Theia

Walraed-Sullivan, Meg; Padhye, Jitendra; Maltz, David A.

doi:10.1145/2670518.2673885

Cited by 7 publications

(1 citation statement)

References 15 publications

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“…Not so long ago were studied fully disaggregated infrastructures, notably with storage rack disaggregation 21 and network requirements for resource disaggregation. 2,22,23 More precisely, Reference 24 carried out a study to estimate the latency and bandwidth requirements that the interconnect in disaggregated datacenters must meet to avoid degrading application-level performance with existing network designs. References 22 and 23 explored new architecture designs to integrate 1 Tbps silicon photonic, remote nonvolatile memory, and System-on-Chips (SoCs)…”

Section: Disaggregationmentioning

confidence: 99%

Networking in next generation disaggregated datacenters

Ekane

Tchana

Hagimont

et al. 2023

Concurrency and Computation

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SummaryNowadays, datacenters lean on a computer‐centric approach based on monolithic servers which include all necessary hardware resources (mainly CPU, RAM, network, and disks) to run applications. Such an architecture comes with two main limitations: (1) difficulty to achieve full resource utilization and (2) coarse granularity for hardware maintenance. Recently, many works investigated a resource‐centric approach called disaggregated architecture where the datacenter is composed of self‐content resource boards interconnected using fast interconnection technologies, each resource board including instances of one resource type. The resource‐centric architecture allows each resource to be managed (maintenance, allocation) independently. LegoOS is the first work which studied the implications of disaggregation on the operating system, proposing to disaggregate the operating system itself. They demonstrated the suitability of this approach, considering mainly CPU and RAM resources. However, they did not study the implication of disaggregation on network resources. We reproduced a LegoOS infrastructure and extended it to support disaggregated networking. We show that networking can be disaggregated following the same principles, and that classical networking optimizations such as DMA, DDIO, or loopback can be reproduced in such an environment. Our evaluations show the viability of the approach and the potential of future disaggregated infrastructures.

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Section: Disaggregationmentioning

confidence: 99%

Networking in next generation disaggregated datacenters

Ekane

Tchana

Hagimont

et al. 2023

Concurrency and Computation

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Condor

Schlinker

Mysore

Smith

et al. 2015

Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication

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The design space for large, multipath datacenter networks is large and complex, and no one design fits all purposes. Network architects must trade off many criteria to design costeffective, reliable, and maintainable networks, and typically cannot explore much of the design space. We present Condor, our approach to enabling a rapid, efficient design cycle. Condor allows architects to express their requirements as constraints via a Topology Description Language (TDL), rather than having to directly specify network structures. Condor then uses constraint-based synthesis to rapidly generate candidate topologies, which can be analyzed against multiple criteria. We show that TDL supports concise descriptions of topologies such as fat-trees, BCube, and DCell; that we can generate known and novel variants of fat-trees with simple changes to a TDL file; and that we can synthesize large topologies in tens of seconds. We also show that Condor supports the daunting task of designing multi-phase network expansions that can be carried out on live networks.

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