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Virtual DBA: virtualizing passive optical networks to enable multi-service operation in true multi-tenant environments

Marco Ruffini, Arsalan Ahmad, Sanwal Zeb, Nima Afraz, and Frank Slyne

Open Access Open Access

Abstract

This paper presents the concept of virtual dynamic bandwidth allocation (DBA), a method we propose to virtualize upstream capacity scheduling in passive optical networks (PONs), which provides multiple independent virtual network operators with the ability to precisely schedule their upstream traffic allocation. After a brief introduction on the evolution of access network sharing, we present our virtual DBA architecture, detailing its main components. We then provide a summary of the work done in this area from both theoretical and practical implementation perspectives. In this paper, we propose a novel stateless algorithm for merging multiple independent virtual bandwidth maps based on priority classes and analyze its performance in terms of efficiency of capacity allocation and latency. Through our results, we discuss the existence of a trade-off between traffic load and grant size distribution versus efficiency and latency. We find that, different from a residential single-tenant application, when PONs are used for low-latency and multi-tenant applications, the system has better overall performance if grants are allocated in small size. In addition, our analysis shows that for high-priority, strict latency services, our proposed merging algorithm presents delay performance that is independent of the traffic distribution considered.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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Figures (16)
Fig. 1.
Fig. 1. Architectural design of the virtual DBA mechanism.

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Fig. 2.
Fig. 2. Schematic of the PON auction mechanism.

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Fig. 3.
Fig. 3. Overall architecture of the PON system prototype running the virtual DBA.

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Fig. 4.
Fig. 4. Interface between a virtual OLT and vDBA.

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Fig. 5.
Fig. 5. Traffic scenario 1: (a) served traffic and (b) latency versus offered traffic.

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Fig. 6.
Fig. 6. Traffic scenario 1: (a) served traffic and (b) latency versus high-priority traffic.

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Fig. 7.
Fig. 7. Traffic scenario 2: (a) served traffic and (b) latency versus offered traffic.

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Fig. 8.
Fig. 8. Traffic scenario 2: (a) served traffic and (b) latency versus high-priority traffic.

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Fig. 9.
Fig. 9. Traffic scenario 3: (a) served traffic and (b) latency versus offered traffic.

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Fig. 10.
Fig. 10. Traffic scenario 3: (a) served traffic and (b) latency versus high-priority traffic.

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Fig. 11.
Fig. 11. Traffic scenario 1: (a) served traffic and (b) latency versus offered traffic for high traffic priority, for uniform, Poisson, and self-similar distributions.

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Fig. 12.
Fig. 12. Traffic scenario 1: (a) served traffic and (b) latency versus offered traffic for low traffic priority, for uniform, Poisson, and self-similar distributions.

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Fig. 13.
Fig. 13. Traffic scenario 3: (a) served traffic and (b) latency versus offered traffic for low traffic priority, for uniform, Poisson, and self-similar distributions.

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Fig. 14.
Fig. 14. Traffic scenario 3: (a) served traffic and (b) latency versus offered traffic for high traffic priority, for uniform, Poisson, and self-similar distributions.

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Fig. 15.
Fig. 15. Traffic scenario 3: (a) served traffic and (b) latency versus high-priority traffic, for traffic priorities 3 and 4, for uniform, Poisson, and self-similar distributions.

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Fig. 16.
Fig. 16. Traffic scenario 3: (a) served traffic and (b) latency versus high-priority traffic, for traffic priorities 1 and 2, for uniform, Poisson, and self-similar distributions.

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Tables (1)

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Algorithm 1. Priority-Based Merging Algorithm

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