Subhasree Mandal
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A Decentralized SDN Architecture for the WAN
Hakim Weatherspoon
Sylvia Ratnasamy
Ashok Narayanan
Nitika Saran
Ankit Singla
2024 ACM Special Interest Group on Data Communication (SIGCOMM) (2024)
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Motivated by our experiences operating a global WAN, we argue that SDN’s reliance on infrastructure external to the data plane has significantly complicated the challenge of maintaining high availability. We propose a new decentralized SDN (dSDN) architecture in which SDN control logic instead runs within routers, eliminating the control plane’s reliance on external infrastructure and restoring fate sharing between control and data planes.
We present dSDN as a simpler approach to realizing the benefits of SDN in the WAN. Despite its much simpler design, we show that dSDN is practical from an implementation viewpoint, and outperforms centralized SDN in terms of routing convergence and SLO impact.
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Orion: Google’s Software-Defined Networking Control Plane
Shawn Chen
Lorenzo Vicisano
Karthik Swaminathan Nagaraj
Shidong Zhang
Joon Suan Ong
Min Zhu
Mike Conley
Waqar Mohsin
Henrik Muehe
Amr Sabaa
KondapaNaidu Bollineni
Rich Alimi
(2021)
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We present Orion, a distributed Software-Defined Networking platform deployed globally in Google’s datacenter (Jupiter) as well as Wide Area (B4) networks. Orion was designed around a modular, micro-service architecture with a central publish-subscribe database to enable a distributed, yet tightly-coupled, software-defined network control system. Orion enables intent-based management and control, is highly scalable and amenable to global control hierarchies.
Over the years, Orion has matured with continuously improving performance in convergence (up to 40x faster), throughput (handling up to 1.16 million network updates per second), system scalability (supporting 16x larger networks), and data plane availability (50x, 100x reduction in unavailable time in Jupiter and B4, respectively) while maintaining high development velocity with bi-weekly release cadence. Today, Orion robustly enables all of Google’s Software-Defined Networks defending against failure modes that are both generic to large scale production networks as well as unique to SDN systems.
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B4 and After: Managing Hierarchy, Partitioning, and Asymmetry for Availability and Scale in Google's Software-Defined WAN
Chandan Bhagat
Jay Kaimal
Jeffrey Liang
Joon Ong
Min Zhu
Kirill Mendelev
Saikat Ray
Faro Thomas Rabe
Malveeka Tewari
Sourabh Jain
Monika Zahn
Kondapa Naidu Bollineni
Rich Alimi
SIGCOMM'18 (2018)
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Private WANs are increasingly important to the operation of enterprises, telecoms, and cloud providers. For example, B4, Google’s private software-defined WAN, is larger and growing faster than our connectivity to the public Internet. In this paper, we present the five-year evolution of B4. We describe the techniques we employed to incrementally move from offering best-effort content-copy services to carrier-grade availability, while concurrently scaling B4 to accommodate 100x more traffic. Our key challenge is balancing the tension introduced by hierarchy required for scalability, the partitioning required for availability, and the capacity asymmetry inherent to the construction and operation of any large-scale network. We discuss our approach to managing this tension: i) we design a custom hierarchical network topology for both horizontal and vertical software scaling, ii) we manage inherent capacity asymmetry in hierarchical topologies using a novel traffic engineering algorithm without packet encapsulation, and iii) we re-architect switch forwarding rules via two-stage matching/hashing to deal with asymmetric network failures at scale.
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B4: Experience with a Globally Deployed Software Defined WAN
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Junlan Zhou
Min Zhu
Amin Vahdat
Joon Ong
Subbaiah Venkata
Jim Wanderer
Sushant Jain
Proceedings of the ACM SIGCOMM Conference, Hong Kong, China (2013)
Preview abstract
One of the goals of traffic engineering is to achieve a
flexible trade-off between fairness and throughput so that users
are satisfied with their bandwidth allocation and the network
operator is satisfied with the utilization of network resources. In
this paper, we propose a novel way to balance the throughput
and fairness objectives with linear programming. It allows the
network operator to precisely control the trade-off by bounding
the fairness degradation for each commodity compared to the
max-min fair solution or the throughput degradation compared
to the optimal throughput. We also present improvements to a
previous algorithm that achieves max-min fairness by solving a
series of linear programs. We significantly reduce the number
of steps needed when the access rate of commodities is limited.
We extend the algorithm to two important practical use cases:
importance weights and piece-wise linear utility functions for
commodities. Our experiments on synthetic and real networks
show that our algorithms achieve a significant speedup and
provide practical insights on the trade-off between fairness and
throughput.
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