Distributed SDN Control: Survey, Taxonomy, and Challenges

Bannour, Fetia; Souihi, Sami; Mellouk, Abdelhamid

doi:10.1109/comst.2017.2782482

Cited by 356 publications

(209 citation statements)

References 88 publications

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“…A 1 's view of the gateway delay between A 1 −A 2 is always accurate and up-to-date, since the gateway router is within domain A 1 ; while the up-to-date gateway delay between A 2 −A 3 is actually 3. On the other hand, A 1 's view and the up-to-date server delays of the servers hosting c (2) 2 and c (2) 3 are 4 and 2, 1 and 3, respectively. Therefore, the domain controller of A 1 would forward the service request to domain A 3 rather than A 2 since the request latencies are estimated to be 2 + 1 + 2 = 5 in the former and 2 + 4 = 6 in the latter based on its view of the network.…”

Section: B Synchronization Among Sdn Controllersmentioning

confidence: 99%

See 1 more Smart Citation

MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design

Zhang

Poularakis

et al. 2019

2019 IEEE 27th International Conference on Network Protocols (ICNP)

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In distributed software-defined networks (SDN), multiple physical SDN controllers, each managing a network domain, are implemented to balance centralised control, scalability, and reliability requirements. In such networking paradigms, controllers synchronize with each other, in attempts to maintain a logically centralised network view. Despite the presence of various design proposals for distributed SDN controller architectures, most existing works only aim at eliminating anomalies arising from the inconsistencies in different controllers' network views. However, the performance aspect of controller synchronization designs with respect to given SDN applications are generally missing. To fill this gap, we formulate the controller synchronization problem as a Markov decision process (MDP) and apply reinforcement learning techniques combined with deep neural networks (DNNs) to train a smart, scalable, and fine-grained controller synchronization policy, called the Multi-Armed Cooperative Synchronization (MACS), whose goal is to maximise the performance enhancements brought by controller synchronizations. Evaluation results confirm the DNN's exceptional ability in abstracting latent patterns in the distributed SDN environment, rendering significant superiority to MACS-based synchronization policy, which are 56% and 30% performance improvements over ONOS and greedy SDN controller synchronization heuristics.

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Section: B Synchronization Among Sdn Controllersmentioning

confidence: 99%

“…Yet, such centralised control suffers from major scalability and reliability issues. In this regard, distributed SDN [2] is proposed to balance the centralised and distributed controls.…”

Section: Introductionmentioning

confidence: 99%

MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design

Zhang

Poularakis

et al. 2019

2019 IEEE 27th International Conference on Network Protocols (ICNP)

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“…PCR has similar goals and evolved from spanning tree protocols, e.g., the Rapid Spanning Tree Protocol (RSTP) [ [77]. In the hybrid approach, the IS-IS protocol in the data plane handles basic functions, e.g., topology discovery and default path computation, while the SDN controller [102] in the control plane manages the Explicit Paths (EPs), as shown in Fig. 16.…”

Section: E Flow Integritymentioning

confidence: 99%

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Nasrallah

Thyagaturu

Alharbi

et al. 2019

IEEE Commun. Surv. Tutorials

498

219

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Many network applications, e.g., industrial control, demand Ultra-Low Latency (ULL). However, traditional packet networks can only reduce the end-to-end latencies to the order of tens of milliseconds. The IEEE 802.1 Time Sensitive Networking (TSN) standard and related research studies have sought to provide link layer support for ULL networking, while the emerging IETF Deterministic Networking (DetNet) standards seek to provide the complementary network layer ULL support. This article provides an up-to-date comprehensive survey of the IEEE TSN and IETF DetNet standards and the related research studies. The survey of these standards and research studies is organized according to the main categories of flow concept, flow synchronization, flow management, flow control, and flow integrity. ULL networking mechanisms play a critical role in the emerging fifth generation (5G) network access chain from wireless devices via access, backhaul, and core networks. We survey the studies that specifically target the support of ULL in 5G networks, with the main categories of fronthaul, backhaul, and network management. Throughout, we identify the pitfalls and limitations of the existing standards and research studies. This survey can thus serve as a basis for the development of standards enhancements and future ULL research studies that address the identified pitfalls and limitations.

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“…In contrast to OpenFlow or other SDN architectures, the two-tier control plane structure therefore does not introduce additional computing nodes that may imply a further risk of failout. The authors of [44] provide an overview on hierarchical and distributed SDN control planes and discuss its specifics. Although we see many advantages in the two-tier control plane architecture, P4-MACsec can be implemented with a one-tier control plane structure featuring a central SDN controller as well.…”

Section: P4-macsecmentioning

confidence: 99%

P4-MACsec: Dynamic Topology Monitoring and Data Layer Protection With MACsec in P4-Based SDN

et al. 2020

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We propose P4-MACsec to protect network links between P4 switches through automated deployment of MACsec, a widespread IEEE standard for securing Layer 2 infrastructures. It is supported by switches and routers from major manufacturers and has only little performance limitations compared to VPN technologies such as IPsec. P4-MACsec introduces a data plane implementation of MACsec including AES-GCM encryption and decryption directly on P4 switches. P4-MACsec features a twotier control plane structure where local controllers running on the P4 switches interact with a central controller. We propose a novel secure link discovery mechanism that leverages protected LLDP frames and the two-tier control plane structure for secure and efficient management of a global link map. Automated deployment of MACsec creates secure channel, generates keying material, and configures the P4 switches for each detected link between two P4 switches. It detects link changes and performs rekeying to provide a secure, configuration-free operation of MACsec. In this paper, we review the technological background of P4-MACsec and explain its architecture. To demonstrate the feasibility of P4-MACsec, we implement it on the BMv2 P4 software switch and validate the prototype through experiments. We evaluate its performance through experiments that focus on TCP throughput and round-trip time. We publish the prototype and experiment setups on Github.

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Distributed SDN Control: Survey, Taxonomy, and Challenges

Cited by 356 publications

References 88 publications

MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design

MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

P4-MACsec: Dynamic Topology Monitoring and Data Layer Protection With MACsec in P4-Based SDN

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