EDBT 2026 Demo / reviewers in the wild / expert
Tianyu Ni
dblp:176/5359
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1
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 |
Network optimization and economics · 100% | |
| Theoretical computer science
1 paper |
Algorithmic game theory and mechanism design · 100% | |
| Network and information security
1 paper |
Network security · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network optimization and economics
resource allocation |
0.4 | 1 | 2019 | On The Robustness of Price-Anticipating Kelly Mechanism · IEEE/ACM Trans. Netw. 2019 |
Algorithmic game theory and mechanism design
non-cooperative game |
0.4 | 1 | 2019 | On The Robustness of Price-Anticipating Kelly Mechanism · IEEE/ACM Trans. Netw. 2019 |
Methods — techniques the papers use, named apart from their topics
game theory · 1.1approximation bounds · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | On The Robustness of Price-Anticipating Kelly MechanismabstractThe price-anticipating Kelly mechanism (PAKM) is one of the most extensively used strategies to allocate divisible resources for strategic users in communication networks and computing systems. The users are deemed as selfish and also benign, each of which maximizes his individual utility of the allocated resources minus his payment to the network operator. However, in many applications a user can use his payment to reduce the utilities of his opponents, thus playing a misbehaving role. It remains mysterious to what extent the misbehaving user can damage or influence the performance of benign users and the network operator. In this work, we formulate a non-cooperative game consisting of a finite amount of benign users and one misbehaving user. The maliciousness of this misbehaving user is captured by his willingness to pay to trade for unit degradation in the utilities of benign users. The network operator allocates resources to all the users via the price-anticipating Kelly mechanism. We present six important performance metrics with regard to the total utility and the total net utility of benign users, and the revenue of network operator under three different scenarios: with and without the misbehaving user, and the maximum. We quantify the robustness of PAKM against the misbehaving actions by deriving the upper and lower bounds of these metrics. With new approaches, all the theoretical bounds are applicable to an arbitrary population of benign users. Our study reveals two important insights: 1) the performance bounds are very sensitive to the misbehaving user's willingness to pay at certain ranges and 2) the network operator acquires more revenues in the presence of the misbehaving user which might disincentivize his countermeasures against the misbehaving actions. Yuedong Xu 0001, Zhujun Xiao, Tianyu Ni, Hui Wang 0011, Xin Wang 0002, Eitan Altman |
IEEE/ACM Trans. Netw. | 3 |