Enabling Quasi-Static Reconfigurable Networks With Robust Topology Engineering Article Swipe
Min Yee Teh
,
Shizhen Zhao
,
Peirui Cao
,
Keren Bergman
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tnet.2022.3210534
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tnet.2022.3210534
Many optical circuit switched data center networks (DCN) have been proposed in the last decade to attain higher capacity and topology reconfigurability, though commercial adoption of these architectures have been minimal. One major challenge these architectures face is the difficulty of handling uncertain traffic demands using commercial optical circuit switches (OCS) with high switching latency. Prior works have generally focused on developing fast-switching OCS prototypes to quickly react to traffic variations through frequent reconfigurations. This approach, however, adds tremendous complexity overhead to the control plane, and raises the barrier for commercial adoption of optical circuit switched data center networks. We propose, a robust topology and routing optimization framework for reconfigurable optical circuit switched data centers. co-optimizes topology and routing based on a convex set of traffic matrices, and offers strict throughput guarantees for any future traffic matrices bounded by the convex set. For the bursty traffic demands that are unbounded by the convex set, we employ a desensitization technique to reduce performance hit. This enables to generate topology and routing solutions capable of handling unexpected traffic changes without relying on frequent topology reconfigurations. Our extensive evaluations based on Facebook's production DCN traces show that, even with daily reconfigurations which could be realized by current commercial MEMS-based OCSs from Calient Technologies, achieves about 20% lower max link utilization, and about 32% lower average hop count compared to cost-equivalent static topologies. Our work shows that adoption of reconfigurable topologies in commercial DCNs is feasible even without fast OCSs.
Related Topics
Concepts
Computer science
Network topology
Reconfigurability
Computer network
Topology (electrical circuits)
Circuit switching
Logical topology
Distributed computing
Robustness (evolution)
Latency (audio)
Routing (electronic design automation)
Telecommunications
Engineering
Electrical engineering
Gene
Chemistry
Biochemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tnet.2022.3210534
- https://ieeexplore.ieee.org/ielx7/90/4359146/09915417.pdf
- OA Status
- hybrid
- Cited By
- 16
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312441425
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4312441425Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tnet.2022.3210534Digital Object Identifier
- Title
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Enabling Quasi-Static Reconfigurable Networks With Robust Topology EngineeringWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-10Full publication date if available
- Authors
-
Min Yee Teh, Shizhen Zhao, Peirui Cao, Keren BergmanList of authors in order
- Landing page
-
https://doi.org/10.1109/tnet.2022.3210534Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/90/4359146/09915417.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/90/4359146/09915417.pdfDirect OA link when available
- Concepts
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Computer science, Network topology, Reconfigurability, Computer network, Topology (electrical circuits), Circuit switching, Logical topology, Distributed computing, Robustness (evolution), Latency (audio), Routing (electronic design automation), Telecommunications, Engineering, Electrical engineering, Gene, Chemistry, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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16Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 4, 2023: 4Per-year citation counts (last 5 years)
- References (count)
-
53Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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