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Reliable Multicast for Time-Critical Systems, Exercises of Data Communication Systems and Computer Networks

Combine error traffic for multiple groups within intersections, while conserving: Coherent, tunable per-group overhead: Ratio of data.

Typology: Exercises

2022/2023

Uploaded on 05/11/2023

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Motivation
Systems
Roadmap
Reliable Multicast for Time-Critical Systems
A-Exam
Mahesh Balakrishnan 1
1Department of Computer Science
Cornell University
Mahesh Balakrishnan A-Exam: Reliable Multicast for Time-Critical Systems
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Motivation Systems Roadmap

Reliable Multicast for Time-Critical Systems

A-Exam

Mahesh Balakrishnan 1 (^1) Department of Computer Science Cornell University

Motivation Systems Roadmap

Outline

(^1) Motivation

(^2) Systems Ricochet PLATO Others

(^3) Roadmap

Motivation Systems Roadmap

Multicast in the Datacenter

Multicast is ubiquitous in the Datacenter... ... hidden under abstractions/middleware such as: Publish-Subscribe Eventing Replicated Data/Functionality In-memory Caching BEA Weblogic, JBoss, Tibco, RTI DDS, Tangosol, Gemfire...

Motivation Systems Roadmap

Time-Critical Applications

Mission-Critical: Finance, Aerospace, Military Scalability, Availability, Fault-Tolerance... Timeliness! Examples: Real-time automated trading Air-traffic Control System Foobooks.com?

Systems Roadmap PLATO Others

Outline

(^1) Motivation

(^2) Systems Ricochet PLATO Others

(^3) Roadmap

Systems Roadmap PLATO Others

Multicast Research

Many different reliable, scalable protocols Designed for streaming video/TV, file distribution Reliable: Packet Loss at WAN Routers, overlay node failures Scalable: Single group with massive numbers of receivers Not suited for datacenter multicast! Different failure mode Different scalability dimensions

Systems Roadmap PLATO Others

(Scalable) Multicast in the Datacenter

Financial Datacenter Example: Each equity is mapped to a multicast group. Each Node is interested in a different set of equities... ... each Node joins a different set of groups.

Tracking S&P 500 Tracking Portfolio

Lots of overlapping groups =⇒ Low per-group data rate.

Systems Roadmap PLATO Others

Designing a Time-Critical Multicast Primitive

Wanted: A reliable, scalable multicast protocol. Reliable: can tolerate end-host loss bursts Scalable: the size of the group the number of senders to a group the number of groups per node

Systems Roadmap PLATO Others

Designing a Time-Critical Multicast Primitive

Wanted: A reliable, scalable multicast protocol. Reliable: can tolerate end-host loss bursts Scalable: the size of the group the number of senders to a group the number of groups per node

Systems Roadmap PLATO Others

Design Space for Reliable Multicast

How does latency scale?

Two Phases: Discovery and Recovery of Lost Packets ACK/timeout: RMTP/RMTP-II Gossip-based: Bimodal Multicast, lpbcast NAK/sender-based sequencing: SRM Forward Error Correction Fundamental Insight: latencyα (^) datarate^1

Systems Roadmap PLATO Others

Forward Error Correction

Pros: Tunable, Proactive Overhead Time-Critical: Eliminates need for retransmission Cons: FEC packets are generated over a stream of data Have to wait for r data packets before generating FEC latencyα (^) datarate^1 data rate: at a single sender, in a single group

Systems Roadmap PLATO Others

Receiver-Based Forward Error Correction

Randomness: Each Receiver picks another Receiver randomly to send XOR to Tunability: Percentage of XOR packets to data is determined by rate-of-fire (r , c) latencyα (^) datarate^1 data rate: across all senders, in a single group

Data Data Lost Data

XOR

XOR

Systems Roadmap PLATO Others

Receiver-Based Forward Error Correction

Randomness: Each Receiver picks another Receiver randomly to send XOR to Tunability: Percentage of XOR packets to data is determined by rate-of-fire (r , c) latencyα (^) datarate^1 data rate: across all senders, in a single group

Data Data Lost Data

XOR

XOR

Systems Roadmap PLATO Others

Receiver-Based Forward Error Correction

Randomness: Each Receiver picks another Receiver randomly to send XOR to Tunability: Percentage of XOR packets to data is determined by rate-of-fire (r , c) latencyα (^) datarate^1 data rate: across all senders, in a single group

E D C B Kernel BufferA

F

F E Kernel BufferD

B C App Buffer App Buffer