QUIC: Better for what and for whom?

Cook, Sarah; Mathieu, Bertrand; Truong, Patrick; Hamchaoui, Isabelle

doi:10.1109/icc.2017.7997281

Cited by 69 publications

(50 citation statements)

References 6 publications

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“…Web browsing includes 4 key actors, namely the enduser, the web browser, the network and the remote web server. While some research work [20,21] describes how to qualify user-experience better, other studies investigate the impact of different Internet protocols on web browsing quality [22,23,24,25,26,27]. Regarding the network, particular interest is given to the upload or download link when investigating bottlenecks [28,29,30] and Naseer and al.…”

Section: Background and Related Workmentioning

confidence: 99%

A 6-month analysis of factors impacting web browsing quality for QoE prediction

2019

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A B S T R A C TWeb browsing is one of the main Internet services, which has largely evolved over the last years. It now includes many different components (images in various formats, css, Javascript, Progressive Web Apps, etc.) and a web page is composed of many different resources provided by many servers located in various places. Knowing how web browsing content is delivered is crucial for better understanding the Quality of Experience (QoE) offered to end-users. Detecting the factors impacting this QoE is important to improve them but also to be able to predict the quality in order to anticipate degradations to it and propose remediations, which is the main goal of our study. For this, we present an analysis of a measurement campaign we have performed during 6 months (July 2018 -January 2019) on the Top 10,000 Alexa websites, which helps to identify the main factors which can impact the quality perceived by end-users. This set of factors has been confirmed by a machine learning process (decision tree), which gives as outputs the set of rules to predict the QoE. The evaluation of our decision tree-based model, on the Top 10,000-15,000 Alexa websites, never accessed before, shows that we can correctly predict the web browsing quality an end-user can get.

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Section: Background and Related Workmentioning

confidence: 99%

A 6-month analysis of factors impacting web browsing quality for QoE prediction

2019

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“…In case of WebSocket, although it is used for real-time pushing of data from a server to a web client and enable persistent connections with simultaneous bidirectional communication, it is not designed for resource constrained devices [21]. QUIC is also rather noteworthy, as a novel transport protocol creating a wave of the new research efforts [38][39][40]. Since QUIC has not been standardized yet, it maybe too early to predict its possible application and impact in IoT based solutions.…”

Section: On Communication Protocolsmentioning

confidence: 99%

A Survey of Communication Protocols for Internet of Things and Related Challenges of Fog and Cloud Computing Integration

et al. 2019

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The fast increment in the number of IoT (Internet of Things) devices is accelerating the research on new solutions to make cloud services scalable. In this context, the novel concept of fog computing as well as the combined fog-to-cloud computing paradigm is becoming essential to decentralize the cloud, while bringing the services closer to the end-system. This paper surveys on the application layer communication protocols to fulfill the IoT communication requirements, and their potential for implementation in fog-and cloud-based IoT systems. To this end, the paper first briefly presents potential protocol candidates, including request-reply and publish-subscribe protocols. After that, the paper surveys these protocols based on their main characteristics, as well as the main performance issues, including latency, energy consumption and network throughput. These findings are thereafter used to place the protocols in each segment of the system (IoT, fog, cloud), and thus opens up the discussion on their choice, interoperability and wider system integration. The survey is expected to be useful to system architects and protocol designers when choosing the communication protocols in an integrated IoT-to-fog-to-cloud system architecture. Continuous innovations in hardware, software and connection solutions in the last decade have lead to the expansion of the Internet of Things (IoT) with the number of connected devices growing by the day [1] [2]. The huge amount of data generated by these devices require to find a proper system architecture able to both process and store all the data. While cloud-based architectures are being currently used for that purpose, the new fog computing paradigm is envisioned to scale and optimize the IoT infrastructures [3]. Examples of the cloud-based IoT solutions have been proposed in [4], [5], [6] and a detailed analysis of properties for IoT cloud providers has been conducted in [7]. These studies have shown that cloud computing has the potential to satisfy many IoT requirements, such as monitoring of services, powerful processing of sensor data streams and visualization tasks. On the other hand, fog-based solutions are suited to address real-time processing, fast data response, and latency issues, thus extending the cloud capabilities closer to the edge of the network [8]. Among many factors that will determine the performance in a combined IoT, fog and cloud computing paradigm, the application layer communication, which in turn depends on the selected communication protocols, is one of the main ones.Despite the popularity and wide spread usage of HTTP, the currently used protocols in various domains of IoT, fog and cloud domains are de-facto fragmented with many different solutions. This is due to the different requirements and areas that IoT needs to cover, combining the functionalities of sensors, actuators and computing power with security, connectivity and a myriad of other features. As a result, there is no common agreement on the reference architecture or adopted standards of co...

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“…The authors in [48] show that QUIC outperforms TCP with respect to Page Load Time (PLT) in different desktop user network conditions such as added delay and loss, variable bandwidth, and mobile environment. The studies of [81] show that QUIC outperforms TLS when used in unstable networks such as WiFi [82]. Specifically, the authors performed experiments to measure PLT for YouTube traffic with different delays (0ms, 50ms, 100ms and 200ms).…”

Section: B Quicmentioning

confidence: 99%

Implementation and analysis of QUIC for MQTT

Kumar

Dezfouli

2019

Computer Networks

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Transport and security protocols are essential to ensure reliable and secure communication between two parties. For IoT applications, these protocols must be lightweight, since IoT devices are usually resource constrained. Unfortunately, the existing transport and security protocols -namely TCP/TLS and UDP/DTLS -fall short in terms of connection overhead, latency, and connection migration when used in IoT applications. In this paper, after studying the root causes of these shortcomings, we show how utilizing QUIC in IoT scenarios results in a higher performance. Based on these observations, and given the popularity of MQTT as an IoT application layer protocol, we integrate MQTT with QUIC. By presenting the main APIs and functions developed, we explain how connection establishment and message exchange functionalities work. We evaluate the performance of MQTTw/QUIC versus MQTTw/TCP using wired, wireless, and long-distance testbeds. Our results show that MQTTw/QUIC reduces connection overhead in terms of the number of packets exchanged with the broker by up to 56%. In addition, by eliminating half-open connections, MQTTw/QUIC reduces processor and memory usage by up to 83% and 50%, respectively. Furthermore, by removing the head-of-line blocking problem, delivery latency is reduced by up to 55%. We also show that the throughput drops experienced by MQTTw/QUIC when a connection migration happens is considerably lower than that of MQTTw/TCP.

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QUIC: Better for what and for whom?

Cited by 69 publications

References 6 publications

A 6-month analysis of factors impacting web browsing quality for QoE prediction

A 6-month analysis of factors impacting web browsing quality for QoE prediction

A Survey of Communication Protocols for Internet of Things and Related Challenges of Fog and Cloud Computing Integration

Implementation and analysis of QUIC for MQTT

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