Programmable switches are recently used for accelerating data-intensive distributed applications. Some computational tasks, traditionally performed on servers in data centers, are offloaded to the network on programmable switches. These tasks may require the support of on-the-fly floatingpoint operations. Unfortunately, the computational capacity of programmable switches is limited to simple integer arithmetic operations. To address this issue, prior approaches either adopt a float-to-integer method or rely on local CPUs of switches, incurring accuracy loss and delayed processing.To this end, we propose NetFC, a table-lookup method to achieve on-the-fly in-network floating-point arithmetic operations nearly without accuracy loss. NetFC adopts a divide-and-conquer mechanism that converts the original huge table into several much smaller tables that are operated by the built-in integer operations. NetFC further leverages a scaling-factor mechanism for improving computational accuracy, and a prefix-based lossless table compression method to reduce memory consumption. We use both synthetic and real-life datasets to evaluate NetFC. The experimental results show that the average accuracy of NetFC is above 99.94% with only 448KB memory consumption. Furthermore, we integrate NetFC into Sonata [12] for detecting Slowloris attack, yielding significant decrease of detection delay.
{"title":"NetFC: Enabling Accurate Floating-point Arithmetic on Programmable Switches","authors":"Penglai Cui, H. Pan, Zhenyu Li, Jiaoren Wu, Shengzhuo Zhang, Xingwu Yang, Hongtao Guan, Gaogang Xie","doi":"10.1109/ICNP52444.2021.9651946","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651946","url":null,"abstract":"Programmable switches are recently used for accelerating data-intensive distributed applications. Some computational tasks, traditionally performed on servers in data centers, are offloaded to the network on programmable switches. These tasks may require the support of on-the-fly floatingpoint operations. Unfortunately, the computational capacity of programmable switches is limited to simple integer arithmetic operations. To address this issue, prior approaches either adopt a float-to-integer method or rely on local CPUs of switches, incurring accuracy loss and delayed processing.To this end, we propose NetFC, a table-lookup method to achieve on-the-fly in-network floating-point arithmetic operations nearly without accuracy loss. NetFC adopts a divide-and-conquer mechanism that converts the original huge table into several much smaller tables that are operated by the built-in integer operations. NetFC further leverages a scaling-factor mechanism for improving computational accuracy, and a prefix-based lossless table compression method to reduce memory consumption. We use both synthetic and real-life datasets to evaluate NetFC. The experimental results show that the average accuracy of NetFC is above 99.94% with only 448KB memory consumption. Furthermore, we integrate NetFC into Sonata [12] for detecting Slowloris attack, yielding significant decrease of detection delay.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131861151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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