Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Markus Schmidt 0003

dblp:45/1347-3 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Algorithms and data structures › analysis of algorithms › beyond worst-case analysis
resource augmentation
0.122005
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
0.112005
On the Performance of Greedy Algorithms in Packet Buffering · SIAM J. Comput. 2005
Approximation and online algorithms
online algorithms
0.112005
On the Performance of Greedy Algorithms in Packet Buffering · SIAM J. Comput. 2005
Approximation and online algorithms › online algorithms
competitive analysis
0.012004
On the performance of greedy algorithms in packet buffering · STOC 2004
Mathematical optimization › combinatorial optimization
greedy algorithm
0.012004
On the performance of greedy algorithms in packet buffering · STOC 2004
Approximation and online algorithms › online algorithms › online network optimization
online buffer management
0.012004
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
YearPublicationVenuePosition
2005 Packet Buffering: Randomization Beats Deterministic Algorithms
Markus Schmidt 0003
STACS1
2005 On the Performance of Greedy Algorithms in Packet Buffering
abstract
We 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 buffering
abstract
We 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
STOC2