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SoftMoW: Recursive and Reconfigurable Cellular WAN Architecture

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Presentation on theme: "SoftMoW: Recursive and Reconfigurable Cellular WAN Architecture"— Presentation transcript:

1 SoftMoW: Recursive and Reconfigurable Cellular WAN Architecture
Mehrdad Moradi Wenfei Wu Erran Li Z. Morley Mao

2 Current Mobile WANs Organized into rigid and very large regions
Minimal interactions among regions Centralized policy enforcement at PGWs UE1 UE2 UE3 Two Regions UE4

3 Mobile WAN Problems Suboptimal routing in large carriers
Lack of sufficiently close PGW (Path Inflation, PAM’14) Lack of support for seamless inter-region mobility No inter-PGW mobility support (DMM, Zuniga et.al., 2013) Scalability and reliability Centralized policy enforcement Ill-suited to adapt to new trends of mobile traffic Signaling storm problem is a major cause of path inflation

4 What is SoftMoW? Clean-slate architecture of cellular WANs
Scalable control plane and data plane Performs new global applications Runs Region optimization Supports Seamless mobility Enables optimal end to end paths Goal: millions of UEs and hundreds of thousands of Base stations

5 SoftMoW’s Cellular WAN
new global cellular apps avoid path inflation Seamlessly interconnected SDN switches nationwide Decentralized policy enforcement Sufficient # egress points middle-boxes placed in edge networks Base stations organized into groups Fine-grained classifier per BS (e.g., SofCell)

6 Challenge 1:Scalability
O(100K) data plane switches, base stations, and middleboxes O(100M) UEs distributed over a continent Scalable control plane? multiple controllers organized in flat topologies SoftMoW HyperFlow Hierarchical Recursive Reconfigurable ONOS Onix

7 Recursive and Reconfigurable Control Plane
Recursively, each controller in the hierarchy exposes: Non-leaf controller can reconfigure logical data plane devices Optimize hierarchy without global states G-Middlebox G-BS G-switch Hierarchical Control Plane Recursively partition the data plane network into logical regions and assign to controllers

8 Recursive Construction
Level 3 C5 G-BS3 GS 5 GS 6 Optimality? C3 C4 Level 2 Virtual Fabric: performance metrics per G-switch port pair GS 1 G-BS1 GS 2 G-BS2 GS 3 C0 C1 C2 Level 1 BS Group 1 SW1 BS Group 2 SW2 SW3 SW4 SW5 SW6

9 SoftMoW Controller Architecture
Network operating system (NOS) Contains core services Agnostic of cellular-specific applications Operator apps E.g., region optimization, HSS, PCRF Recursive abstraction app (RecA) Eastbound API for operator apps Agent exposes G-switch, G-BS, G-Middleboxes Management Plane Bootstraps the recursive control plane. E.g., IP assignment, tree configuration Agent communicates with the parent controller

10 Challenge 2: Topology Discovery
SW1 SW2 C1 C5 Level 3 GS 5 GS 6 G-BS3 LLDP protocol C3 C4 Scalable topology discovery without breaking abstractions? GS 1 GS 2 GS 3 Level 2 G-BS1 G-BS2 Each physical inter-switch link is visible to only one controller C0 C1 C2 SW1 SW5 SW2 SW3 SW4 Level 1 SW6 BS Group 1 BS Group 2

11 Core Service: Topology Discovery
Scalable and recursive link and switch discovery protocols Similar to LLDP Parallel-sequential periodical protocol G-switch discovery Inter-G-switch link discovery Abstract G-switch computation

12 Core Service: Topology Discovery
In SoftMoW, each link discovery message has: Stack field: stores the traversed path in the control plane Stack entry format: (Controller ID, G-switch ID, G-switch port) (C0, GS1, p1) From (GS1,p1) to (GS2, p4) p1 C0 p4 GS1 GS2 Origination (C1, SW2, p2) (C0, GS1, p1) C1 C2 (GS2, p4) p2 p3 Return SW1 SW2 (SW3, p3) SW3 SW4 Payload Stack

13 Challenge 3:Path Implementation
Scalable path implementation without breaking abstractions? Level 3 GS 5 GS 6 G-BS3 Controllers make local decisions C3 C4 Decisions made by a controller must be visible across its links GS 1 GS 2 GS 3 Level 2 G-BS1 G-BS2 C0 C1 C2 Label stacking: packing all local decisions into each packet SW1 SW2 SW3 SW4 SW5 Level 1 L1, L2, L3 SW6 The controller expects these two G-switches to process the traffic based on its local decisions. However, each of these G-switches represent a controller sub-hierarchy where child controllers Make local decisions. One can achieve this goal, by packing all decisions into each packet. BS Group 1 BS Group 2 Per packet stack

14 Recursive Label Swapping
Fine-grained classifications at base stations Any controller has its own local policy or label: Ingress switch: Pop parent label, Push local labels Egress switch: Pop local labels, Push parent label Root Level Parent Level Leaf Level

15 Challenge 4: Region Optimization
Region optimization and without global states? Level 3 GS 5 GS 6 G-BS3 Inter region handovers increase ‘’east-west’’ control plane load C3 C4 GS 1 GS 2 GS 3 Level 2 Require the intervention of at least three controllers G-BS1 G-BS2 C0 C1 C2 Regions should be refined to reduce such loads SW1 SW2 SW3 SW4 SW5 Level 1 SW6 Group 1 Group 2

16 App: Region Optimization and Reconfiguration
Handover patterns vary across time-of-day. Difficult to find static borders Design a greedy-iterative approach Regions at a higher level has higher priority Handover graphs to record patterns Nodes are G-BSes W(edge): handover frequency Handover-specific Reconfiguration Detach a G-BS from a source G-switch Associate with a destination G-switch Reconfiguration

17 App: Region Optimization and Reconfiguration
GS 2 G-BS2 SW1 GS 1 G-BS1 SW2 SW3 Group 1 Group 2 Initiator controller finds the G-BS with the highest gain Gain: Inter-region handover load reduction Contact the management plane (MP) MP Finds the leaf controllers Seamless control transfer at the leaf EQUAL ROLE Logical regions updated bottom-up Stop when: There is no gain All G-switches (recursive child regions) meet their max and min loads C3 G-BS1 GS 1 GS 2 G-BS2 C0 C1 SW1 SW2 SW3 Group 1 Group 2

18 Evaluation Prototype SoftMoW on top of the Floodlight and Mininet
Egress points using iPlane traces Core topology using RocketFuel Two-level SoftMoW Large scale simulation based on data from a LTE network ~1000 base stations and 1 million mobile devices Inferred BS groups based on handover patterns Generate uplink/downlink traffic based on actual demands Attach base station groups to access switches

19 Recursive Topology Discovery
A 2-level SoftMoW compared with flat topology discovery SoftMoW’s controllers discover between 44% and 58% faster Root cause: less queuing delay at each due to information hiding

20 Performance A 2-level SoftMoW compared with LTE network
Global routing for variable number of egress points End-to-end shortest paths 75th and 85th percentile RTT latencies reduce by 43% and 60% End-to-End Hop Count End-to-End Latency

21 Reconfiguration and Region Optimization
Inter-region handovers handled by the root over 48 hours 8 leaf region and 4 leaf region settings. High inter-region handovers in peak hours (e.g., inter-city movements) Root can reduce the load by 38.08% to 44.61%

22 Conclusion SoftMoW: A scalable architecture that is based on on effective recursive and reconfigurable abstractions Three core services: link discovery, routing, and path setup Three operator apps: global mobility, region optimization, and bearer management


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