zoning for hierarchical network optimization in...

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Xu Li, Petar Djukic, Hang Zhang

“Zoning for Hierarchical Network Optimization in SDN”

HUAWEI TECHNOLOGIES CO., LTD. Huawei Confidential

Outline •  Introduction

•  Distributed network optimization model

•  Zoning problem formulation

•  Our proposals

•  Performance evaluation

•  Conclusion


Introduction •  In SDN, network optimization is done centrally by an controller

-  Supporting information is collected at the SDN controller •  Nodes report their status, e.g. loading, resource availability, resource quality, etc.

•  Nodes report their incidental links status, e.g. remaining capacity, buffer size, etc.

-  Computation logic is executed at the SDN controller -  Provisioning is made by the SDN controller

•  The SDN controller issues control commends to data plane nodes

•  Fully centralized network optimization implies hardware complexity, communication overhead and provisioning delay -  The SDN controller needs powerful computing module to handle large-scale

optimization

-  Its computational capability limitation causes large delay in large-scale optimization

-  Global information collection and provisioning occupy large bandwidth and incur large end-to-end network delays


Related Work •  Distributed network optimization can mitigate these problems

-  Network status information is transmitted locally -  Computational load is distributed throughout the network

-  Provisioning decisions are sent locally

•  Generally speaking, distributed network optimization organizes the network into disjoint zones (without node sharing), each with a local zone controller -  Each zone controller solves a local zone optimization problem and performs local

provisioning •  Complexity is shared among multiple controllers •  Network status overhead is local only

-  A master controller coordinates zone controllers’ computation to ensure local provisioning lead to a consistent (optimal) global provisioning

•  By communicating information about edge shared between zones

•  Current distributed network optimization solutions either take each individual node as a zone or lack specific zoning strategies


Distributed Network Optimization Model


Size-balanced, Min-Overhead Zoning •  The objective is to minimize the control overhead of distributed

network optimization while balancing zone size -  Size balancing is for balancing the processing load among controllers

•  The control overhead comets from four aspects -  Initialization overhead

•  Initialize zone controllers for performing distributed netw. optimization

-  Collection overhead

•  For gathering network state at zone controllers

-  Coordination overhead

•  For coordinating zone controllers computation

-  Provisioning overhead

•  For issuing control commands from zone controllers to network nodes


The Zoning Problem Formulation •  An LP problem (see details in the paper)

•  A binary integer programming problem -  NP-hard problem


One-Step Approach •  Task assignment heuristic (following the MinMin framework [14]) to

achieved trade-off of size balancing and overhead minimization under the controller of a zone imbalance cost factor (ZIF)


Two-Step Approach •  Step 1: Partitioning (solving a balanced minimum k cut problem)

-  Divide the network topology graph into k disjoint size-balanced pieces, called partitions, with a minimum number of edges between partitions.

•  Cut minimization is for simplifying coordination

-  Can be solved using an efficient heuristic algorithm [13]

•  Step 2: Controller dispatch (solving a task assignment problem) -  Dispatch a distinct SDN controller to each partition as zone controller for

distributed optimization control overhead minimization

-  Can be solved optimally by any LP solver in polynomial time.


A Visual Comparison

One-step approach Two-step approach


Simulation Setup •  Random network topology

-  Generated using the “generalized linear preference” topology model [17].

•  Four controllers randomly dropped -  One of them is selected to be the master controller

•  Four zones are to be created

•  Results are averaged over 10 random scenarios for each setting


Simulation Results •  ZIF = 1 for the assignment heuristic


Simulation Results

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Conclusion •  Studied the problem of zoning for distributed network optimization

-  The zoning problem is formulated to balance the zone size and minimize the communication overhead of the optimization process

•  Presented two heuristic solutions, the assignment heuristic and the partitioning heuristic

•  Reported some preliminary simulation results

•  Future work includes extended simulation study and study of zoning on optimization convergence

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