Demo Data from EDBT 2026 / reviewers in the wild / expert
Markus Schmidt 0003
dblp:45/1347-3
· DBLP profile ↗
3ranked-venue papers
1as first author
0since 2021 · last 2005
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 3 · 1 first-author
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.
| Theoretical computer science
2 papers |
Approximation and online algorithms · 58% Algorithms and data structures · 29% Mathematical optimization · 13% |
Topics — the 6 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithms and data structures › analysis of algorithms › beyond worst-case analysis
resource augmentation |
0.1 | 2 | 2005 | On the Performance of Greedy Algorithms in Packet Buffering · SIAM J. Comput. 2005 On the performance of greedy algorithms in packet buffering · STOC 2004 |
Approximation and online algorithms › online algorithms › online packing and covering
buffer management |
0.1 | 1 | 2005 | On the Performance of Greedy Algorithms in Packet Buffering · SIAM J. Comput. 2005 |
Approximation and online algorithms
online algorithms |
0.1 | 1 | 2005 | On the Performance of Greedy Algorithms in Packet Buffering · SIAM J. Comput. 2005 |
Approximation and online algorithms › online algorithms
competitive analysis |
0.0 | 1 | 2004 | On the performance of greedy algorithms in packet buffering · STOC 2004 |
Mathematical optimization › combinatorial optimization
greedy algorithm |
0.0 | 1 | 2004 | On the performance of greedy algorithms in packet buffering · STOC 2004 |
Approximation and online algorithms › online algorithms › online network optimization
online buffer management |
0.0 | 1 | 2004 | On the performance of greedy algorithms in packet buffering · STOC 2004 |
Methods — techniques the papers use, named apart from their topics
competitive analysis · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | Packet Buffering: Randomization Beats Deterministic Algorithms
Markus Schmidt 0003 |
STACS | 1 |
| 2005 | On the Performance of Greedy Algorithms in Packet BufferingabstractWe study a basic buffer management problem that arises in network switches. Consider m input ports, each of which is equipped with a buffer (queue) of limited capacity. Data packets arrive online and can be stored in the buffers if space permits; otherwise packet loss occurs. In each time step the switch can transmit one packet from one of the buffers to the output port. The goal is to maximize the number of transmitted packets. Simple arguments show that any work-conserving algorithm, which serves any nonempty buffer, is 2-competitive. Azar and Richter recently presented a randomized online algorithm and gave lower bounds for deterministic and randomized strategies. In practice, greedy algorithms are very important because they are fast, use little extra memory, and reduce packet loss by always serving a longest queue. In this paper we first settle the competitive performance of the entire family of greedy strategies. We prove that greedy algorithms are not better than 2-competitive no matter how ties are broken. Our lower bound proof uses a new recursive construction for building adversarial buffer configurations that may be of independent interest. We also give improved lower bounds for deterministic and randomized online algorithms. In this paper we present the first deterministic online algorithm that is better than 2-competitive. We develop a modified greedy algorithm, called semigreedy, and prove that it achieves a competitive ratio of $17/9 \approx 1.89$. The new algorithm is simple, fast, and uses little extra memory. Only when the risk of packet loss is low does it not serve the longest queue. Additionally we study scenarios when an online algorithm is granted additional resources. We consider resource augmentation with respect to memory and speed; i.e., an online algorithm may be given larger buffers or higher transmission rates. We analyze greedy and other online strategies. Susanne Albers, Markus Schmidt 0003 |
SIAM J. Comput. | 2 |
| 2004 | On the performance of greedy algorithms in packet bufferingabstractWe study a basic buffer management problem that arises in network switches. Consider m input ports, each of which is equipped with a buffer (queue) of limited capacity. Data packets arrive online and can be stored in the buffers if space permits; otherwise packet loss occurs. In each time step the switch can transmit one packet from one of the buffers to the output port. The goal is to maximize the number of transmitted packets. Simple arguments show that any reasonable algorithm, which serves any non-empty buffer, is 2-competitive. Azar and Richter recently presented a randomized online algorithm and gave lower bounds for deterministic and randomized strategies.In practice greedy algorithms are very important because they are fast, use little extra memory and reduce packet loss by always serving a longest queue. In this paper we first settle the competitive performance of the entire family of greedy strategies. We prove that greedy algorithms are not better than 2-competitive no matter how ties are broken. Our lower bound proof uses a new recursive construction for building adversarial buffer configurations that may be of independent interest. We also give improved lower bounds for deterministic and randomized online algorithms.In the second part of the paper we present the first deterministic online algorithm that is better than 2-competitive. We develop a modified greedy algorithm, called Semi-Greedy, and prove that it achieves a competitive ratio of $17/9 ≅ 1. 89$. The new algorithm is simple, fast and uses little extra memory. Only when the risk of packet loss is low, it does not serve the longest queue. Additionally we study scenarios when an online algorithm is granted additional resources. We consider resource augmentation with respect to memory and speed, i. e. an online algorithm may be given larger buffers or higher transmission rates. We analyze greedy and other online strategies. Susanne Albers, Markus Schmidt 0003 |
STOC | 2 |