PPT-Heavy-Hitter Detection Entirely in the Data Plane

Author : briana-ranney | Published Date : 2018-12-11

Vibhaalakshmi Sivaraman Srinivas Narayana Ori RottenSTREICH MUTHU MuthuKRSISHNAN JENNIFER REXFORD 1 Heavy Hitter Flows Flows above a certain threshold of total

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Heavy-Hitter Detection Entirely in the Data Plane: Transcript

Vibhaalakshmi Sivaraman Srinivas Narayana Ori RottenSTREICH MUTHU MuthuKRSISHNAN JENNIFER REXFORD 1 Heavy Hitter Flows Flows above a certain threshold of total packets Top k flows by size. D. ynamic . Re. source . A. llocation for Software-defined . M. easurement. Masoud Moshref. , Minlan Yu, . Ramesh Govindan, Amin Vahdat. 1. (SIGCOMM’14). Measurement is Crucial for Network Management. . decays. L. Littenberg. (work mainly done by. A.T.Shaikhiev. . INR RAS. ). HQL-2016. 2. Outline. Motivation. Previous heavy neutrino searches. Experiment BNL-E949. Selection criteria. Efficiency and background study. D. ynamic . Re. source . A. llocation for Software-defined . M. easurement. Masoud Moshref. , Minlan Yu, . Ramesh Govindan, Amin Vahdat. 1. (SIGCOMM’14). Measurement is Crucial for Network Management. Approach. . for Software-Defined . Monitoring. . using Universal Streaming. Vyas Sekar. Zaoxing Liu. , . Greg . Vorsanger. , . Vladimir Braverman. Network Management:. Many . Monitoring Requirements. of . Graph Streams. Arijit Khan. Charu. C. Aggarwal. Nanyang Technical University. Singapore. IBM. T. J. Watson Research Lab. NY,. USA. Graph Streams. 1. /23. Graph Stream:. Continuous stream of graph edges . Vibhaalakshmi Sivaraman. Srinivas Narayana, Ori . RottenSTREICH. , MUTHU . MuthuKRSISHNAN. , JENNIFER REXFORD. 1. Heavy Hitter Flows. Flows above a certain threshold of total packets. “Top-. k. ” flows by size. David Woodruff . IBM . Almaden. Based on works with Vladimir . Braverman. , Stephen R. Chestnut Nikita . Ivkin. , Jelani Nelson, and . Zhengyu. Wang. Streaming Model. Stream of elements a. 1. , …, a. 1. CONTENTS. ABSTRACT. INTRODUCTION. OBJECTIVES. STUDY AND ANALYSIS. FLOW CHART. FUTURE SCOPE. LIMITATIONS. APPLICATIONS. CONCLUSION. REFERENCES. 01-Feb-12. 2. Data Leakage Detection. ABSTRACT. A data . ?. Backpack Investigation . Model. Compare and analyze data . What’s the . question to investigate?. Ideas:. . Do students at my school carry backpacks that are too heavy?. . Who carries heavier backpacks?. for Real-Time Telemetry. Qizhe Cai. Advisor: Jennifer Rexford. 1. Heavy-Hitter Flows. FlowID. Count. Green. 101. Blue. 30. Red. 10. 2. Heavy-Hitters. . exceed some. threshold of packets or bytes . David Woodruff . IBM . Almaden. Based on works with Vladimir . Braverman. , Stephen R. Chestnut Nikita . Ivkin. , Jelani Nelson, and . Zhengyu. Wang. Streaming Model. Stream of elements a. 1. , …, a. Router/Switch Architecture Overview. Generalization of IP Router and Ethernet Switch Data Plane Operations. “match-action” forwarding abstraction. Openflow. and Software-Defined . N. etworking (SDN). Why Social Graphs Are Different. Communities. Finding Triangles. Jeffrey D. Ullman. Stanford University/. Infolab. Social Graphs. Graphs can be either directed or undirected.. Example. : The Facebook “friends” graph (undirected).. Muhammad . Shahbaz. Tushar Swamy, Alex Rucker, . Neeraja. . Yadwadkar. , . Yaqi. Zhang, and Kunle . Olukotun. . Taurus: An Intelligent Data Plane. Programmable Data Plane. fused with. Machine Intelligence.

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