High-Speed Software Data Plane via Vectorized Packet Processing

2018 ◽  
Vol 56 (12) ◽  
pp. 97-103 ◽  
Author(s):  
David Barach ◽  
Leonardo Linguaglossa ◽  
Damjan Marion ◽  
Pierre Pfister ◽  
Salvatore Pontarelli ◽  
...  
Keyword(s):  
High Speed ◽  
Packet Processing ◽  
Data Plane

High-speed data plane and network functions virtualization by vectorizing packet processing

2019 ◽  
Vol 149 ◽  
pp. 187-199 ◽  
Author(s):  
Leonardo Linguaglossa ◽  
Dario Rossi ◽  
Salvatore Pontarelli ◽  
Dave Barach ◽  
Damjan Marjon ◽  
...  
Keyword(s):  
High Speed ◽  
Packet Processing ◽  
Data Plane ◽  
High Speed Data ◽  
Network Functions

Batched packet processing for high-speed software data plane functions

2018 ◽  
Author(s):  
David Barach ◽  
Leonardo Linguaglossa ◽  
Damjan Marion ◽  
Pierre Pfister ◽  
Salvatore Pontarelli ◽  
...  
Keyword(s):  
High Speed ◽  
Packet Processing ◽  
Data Plane

Data Plane Offloading on a High-Speed Parallel Processing Architecture

2018 ◽  
Author(s):  
Danilo Cerovic ◽  
Valentin Del Piccolo ◽  
Ahmed Amamou ◽  
Kamel Haddadou ◽  
Guy Pujolle
Keyword(s):  
Parallel Processing ◽  
High Speed ◽  
Data Plane ◽  
Processing Architecture

High-speed data-plane packet aggregation and disaggregation by P4 switches

2019 ◽  
Vol 142 ◽  
pp. 98-110 ◽  
Author(s):  
Shie-Yuan Wang ◽  
Chia-Ming Wu ◽  
Yi-Bing Lin ◽  
Ching-Chun Huang
Keyword(s):  
High Speed ◽  
Packet Aggregation ◽  
Data Plane ◽  
High Speed Data ◽  
Aggregation And Disaggregation

scholarly journals Metron

2021 ◽  
Vol 38 (1-2) ◽  
pp. 1-45
Author(s):  
Georgios P. Katsikas ◽  
Tom Barbette ◽  
Dejan Kostić ◽  
JR. Gerald Q. Maguire ◽  
Rebecca Steinert
Keyword(s):  
Load Balancing ◽  
High Speed ◽  
State Of The Art ◽  
Single Server ◽  
Packet Processing ◽  
Commodity Hardware ◽  
Multiple Servers ◽  
Underlying Network ◽  
Network Functions

Deployment of 100Gigabit Ethernet (GbE) links challenges the packet processing limits of commodity hardware used for Network Functions Virtualization (NFV). Moreover, realizing chained network functions (i.e., service chains) necessitates the use of multiple CPU cores, or even multiple servers, to process packets from such high speed links. Our system Metron jointly exploits the underlying network and commodity servers’ resources: ( i ) to offload part of the packet processing logic to the network, ( ii )  by using smart tagging to setup and exploit the affinity of traffic classes, and ( iii )  by using tag-based hardware dispatching to carry out the remaining packet processing at the speed of the servers’ cores, with zero inter-core communication. Moreover, Metron transparently integrates, manages, and load balances proprietary “blackboxes” together with Metron service chains. Metron realizes stateful network functions at the speed of 100GbE network cards on a single server, while elastically and rapidly adapting to changing workload volumes. Our experiments demonstrate that Metron service chains can coexist with heterogeneous blackboxes, while still leveraging Metron’s accurate dispatching and load balancing. In summary, Metron has ( i )  2.75–8× better efficiency, up to ( ii )  4.7× lower latency, and ( iii )  7.8× higher throughput than OpenBox, a state-of-the-art NFV system.

scholarly journals An In-Network Cooperative Storage Schema Based on Neighbor Offloading in a Programmable Data Plane

2021 ◽  
Vol 14 (1) ◽  
pp. 18
Author(s):  
Shoujiang Dang ◽  
Rui Han
Keyword(s):  
Data Storage ◽  
Particle Physics ◽  
High Speed ◽  
High Energy ◽  
High Energy Particle ◽  
Data Traffic ◽  
Energy Particle ◽  
Resolution System ◽  
Data Plane ◽  
High Speed Data

In scientific domains such as high-energy particle physics and genomics, the quantity of high-speed data traffic generated may far exceed the storage throughput and be unable to be in time stored in the current node. Cooperating and utilizing multiple storage nodes on the forwarding path provides an opportunity for high-speed data storage. This paper proposes the use of flow entries to dynamically split traffic among selected neighbor nodes to sequentially amortize excess traffic. We propose a neighbor selection mechanism based on the Local Name Mapping and Resolution System, in which the node weights are computed by combing the link bandwidth and node storage capability, and determining whether to split traffic by comparing normalized weight values with thresholds. To dynamically offload traffic among multiple targets, the cooperative storage strategy implemented in a programmable data plane is presented using the relative weights and ID suffix matching. Evaluation shows that our proposed schema is more efficient compared with end-to-end transmission and ECMP in terms of bandwidth usage and transfer time, and is beneficial in big science.

scholarly journals Wildcard Fields-Based Partitioning for Fast and Scalable Packet Classification in Vehicle-to-Everything

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2563 ◽  
Author(s):  
Jaehyung Wee ◽  
Jin-Ghoo Choi ◽  
Wooguil Pak
Keyword(s):  
Decision Tree ◽  
Communication Networks ◽  
High Speed ◽  
Classification Performance ◽  
Packet Classification ◽  
Classification Algorithms ◽  
Packet Processing ◽  
Security Issues ◽  
Security Of Network ◽  
High Level

Vehicle-to-Everything (V2X) requires high-speed communication and high-level security. However, as the number of connected devices increases exponentially, communication networks are suffering from huge traffic and various security issues. It is well known that performance and security of network equipment significantly depends on the packet classification algorithm because it is one of the most fundamental packet processing functions. Thus, the algorithm should run fast even with the huge set of packet processing rules. Unfortunately, previous packet classification algorithms have focused on the processing speed only, failing to be scalable with the rule-set size. In this paper, we propose a new packet classification approach balancing classification speed and scalability. It can be applied to most decision tree-based packet classification algorithms such as HyperCuts and EffiCuts. It determines partitioning fields considering the rule duplication explicitly, which makes the algorithm memory-effective. In addition, the proposed approach reduces the decision tree size substantially with the minimal sacrifice of classification performance. As a result, we can attain high-speed packet classification and scalability simultaneously, which is very essential for latest services such as V2X and Internet-of-Things (IoT).

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