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Self-Programming Networks: Measuring and Controlling Networks from the Edge.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Self-Programming Networks: Measuring and Controlling Networks from the Edge./
Author:	Liu, Shiyu.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	130 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
Subject:	Software. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28483284
ISBN:	9798505571880

Self-Programming Networks: Measuring and Controlling Networks from the Edge.
Liu, Shiyu.

Self-Programming Networks: Measuring and Controlling Networks from the Edge. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 130 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

The Self-Programming Networks (SPNs) research project aims to build autonomous networks; i.e., networks which sense, monitor, program and control themselves. SPNs use an "edge-centric" architecture, where sensing and controlling are primarily conducted at the network's edge. Prior work on SPNs has yielded two key systems: (i) Huygens, a highly accurate and scalable algorithm for network clock synchronization, and (ii) SIMON, a system for fine-grained network telemetry using observations at the network's edge. These systems have been applied in distributed computing -- e.g., for building faster distributed databases and scalable state-machine replication, and networking -- e.g., for building "jitter-free networks" which underlie financial trading exchanges.In this dissertation, I present my contributions to SPNs. First, by building on SIMON, I present the methods of reconstructing more detailed network states, extending its deployability, and reducing its overhead in computation and storage. Second, I present a novel approach, called On-Ramp, for controlling congestion in SPNs. Akin to on-ramp meters at freeways, On-Ramp "holds" or "pauses" traffic at the network's edge if congestion on the traffic's path is high. Congestion is measured using accurately synchronized clocks at the sender and receiver. On-Ramp operates as a universal underlay: it augments, and greatly improves, the performance of any existing congestion-control protocol during periods of transient overload. It can be deployed by cloud users in their virtual machines (VMs) without any in-network support. I demonstrate the effectiveness of On-Ramp on Google Cloud, CloudLab, ns-3 simulations, and the production clusters in Facebook. Finally, I conclude the dissertation by describing another SPN-enabled application, ClockChain, which scales the transaction ordering service in permissioned distributed ledgers.

ISBN: 9798505571880Subjects--Topical Terms:

619355
Software.

Self-Programming Networks: Measuring and Controlling Networks from the Edge.
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500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: Prabhakar, Balaji;Alizadeh, Mohammad;Rosenblum, Mendel.
502 $a Thesis (Ph.D.)--Stanford University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a The Self-Programming Networks (SPNs) research project aims to build autonomous networks; i.e., networks which sense, monitor, program and control themselves. SPNs use an "edge-centric" architecture, where sensing and controlling are primarily conducted at the network's edge. Prior work on SPNs has yielded two key systems: (i) Huygens, a highly accurate and scalable algorithm for network clock synchronization, and (ii) SIMON, a system for fine-grained network telemetry using observations at the network's edge. These systems have been applied in distributed computing -- e.g., for building faster distributed databases and scalable state-machine replication, and networking -- e.g., for building "jitter-free networks" which underlie financial trading exchanges.In this dissertation, I present my contributions to SPNs. First, by building on SIMON, I present the methods of reconstructing more detailed network states, extending its deployability, and reducing its overhead in computation and storage. Second, I present a novel approach, called On-Ramp, for controlling congestion in SPNs. Akin to on-ramp meters at freeways, On-Ramp "holds" or "pauses" traffic at the network's edge if congestion on the traffic's path is high. Congestion is measured using accurately synchronized clocks at the sender and receiver. On-Ramp operates as a universal underlay: it augments, and greatly improves, the performance of any existing congestion-control protocol during periods of transient overload. It can be deployed by cloud users in their virtual machines (VMs) without any in-network support. I demonstrate the effectiveness of On-Ramp on Google Cloud, CloudLab, ns-3 simulations, and the production clusters in Facebook. Finally, I conclude the dissertation by describing another SPN-enabled application, ClockChain, which scales the transaction ordering service in permissioned distributed ledgers.
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