6 research outputs found
Experimental Evaluation of Large Scale WiFi Multicast Rate Control
WiFi multicast to very large groups has gained attention as a solution for
multimedia delivery in crowded areas. Yet, most recently proposed schemes do
not provide performance guarantees and none have been tested at scale. To
address the issue of providing high multicast throughput with performance
guarantees, we present the design and experimental evaluation of the Multicast
Dynamic Rate Adaptation (MuDRA) algorithm. MuDRA balances fast adaptation to
channel conditions and stability, which is essential for multimedia
applications. MuDRA relies on feedback from some nodes collected via a
light-weight protocol and dynamically adjusts the rate adaptation response
time. Our experimental evaluation of MuDRA on the ORBIT testbed with over 150
nodes shows that MuDRA outperforms other schemes and supports high throughput
multicast flows to hundreds of receivers while meeting quality requirements.
MuDRA can support multiple high quality video streams, where 90% of the nodes
report excellent or very good video quality
๋ฌด์ ๋ ๋น๋์ค ๋ฉํฐ์บ์คํธ์ ๋ฌธ์ ๋ฐ๊ฒฌ ๋ฐ ์ฑ๋ฅ ํฅ์ ๊ธฐ๋ฒ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ, 2017. 8. ์ต์ฑํ.Video multicast, streaming real-time videos via multicast, over wireless local area network (WLAN) has been considered a promising solution to share common venue-specific videos. By virtue of the nature of the wireless broadcast medium, video multicast basically enables scale-free video delivery, i.e., it can deliver a common video with the fixed amount of wireless resource regardless of the number of receivers. However, video multicast has not been widely enjoyed in our lives due to three major challenges: (1) power saving-related problem, (2) low reliability and efficiency, and (3) limited coverage.
In this dissertation, we consider three research topics, i.e., (1) identification of practical issues with multicast power saving, (2) physical (PHY) rate and forward erasure correction code (FEC) rate adaptation over a single-hop network, and (3) multi-hop multicast, which deal with the three major challenges, respectively.
Firstly, video multicast needs to be reliably delivered to power-saving stations, given that many portable devices are battery-powered. Accordingly, we investigate the impact of multicast power saving, and address two practical issues related with the multicast power saving. From the measurement with several commercial WLAN devices, we observe that many devices are not standard compliant, thus making video multicast performance severely degraded. We categorize such standard incompliant malfunctions that can result in significant packet losses. We also figure out a coexistence
problem between video multicast and voice over Internet protocol (VoIP) when video receivers runs in power saving mode (PSM). The standard-compliant power save delivery of multicast deteriorates the VoIP performance in the same WLAN. We analyze the VoIP packet losses due to the coexistence problem, and propose a new power save delivery scheme to resolve the problem. We further implement the proposed scheme with an open source device driver, and our measurement results demonstrate that the proposed scheme significantly enhances the VoIP performance without sacrificing the video multicast performance.
Second, multi-PHY rate FEC-applied wireless multicast enables reliable and efficient video multicast with intelligent selection of PHY rate and FEC rate. The optimal PHY/FEC rates depend on the cause of the packet losses. However, previous approaches select the PHY/FEC rates by considering only channel errors even when interference is also a major source of packet losses.We propose InFRA, an interference-aware PHY/FEC rate adaptation framework that (1) infers the cause of the packet losses based on received signal strength indicator (RSSI) and cyclic redundancy check (CRC) error notifications, and (2) determines the PHY/FEC rates based on the cause of packet losses. Our prototype implementation with off-the-shelf chipsets demonstrates that InFRA enhances the multicast delivery under various network scenarios. InFRA enables 2.3x and 1.8x more nodes to achieve a target video packet loss rate with a contention interferer and a hidden interferer, respectively, compared with the state-of-theart
PHY/FEC rate adaptation scheme. To the best of our knowledge, InFRA is the first work to take the impact of interference into account for the PHY/FEC rate adaptation.
Finally, collaborative relaying that enables selected receiver nodes to relay the received
packets from source node to other nodes enhances service coverage, reliability, and efficiency of video multicast. The intelligent selection of sender nodes (source and relays) and their transmission parameters (PHY rate and the number of packets to send) is the key to optimize the performance. We propose EV-CAST, an interference
and energy-aware video multicast system using collaborative relays, which entails online network management based on interference-aware link characterization, an algorithm for joint determination of sender nodes and transmission parameters, and polling-based relay protocol. In order to select most appropriate set of the relay nodes, EV-CAST considers interference, battery status, and spatial reuse, as well as
other factors accumulated over last decades. Our prototype-based measurement results demonstrate that EV-CAST outperforms the state-of-the-art video multicast schemes.
In summary, from Chapter 2 to Chapter 4, the aforementioned three pieces of the research work, i.e., identification of power saving-related practical issues, InFRA for interference-resilient single-hop multicast, and EV-CAST for efficient multi-hop multicast, will be presented, respectively.1 Introduction 1
1.1 Video Multicast over WLAN 1
1.2 Overview of Existing Approaches 4
1.2.1 Multicast Power Saving 4
1.2.2 Reliability and Efficiency Enhancement 4
1.2.3 Coverage Extension 5
1.3 Main Contributions 7
1.3.1 Practical Issues with Multicast Power Saving 7
1.3.2 Interference-aware PHY/FEC Rate Adaptation 8
1.3.3 Energy-aware Multi-hop Multicast 9
1.4 Organization of the Dissertation 10
2 Practical Issues with Multicast Power Saving 12
2.1 Introduction 12
2.2 Multicast & Power Management Operation in IEEE 802.11 14
2.3 Inter-operability Issue 15
2.3.1 Malfunctions of Commercial WLAN Devices 17
2.3.2 Performance Evaluation 20
2.4 Coexistence Problem of Video Multicast and VoIP 21
2.4.1 Problem Statement 21
2.4.2 Problem Identification: A Measurement Study 23
2.4.3 Packet Loss Analysis 27
2.4.4 Proposed Scheme 32
2.4.5 Performance Evaluation 33
2.5 Summary 37
3 InFRA: Interference-Aware PHY/FEC Rate Adaptation for Video Multicast over WLAN 39
3.1 Introduction 39
3.2 Related Work 42
3.2.1 Reliable Multicast Protocol 42
3.2.2 PHY/FEC rate adaptation for multicast service 44
3.2.3 Wireless Video Transmission 45
3.2.4 Wireless Loss Differentiation 46
3.3 Impact of Interference on Multi-rate FEC-applied Multicast 46
3.3.1 Measurement Setup 47
3.3.2 Measurement Results 47
3.4 InFRA: Interference-aware PHY/FEC Rate Adaptation Framework 49
3.4.1 Network Model and Objective 49
3.4.2 Overall Architecture 50
3.4.3 FEC Scheme 52
3.4.4 STA-side Operation 53
3.4.5 AP-side Operation 61
3.4.6 Practical Issues 62
3.5 Performance Evaluation 65
3.5.1 Measurement Setup 66
3.5.2 Small Scale Evaluation 67
3.5.3 Large Scale Evaluation 70
3.6 Summary 74
4 EV-CAST: Interference and Energy-aware Video Multicast Exploiting Collaborative Relays 75
4.1 Introduction 75
4.2 Factors for Sender Node and Transmission Parameter Selection 78
4.3 EV-CAST: Interference and Energy-aware Multicast Exploiting Collaborative Relays 80
4.3.1 Network Model and Objective 80
4.3.2 Overview 81
4.3.3 Network Management 81
4.3.4 Interference and Energy-aware Sender Nodes and Transmission Parameter Selection (INFER) Algorithm 87
4.3.5 Assignment, Polling, and Re-selection of Relays 93
4.3.6 Discussion 95
4.4 Evaluation 96
4.4.1 Measurement Setup 96
4.4.2 Micro-benchmark 98
4.4.3 Macro-benchmark 103
4.5 Related Work 105
4.5.1 Multicast Opportunistic Routing 105
4.5.2 Multicast over WLAN 106
4.6 Summary 106
5 Conclusion 108
5.1 Research Contributions 108
5.2 Future Research Directions 109
Abstract (In Korean) 121Docto
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Measurement-Driven Algorithm and System Design for Wireless and Datacenter Networks
The growing number of mobile devices and data-intensive applications pose unique challenges for wireless access networks as well as datacenter networks that enable modern cloud-based services. With the enormous increase in volume and complexity of traffic from applications such as video streaming and cloud computing, the interconnection networks have become a major performance bottleneck. In this thesis, we study algorithms and architectures spanning several layers of the networking protocol stack that enable and accelerate novel applications and that are easily deployable and scalable. The design of these algorithms and architectures is motivated by measurements and observations in real world or experimental testbeds.
In the first part of this thesis, we address the challenge of wireless content delivery in crowded areas. We present the AMuSe system, whose objective is to enable scalable and adaptive WiFi multicast. AMuSe is based on accurate receiver feedback and incurs a small control overhead. This feedback information can be used by the multicast sender to optimize multicast service quality, e.g., by dynamically adjusting transmission bitrate. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes which periodically send information about the channel quality to the multicast sender. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSe's feedback to optimally tune the physical layer multicast rate. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications.
We implemented the AMuSe system on the ORBIT testbed and evaluated its performance in large groups with approximately 200 WiFi nodes. Our extensive experiments demonstrate that AMuSe can provide accurate feedback in a dense multicast environment. It outperforms several alternatives even in the case of external interference and changing network conditions. Further, our experimental evaluation of MuDRA on the ORBIT testbed shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of nodes while meeting quality requirements. As an example application, MuDRA can support multiple high quality video streams, where 90% of the nodes report excellent or very good video quality.
Next, we specifically focus on ensuring high Quality of Experience (QoE) for video streaming over WiFi multicast. We formulate the problem of joint adaptation of multicast transmission rate and video rate for ensuring high video QoE as a utility maximization problem and propose an online control algorithm called DYVR which is based on Lyapunov optimization techniques. We evaluated the performance of DYVR through analysis, simulations, and experiments using a testbed composed of Android devices and o the shelf APs. Our evaluation shows that DYVR can ensure high video rates while guaranteeing a low but acceptable number of segment losses, buffer underflows, and video rate switches.
We leverage the lessons learnt from AMuSe for WiFi to address the performance issues with LTE evolved Multimedia Broadcast/Multicast Service (eMBMS). We present the Dynamic Monitoring (DyMo) system which provides low-overhead and real-time feedback about eMBMS performance. DyMo employs eMBMS for broadcasting instructions which indicate the reporting rates as a function of the observed Quality of Service (QoS) for each UE. This simple feedback mechanism collects very limited QoS reports which can be used for network optimization. We evaluated the performance of DyMo analytically and via simulations. DyMo infers the optimal eMBMS settings with extremely low overhead, while meeting strict QoS requirements under different UE mobility patterns and presence of network component failures.
In the second part of the thesis, we study datacenter networks which are key enablers of the end-user applications such as video streaming and storage. Datacenter applications such as distributed file systems, one-to-many virtual machine migrations, and large-scale data processing involve bulk multicast flows. We propose a hardware and software system for enabling physical layer optical multicast in datacenter networks using passive optical splitters. We built a prototype and developed a simulation environment to evaluate the performance of the system for bulk multicasting. Our evaluation shows that the optical multicast architecture can achieve higher throughput and lower latency than IP multicast and peer-to-peer multicast schemes with lower switching energy consumption.
Finally, we study the problem of congestion control in datacenter networks. Quantized Congestion Control (QCN), a switch-supported standard, utilizes direct multi-bit feedback from the network for hardware rate limiting. Although QCN has been shown to be fast-reacting and effective, being a Layer-2 technology limits its adoption in IP-routed Layer 3 datacenters. We address several design challenges to overcome QCN feedback's Layer- 2 limitation and use it to design window-based congestion control (QCN-CC) and load balancing (QCN-LB) schemes. Our extensive simulations, based on real world workloads, demonstrate the advantages of explicit, multi-bit congestion feedback, especially in a typical environment where intra-datacenter traffic with short Round Trip Times (RTT: tens of s) run in conjunction with web-facing traffic with long RTTs (tens of milliseconds)
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Algorithms and Experimentation for Future Wireless Networks: From Internet-of-Things to Full-Duplex
Future and next-generation wireless networks are driven by the rapidly growing wireless traffic stemming from diverse services and applications, such as the Internet-of-Things (IoT), virtual reality, autonomous vehicles, and smart intersections. Many of these applications require massive connectivity between IoT devices as well as wireless access links with ultra-high bandwidth (Gbps or above) and ultra-low latency (10ms or less). Therefore, realizing the vision of future wireless networks requires significant research efforts across all layers of the network stack. In this thesis, we use a cross-layer approach and focus on several critical components of future wireless networks including IoT systems and full-duplex (FD) wireless, and on experimentation with advanced wireless technologies in the NSF PAWR COSMOS testbed.
First, we study tracking and monitoring applications in the IoT and focus on ultra-low-power energy harvesting networks. Based on realistic hardware characteristics, we design and optimize Panda, a centralized probabilistic protocol for maximizing the neighbor discovery rate between energy harvesting nodes under a power budget. Via testbed evaluation using commercial off-the-shelf energy harvesting nodes, we show that Panda outperforms existing protocols by up to 3x in terms of the neighbor discovery rate. We further explore this problem and consider a general throughput maximization problem among a set of heterogeneous energy-constrained ultra-low-power nodes. We analytically identify the theoretical fundamental limits of the rate at which data can be exchanged between these nodes, and design the distributed probabilistic protocol, EconCast, which approaches the maximum throughput in the limiting sense. Performance evaluations of EconCast using both simulations and real-world experiments show that it achieves up to an order of magnitude higher throughput than Panda and other known protocols.
We then study FD wireless - simultaneous transmission and reception at the same frequency - a key technology that can significantly improve the data rate and reduce communication latency by employing self-interference cancellation (SIC). In particular, we focus on enabling FD on small-form-factor devices leveraging the technique of frequency-domain equalization (FDE). We design, model, and optimize the FDE-based RF canceller, which can achieve >50dB RF SIC across 20MHz bandwidth, and experimentally show that our prototyped FD radios can achieve a link-level throughput gain of 1.85-1.91x. We also focus on combining FD with phased arrays, employing optimized transmit and receive beamforming, where the spatial degrees of freedom in multi-antenna systems are repurposed to achieve wideband RF SIC. Moving up in the network stack, we study heterogeneous networks with half-duplex and FD users, and develop the novel Hybrid-Greedy Maximum Scheduling (H-GMS) algorithm, which achieves throughput optimality in a distributed manner. Analytical and simulation results show that H-GMS achieves 5-10x better delay performance and improved fairness compared with state-of-the-art approaches.
Finally, we described experimentation and measurements in the city-scale COSMOS testbed being deployed in West Harlem, New York City. COSMOS' key building blocks include software-defined radios, millimeter-wave radios, a programmable optical network, and edge cloud, and their convergence will enable researchers to remotely explore emerging technologies in a real world environment. We provide a brief overview of the testbed and focus on experimentation with advanced technologies, including the integrating of open-access FD radios in the testbed and a pilot study on converged optical-wireless x-haul networking for cloud radio access networks (C-RANs). We also present an extensive 28GHz channel measurements in the testbed area, which is a representative dense urban canyon environment, and study the corresponding signal-to-noise ratio (SNR) coverage and achievable data rates. The results of this part helped drive and validate the design of the COSMOS testbed, and can inform further deployment and experimentation in the testbed.
In this thesis, we make several theoretical and experimental contributions to ultra-low-power energy harvesting networks and the IoT, and FD wireless. We also contribute to the experimentation and measurements in the COSMOS advanced wireless testbed. We believe that these contributions are essential to connect fundamental theory to practical systems, and ultimately to real-world applications, in future wireless networks
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Learning for Network Applications and Control
The emergence of new Internet applications and technologies have resulted in an increased complexity as well as a need for lower latency, higher bandwidth, and increased reliability. This ultimately results in an increased complexity of network operation and management. Manual management is not sufficient to meet these new requirements.
There is a need for data driven techniques to advance from manual management to autonomous management of network systems. One such technique, Machine Learning (ML), can use data to create models from hidden patterns in the data and make autonomous modifications. This approach has shown significant improvements in other domains (e.g., image recognition and natural language processing). The use of ML, along with advances in programmable control of Software- Defined Networks (SDNs), will alleviate manual network intervention and ultimately aid in autonomous network operations. However, realizing a data driven system that can not only understand what is happening in the network but also operate autonomously requires advances in the networking domain, as well as in ML algorithms.
In this thesis, we focus on developing ML-based network architectures and data driven net- working algorithms whose objective is to improve the performance and management of future networks and network applications. We focus on problems spanning across the network protocol stack from the application layer to the physical layer. We design algorithms and architectures that are motivated by measurements and observations in real world or experimental testbeds.
In Part I we focus on the challenge of monitoring and estimating user video quality of experience (QoE) of encrypted video traffic for network operators. We develop a system for REal-time QUality of experience metric detection for Encrypted Traffic, Requet. Requet uses a detection algorithm to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a random forest ML model to predict QoE metrics. We evaluate Requet on a YouTube dataset we collected, consisting of diverse video assets delivered over various WiFi and LTE network conditions. We then extend Requet, and present a study on YouTube TV live streaming traffic behavior over WiFi and cellular networks covering a 9-month period. We observed pipelined chunk requests, a reduced buffer capacity, and a more stable chunk duration across various video resolutions compared to prior studies of on-demand streaming services. We develop a YouTube TV analysis tool using chunks statistics detected from the extracted data as input to a ML model to infer user QoE metrics.
In Part II we consider allocating end-to-end resources in cellular networks. Future cellular networks will utilize SDN and Network Function Virtualization (NFV) to offer increased flexibility for network infrastructure operators to utilize network resources. Combining these technologies with real-time network load prediction will enable efficient use of network resources. Specifically, we leverage a type of recurrent neural network, Long Short-Term Memory (LSTM) neural networks, for (i) service specific traffic load prediction for network slicing, and (ii) Baseband Unit (BBU) pool traffic load prediction in a 5G cloud Radio Access Network (RAN). We show that leveraging a system with better accuracy to predict service requirements results in a reduction of operation costs.
We focus on addressing the optical physical layer in Part III. Greater network flexibility through SDN and the growth of high bandwidth services are motivating faster service provisioning and capacity management in the optical layer. These functionalities require increased capacity along with rapid reconfiguration of network resources. Recent advances in optical hardware can enable a dramatic reduction in wavelength provisioning times in optical circuit switched networks. To support such operations, it is imperative to reconfigure the network without causing a drop in service quality to existing users. Therefore, we present a ML system that uses feedforward neural networks to predict the dynamic response of an optically circuit-switched 90-channel multi-hop Reconfigurable Optical Add-Drop Multiplexer (ROADM) network. We show that the trained deep neural network can recommend wavelength assignments for wavelength switching with minimal power excursions. We extend the performance of the ML system by implementing and testing a Hybrid Machine Learning (HML) model, which combines an analytical model with a neural network machine learning model to achieve higher prediction accuracy.
In Part IV, we use a data-driven approach to address the challenge of wireless content delivery in crowded areas. We present the Adaptive Multicast Services (AMuSe) system, whose objective is to enable scalable and adaptive WiFi multicast. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSeโs feedback to optimally tune the physical layer multicast rate. Our experimental evaluation of MuDRA on the ORBIT testbed shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of nodes while meeting quality requirements. We leverage the lessons learned from AMuSe for WiFi and use order statistics to address the performance issues with LTE evolved Multimedia Broadcast/Multicast Service (eMBMS). We present the Dynamic Monitoring (DyMo) system which provides low-overhead and real-time feedback about eMBMS performance to be used for network optimization. We focus on the Quality of Service (QoS) Evaluation module and develop a Two-step estimation algorithm which can efficiently identify the SNR Threshold as a one time estimation. DyMo significantly outperforms alternative schemes based on the Order-Statistics estimation method which relies on random or periodic sampling