Demo Data from EDBT 2026 / reviewers in the wild / expert
Sandeep Sikka
dblp:85/478
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2000
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Computer networks · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 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 |
Routing and switching · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Memory systems · 77% GPUs and heterogeneous computing · 23% | |
| Theoretical computer science
1 paper |
Algorithmic game theory and mechanism design · 100% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Routing and switching
IP lookup |
0.0 | 1 | 2000 | Memory-efficient state lookups with fast updates · SIGCOMM 2000 |
Routing and switching › IP lookup
longest prefix matching |
0.0 | 1 | 2000 | Memory-efficient state lookups with fast updates · SIGCOMM 2000 |
Routing and switching
packet forwarding |
0.0 | 1 | 2000 | Memory-efficient state lookups with fast updates · SIGCOMM 2000 |
Memory systems › memory management
memory allocation |
0.0 | 1 | 2000 | Memory-efficient state lookups with fast updates · SIGCOMM 2000 |
GPUs and heterogeneous computing
packet processing |
0.0 | 1 | 2000 | Memory-efficient state lookups with fast updates · SIGCOMM 2000 |
Methods — techniques the papers use, named apart from their topics
pipelining · 0.1memory allocation algorithms · 0.0memory allocation algorithm · 0.0game-theoretic optimization · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2000 | Memory-efficient state lookups with fast updatesabstractRouters must do a best matching prefix lookup for every packet; solutions for Gigabit speeds are well known. As Internet link speeds higher, we seek a scalable solution whose speed scales with memory speeds while allowing large prefix databases. In this paper we show that providing such a solution requires careful attention to memory allocation and pipelining. This is because fast lookups require on-chip or off-chip SRAM which is limited by either expense or manufacturing process. We show that doing so while providing guarantees on the number of prefixes supported requires new algorithms and the breaking down of traditional abstraction boundaries between hardware and software. We introduce new problem-specific memory allocators that have provable memory utilization guarantees that can reach 100%; this is contrast to all standard allocators that can only guarantee 20% utilization when the requests can come in the range [1 ... 32]. An optimal version of our algorithm requires a new (but feasible) SRAM memory design that allows shifted access in addition to normal word access. Our techniques generalize to other IP lookup schemes and to other state lookups besides prefix lookup. Sandeep Sikka, George Varghese |
SIGCOMM | 1 |
| 1999 | Algorithms for Optimizing Leveled Commitment Contracts
Tuomas Sandholm, Sandeep Sikka, Samphel Norden |
IJCAI | 2 |