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Lanaotek Data Center Switch Architecture: Solving East-West Traffic Bottlenecks in Modern AI-Optimized Data Centers
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Lanaotek Data Center Switch Architecture: Solving East-West Traffic Bottlenecks in Modern AI-Optimized Data Centers


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    For two decades, data center networks were designed primarily for north-south traffic: users outside the facility sending requests in, servers processing them, and responses flowing back out. In 2026, the dominant performance constraint is east-west traffic — the horizontal movement of data between servers, applications, containers, and GPU clusters inside the same facility.


    switch for data center-01.png


    Three forces have accelerated this shift:

    • Container and Microservices Architectures: Application components now generate network packets as they call across service boundaries. Modern multi-tier architectures decompose monoliths into microservices running in different server pods.

    • AI and GPU Cluster Workloads: Large AI training jobs generate massive east-west traffic during gradient synchronization across hundreds of GPUs. A single run can generate terabytes of traffic per hour, turning network inefficiency into expensive GPU idle time.

    • Multi-cloud and Hybrid Architectures: Workloads spanning on-premise and cloud environments require high-bandwidth internal paths to move data efficiently.


    2. The Leaf-Spine Architecture: Making East-West Traffic Scalable

    2.1 Why Leaf-Spine is the Baseline

    The leaf-spine topology (Clos fabric) provides consistent hop counts and multiple equal-cost paths.

    • Every leaf switch connects to every spine switch.

    • East-west traffic traverses exactly two hops: Leaf → Spine → Leaf.

    • ECMP (Equal-Cost Multi-Path) distributes flows across all available uplinks.

    2.2 Sizing the Fabric: Oversubscription Targets

    Deployment SizeLeaf SwitchSpine SwitchUplink SpeedTypical Oversubscription
    Small (<200 servers)IRSL3LM-04X24GP-2D-Z8CSL3M-04X48GP-H2A-L25G SFP281:4 to 1:6
    Medium (200-1000 servers)IRSL3LM-04X24GP-2D-Z8CSL3M-04X48GP-H2A-L (x2)100G QSFP281:2 to 1:4
    Large (AI Workloads)CSL3M-04X48GP-H2A-LCSL3M-04X48GP-H2A-L (x4+)100G or 400G1:1 or 1:2

    2.3 Physical Layer Considerations

    • Fiber: Single-mode (OS2) for spans >500m; OM4/OM5 for shorter campus-style deployments.

    • Connectivity: Use MPO/MTP trunk cables for high-density 100G parallel fiber connections.


    3. Controlling Congestion: ECMP, ECN, and RoCE

    3.1 Flowlet-based ECMP

    Standard hashing can cause "hot spots" when large elephant flows (like storage backups) collide on the same link. Flowlet-aware ECMP (supported by the Lanaotek CSL3M-04X48GP-H2A-L) fragments these flows into bursts, allowing the ASIC to redistribute them across alternate paths more effectively.

    3.2 ECN (Explicit Congestion Notification)

    ECN marks packets rather than dropping them when buffers fill up. The end host's TCP stack then slows down transmission, stabilizing the network before overflow occurs.

    3.3 RoCEv2 (RDMA over Converged Ethernet)

    RoCEv2 is critical for AI clusters, providing microsecond latency. It requires a lossless fabric via:

    • PFC (Priority Flow Control): Pauses specific priority queues to prevent drops.

    • DCBX: Negotiates PFC parameters between switches and endpoints.


    switch for data center-02.png


    4. QoS, Micro-Segmentation, and Telemetry

    4.1 QoS Design for Mixed Workloads

    Traffic ClassSourcePriorityQueue Treatment
    RoCE/StorageGPU Clusters, NVMe-oFHighest (PFC)Priority Queue + ECN
    API CallsMicroservicesHighShort Queue, Low Latency
    General ComputeStandard WorkloadsMediumDefault Queue
    Batch/BackupReplication JobsLowWDRR Scheduling

    4.2 Micro-Segmentation & Telemetry

    Micro-segmentation restricts workloads to required paths, reducing broadcast flooding. Simultaneously, Streaming Telemetry via the LanaotekView NMS allows operators to see queue depths and ECMP heatmaps in real-time.


    switch for data center-03.jpg


    5. Lanaotek Data Center Switch Configuration Examples

    5.1 QoS Configuration: DSCP-Based Classification

    IRSL3LM04X24GP(config)# class-map match-any ROCE-TRAFFIC
    IRSL3LM04X24GP(config-cmap)# match dscp 34
    IRSL3LM04X24GP(config-cmap)# exit
    IRSL3LM04X24GP(config)# policy-map QoS-POLICY
    IRSL3LM04X24GP(config-pmap)# class ROCE-TRAFFIC
    IRSL3LM04X24GP(config-pmap-c)# set dscp 34
    IRSL3LM04X24GP(config-pmap-c)# priority
    IRSL3LM04X24GP(config)# interface range gi1/0/1-24
    IRSL3LM04X24GP(config-if-range)# service-policy input QoS-POLICY

    5.2 PFC Configuration for Lossless Fabric

    CSL3M04X48GP(config)# vlan 100 name ROCE-FABRIC
    CSL3M04X48GP(config)# interface range gi1/0/1-24
    CSL3M04X48GP(config-if-range)# dcbx mode ieee
    CSL3M04X48GP(config-if-range)# priority-flow-control mode on
    CSL3M04X48GP(config-if-range)# priority-flow-control priority 3 no-drop

    5.3 ECN Configuration

    IRSL3LM04X24GP(config)# ecn enable
    IRSL3LM04X24GP(config)# ecn threshold queue 0 50 75


    6. Lanaotek Product Comparison

    ProductPort ConfigUplinksBest Fit
    IRSL3LM-04X24GP-2D-Z824x GE4x 25G/100GLeaf switch for mid-size DC
    CSL3M-04X48GP-H2A-L48x GE4x 100G QSFP28Spine switch / AI Leaf
    LA-OT-10G3-1.4SFP2810G SR/LRLeaf-Spine optics
    2x100GBASE-SR4QSFP28100G SR4/LR4Spine aggregation optics


    Ready to Optimize Your Data Center Fabric?

    Keywords: data center switch, east-west traffic, leaf-spine architecture, ECMP, ECN, PFC, RoCEv2, AI cluster networking, Lanaotek CSL3M-04X48GP-H2A-L, data center fabric, QoS, RDMA.


    By Lanaotek
    By Lanaotek

    Lanaotek specializes in translating cutting-edge optical and Ethernet transmission technologies into clear, valuable insights that help our customers stay ahead in a fast-evolving digital world.

    By turning complex technical concepts into practical, business-driven content, we aim to empower decision-makers with the knowledge they need to make confident, future-ready choices.


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