dslab-epfl · zeinabshmeis · Dec 5, 2023
diff --git a/bib/pubs.bib b/bib/pubs.bib
@@ -1,3 +1,76 @@
+@inproceedings{alvarez2023qoe,
+	author = {Alvarez, Catalina and Argyraki, Katerina},
+	title = {Learning a QoE Metric from Social Media and Gaming Footage},
+	year = {2023},
+	isbn = {9798400704154},
+	publisher = {Association for Computing Machinery},
+	address = {New York, NY, USA},
+	url = {https://doi.org/10.1145/3626111.3628208},
+	doi = {10.1145/3626111.3628208},
+	abstract = {Defining a universal metric for Quality of Experience (QoE) is notoriously hard due to the complex relationship between low-level performance metrics and user satisfaction. The most common metric, the Mean Opinion Score (MOS), has well-known biases and inconsistency issues. We propose an alternative that leverages (a) social-media comments on network performance and (b) streaming footage that includes performance numbers. We argue that our proposal is feasible for online gaming, and it may apply to other applications in the near future. We discuss its potential to enable a direct mapping from low-level performance metrics to accurate QoE scores---the golden standard for assessing user satisfaction.},
+	booktitle = {Proceedings of the 22nd ACM Workshop on Hot Topics in Networks},
+	pages = {117–123},
+	numpages = {7},
+	keywords = {Quality of Experience, Social Media, Internet Measurement},
+	location = {<conf-loc>, <city>Cambridge</city>, <state>MA</state>, <country>USA</country>, </conf-loc>},
+	series = {HotNets '23}
+}
+
+@inproceedings{abdullah2023caching,
+	author = {Abdullah, Muhammad and Nikolopoulos, Pavlos and Argyraki, Katerina},
+	title = {Caching and Neutrality},
+	year = {2023},
+	isbn = {9798400704154},
+	publisher = {Association for Computing Machinery},
+	address = {New York, NY, USA},
+	url = {https://doi.org/10.1145/3626111.3628211},
+	doi = {10.1145/3626111.3628211},
+	abstract = {We are used to defining network neutrality as absence of traffic differentiation, like policing or shaping. These mechanisms, however, are often not what determines end-users' quality of experience (QoE). Most content today is accessed through edge caches, operated by cloud providers, but located near or inside the end-user's Internet Service Provider (ISP). Hence, the end-users' QoE is often determined by the interplay between the caching system (controlled by the cloud provider) and the network between edge cache and end-user (controlled by the eyeball ISP). So, we argue that an obvious point where differentiation may occur, and where transparency and neutrality may be desirable is the caching system; and that we (as a community) should perhaps consider notions of neutrality that capture the connection between caching and QoE.},
+	booktitle = {Proceedings of the 22nd ACM Workshop on Hot Topics in Networks},
+	pages = {63–69},
+	numpages = {7},
+	keywords = {Network neutrality, Edge caching},
+	location = {<conf-loc>, <city>Cambridge</city>, <state>MA</state>, <country>USA</country>, </conf-loc>},
+	series = {HotNets '23}
+}
+
+@inproceedings{alvarez2023twitch,
+	author = {Alvarez, Catalina and Argyraki, Katerina},
+	title = {Using Gaming Footage as a Source of Internet Latency Information},
+	year = {2023},
+	isbn = {9798400703829},
+	publisher = {Association for Computing Machinery},
+	address = {New York, NY, USA},
+	url = {https://doi.org/10.1145/3618257.3624816},
+	doi = {10.1145/3618257.3624816},
+	abstract = {Keeping track of Internet latency is a classic measurement problem. Open measurement platforms like RIPE Atlas are a great solution, but they also face challenges: preventing network overload that may result from uncontrolled active measurements, and maintaining the involved devices, which are typically contributed by volunteers and non-profit organizations, and tend to lag behind the state of the art in terms of features and performance. We explore gaming footage as a new source of real-time, publicly available, passive latency measurements, which have the potential to complement open measurement platforms. We show that it is feasible to mine this source of information by presenting Tero, a system that continuously downloads gaming footage from the Twitch streaming platform, extracts latency measurements from it, and converts them to latency distributions per geographical location. Our data-sets and source code are publicly available at https://nal-epfl.github.io/tero-project.},
+	booktitle = {Proceedings of the 2023 ACM on Internet Measurement Conference},
+	pages = {606–626},
+	numpages = {21},
+	keywords = {passive measurement, social media, internet performance},
+	location = {, Montreal QC, Canada, },
+	series = {IMC '23}
+}
+
+@inproceedings{shmeis2023localization,
+	author = {Shmeis, Zeinab and Abdullah, Muhammad and Nikolopoulos, Pavlos and Argyraki, Katerina and Choffnes, David and Gill, Phillipa},
+	title = {Localizing Traffic Differentiation},
+	year = {2023},
+	isbn = {9798400703829},
+	publisher = {Association for Computing Machinery},
+	address = {New York, NY, USA},
+	url = {https://doi.org/10.1145/3618257.3624809},
+	doi = {10.1145/3618257.3624809},
+	abstract = {Network neutrality is important for users, content providers, policymakers, and regulators interested in understanding how network providers differentiate performance. When determining whether a network differentiates against certain traffic, it is important to have strong evidence, especially given that traffic differentiation is illegal in certain countries. In prior work, WeHe detects differentiation via end-to-end throughput measurements between a client and server but does not isolate the network responsible for it. Differentiation can occur anywhere on the network path between endpoints; thus, further evidence is needed to attribute differentiation to a specific network. We present a system, WeHeY, built atop WeHe, that can localize traffic differentiation, i.e., obtain concrete evidence that the differentiation happened within the client's ISP. Our system builds on ideas from network performance tomography; the challenge we solve is that TCP congestion control creates an adversarial environment for performance tomography (because it can significantly reduce the performance correlation on which tomography fundamentally relies). We evaluate our system via measurements "in the wild,'' as well as in emulated scenarios with a wide-area testbed; we further explore its limits via simulations and show that it accurately localizes traffic differentiation across a wide range of network conditions. WeHeY's source code is publicly available athttps://nal-epfl.github.io/WeHeY.},
+	booktitle = {Proceedings of the 2023 ACM on Internet Measurement Conference},
+	pages = {591–605},
+	numpages = {15},
+	keywords = {traffic differentiation, network neutrality},
+	location = {<conf-loc>, <city>Montreal QC</city>, <country>Canada</country>, </conf-loc>},
+	series = {IMC '23}
+}
+
+
 @article{Gupta:000000,
       title = {Ship your Critical Section, Not Your Data: Enabling Transparent Delegation with TCLocks},
       author = {Gupta, Vishal and Dwivedi, Kumar Kartikeya and Kothari, Yugesh and Pan, Yueyand and Zhou, Diyu and Kashyap, Sanidhya},