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Dao-Yuan Chang

dblp:178/9069 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Internet architecture and protocols › packet processing › packet classification
multi-match packet classification
0.212016
TCAM-Based Multi-Match Packet Classification Using Multidimensional Rule Layering · IEEE/ACM Trans. Netw. 2016
Internet architecture and protocols › packet processing
packet classification
0.212016
TCAM-Based Multi-Match Packet Classification Using Multidimensional Rule Layering · IEEE/ACM Trans. Netw. 2016
Memory systems › content-addressable memory › TCAM
range encoding
0.212016
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
0.212016
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
YearPublicationVenuePosition
2016 TCAM-Based Multi-Match Packet Classification Using Multidimensional Rule Layering
abstract
Ternary 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