency to the requested services in the best flying trajectory while considering service level priorities as well. Our simulation results show that our approach can save up to 14% energy while providing even higher Quality of Service (QoS) in a given trajectory

Trajectory optimization of flying energy sources using Q-Learning to recharge hotspot UAVs

Despite the increasing popularity of commercial usage of UAVs or drone-delivered services, their dependence on the limited-capacity on-board batteries hinders their flighttime and mission continuity. As such, developing in-situ power transfer solutions for topping-up UAV batteries have the potential to extend their mission duration. In this paper, we study a scenario where UAVs are deployed as base stations (UAV-BS) providing wireless Hotspot services to the ground nodes, while harvesting wireless energy from flying energy sources. These energy sources are specialized UAVs (Charger or transmitter UAVs, tUAVs), equipped with wireless power transmitting devices such as RF antennae. tUAVs have the flexibility to adjust their flight path to maximize energy transfer. With the increasing number of UAV-BSs and environmental complexity, it is necessary to develop an intelligent trajectory selection procedure for tUAVs so as to optimize the energy transfer gain. In this paper, we model the trajectory optimization of tUAVs as a Markov Decision Process (MDP) problem and solve it using Q-Learning algorithm. Simulation results confirm that the Q-Learning based optimized trajectory of the tUAVs outperforms two benchmark strategies, namely random path planning and static hovering of the tUAVs

Poster abstract: A QoS-Aware, energy-efficient trajectory optimization for UAV base stations using Q-Learning

Next generation mobile networks have proposed the integration of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes with potentially varying QoS requirements. However, the dependence on the on-board, limited-capacity battery of the UAV-BS limits their service continuity. While conserving energy is important, meeting the QoS requirements of the ground nodes is equally important. We present an energy-efficient trajectory optimization for the UAV-BS while satisfying QoS requirements. We model the trajectory optimization as an MDP problem and solve it using Q-Learning. Simulation results reveal that our proposed algorithm decreases the average energy consumption by nearly 55% compared to a randomly-served algorithm