EDBT 2026 Demo / reviewers in the wild / expert
Siva Phani Keshav Bachu
dblp:334/6400
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
1 paper |
Routing and switching · 30% Transport protocols and congestion control · 30% Network optimization and economics · 30% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network optimization and economics › resource allocation
bandwidth allocation |
0.6 | 1 | 2022 | FlowTele: remotely shaping traffic on internet-scale networks · CoNEXT 2022 |
Transport protocols and congestion control › congestion control fairness
TCP friendliness |
0.6 | 1 | 2022 | FlowTele: remotely shaping traffic on internet-scale networks · CoNEXT 2022 |
Routing and switching
traffic engineering |
0.6 | 1 | 2022 | FlowTele: remotely shaping traffic on internet-scale networks · CoNEXT 2022 |
Content delivery and video streaming
quality of experience |
0.2 | 1 | 2022 | FlowTele: remotely shaping traffic on internet-scale networks · CoNEXT 2022 |
Methods — techniques the papers use, named apart from their topics
source control · 0.6TCP fairness · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | FlowTele: remotely shaping traffic on internet-scale networksabstractInternet content providers often deliver content through bandwidth bottlenecks that are out of their control. Thus, despite often having massively over-provisioned upstream servers, the content providers still cannot control the end-to-end user experience. This paper explores remote traffic shaping, allowing the content provider to allocate its share of a remote bottleneck link across its users using a metric other than TCP fairness, while remaining TCP-friendly to cross traffic on the bottleneck link. To evaluate this approach, we designed FlowTele, the first system that shapes outbound traffic on an Internet-scale network to optimize provider-selected metrics, using source control with neither in-network support nor special client support. Our extensive evaluations over the Internet show that by strategically reallocating bandwidth among provider-owned co-bottlenecked flows, FlowTele improves the provider's total revenue by roughly 20%--30% in various network settings, compared with both (i) status quo TCP fairshare and (ii) recent practice by content providers that proactively throttles video quality during the COVID-19 pandemic, while being TCP-friendly to cross-traffic. Besides revenue, we also study other metrics, such as QoE fairness, that a content provider may wish to optimize using FlowTele. Bo-Rong Chen, Zhuotao Liu, Jinhui Song, Fanhui Zeng, Zhoushi Zhu, Siva Phani Keshav Bachu, Yih-Chun Hu |
CoNEXT | 6 |