MLTree: an efficient packet classification algorithm using multiple layered trees in software defined networks
學年 113
學期 2
出版（發表）日期 2025-06-01
作品名稱 MLTree: an efficient packet classification algorithm using multiple layered trees in software defined networks
作品名稱（其他語言）
著者 Chuang, Po‑Jen; Yao, Jung‑Chun
單位
出版者
著錄名稱、卷期、頁數 The Journal of Supercomputing, 81，p.1-35
摘要 In software defined networks (SDN), the open-source virtual switch tends to face challenges due to enormous rule processing and intricate data forwarding. Packet classification becomes a vitally important topic. A good classification strategy should achieve fast rule lookup and update at reasonable memory cost. It is challenging for both tuple-based and tree-based algorithms to simultaneously maintain desirable lookup and update performance. Tuple-based algorithms may attain efficient updates but experience unappealing lookup, while tree-based algorithms may attain better lookup by tree traversal but surrender update performance to potential rule replication. The main goal of this investigation is to construct a new algorithm to improve the classification efficiency of previous approaches—particularly to secure a proper performance balance between lookup and update. The proposed MLTree algorithm is an essentially tree-based approach. It maintains the lookup benefits of decision trees and also produces desirable updates by tuple-based multilayer partitioning which fits better to the universality of varying field lengths and scalability of multiple fields in SDN. Evaluation results show that, with feasible cost, MLTree realizes steadily better lookup and update performance than previous algorithms.
關鍵字 Software defined networks (SDN); OpenvSwitch; Packet classification; Tuple space search; Decision trees; Performance evaluation
語言 en
ISSN
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Chuang, Po-Jen
審稿制度 是
國別 USA
公開徵稿
出版型式 ,電子版,紙本
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