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
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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
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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
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Distributed Network Optimization Model
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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
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The Zoning Problem Formulation • An LP problem (see details in the paper)
• A binary integer programming problem - NP-hard problem
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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)
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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.
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A Visual Comparison
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One-step approach Two-step approach
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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
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Simulation Results • ZIF = 1 for the assignment heuristic
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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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