Demo Data from VLDB 2026 / reviewers in the wild / expert
Dao-Yuan Chang
dblp:178/9069
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 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.
| Computer networks
1 paper |
Internet architecture and protocols · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Hardware accelerators and domain-specific architectures · 50% Memory systems · 50% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Internet architecture and protocols › packet processing › packet classification
multi-match packet classification |
0.2 | 1 | 2016 | TCAM-Based Multi-Match Packet Classification Using Multidimensional Rule Layering · IEEE/ACM Trans. Netw. 2016 |
Internet architecture and protocols › packet processing
packet classification |
0.2 | 1 | 2016 | TCAM-Based Multi-Match Packet Classification Using Multidimensional Rule Layering · IEEE/ACM Trans. Netw. 2016 |
Memory systems › content-addressable memory › TCAM
range encoding |
0.2 | 1 | 2016 | TCAM-Based Multi-Match Packet Classification Using Multidimensional Rule Layering · IEEE/ACM Trans. Netw. 2016 |
Hardware accelerators and domain-specific architectures › network accelerator
TCAM-based packet classification |
0.2 | 1 | 2016 | TCAM-Based Multi-Match Packet Classification Using Multidimensional Rule Layering · IEEE/ACM Trans. Netw. 2016 |
Methods — techniques the papers use, named apart from their topics
rule layering · 0.5range encoding · 0.5bitmap tagging · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | TCAM-Based Multi-Match Packet Classification Using Multidimensional Rule LayeringabstractTernary content addressable memory (TCAM) has superior performance for single-match packet classification but not the case for multi-match packet classification. The limitation is caused by TCAM architecture that reports only the first matching rule. To cope with the limitation, previous algorithms use extra TCAM entries or accesses, or both, to fulfill multi-match packet classification. These algorithms also reorder rules; thus, a multi-match classifier based on these algorithms cannot maintain performance for single-match packet classification. In other words, all matching rules must be yielded to determine the highest priority matching rule. In this paper, we present a TCAM-based scheme for multi-match packet classification without single-match penalty. Our scheme partitions a rule set based on range layering, which can be applied to achieve range encoding. The rule partitioning generates rule subsets which satisfy that the rules in a subset are mutually disjoint. Each rule is then tagged a bitmap for subset identification to fulfill multi-match packet classification. Two approaches, loose coupling and tight coupling, are derived with different search procedures while incorporating range encoding. Both approaches can maintain original rule order, but with different performance tradeoff. We also present a refinement which uses all available TCAM entries to improve the performance of multi-match packet classification. The experimental results show that combining range encoding with multi-match packet classification has advantages of storage efficiency and speed superiority. The capability of supporting single-match packet classification also provides better flexibility of applying different packet actions. Dao-Yuan Chang, Pi-Chung Wang |
IEEE/ACM Trans. Netw. | 1 |