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Oliver Michel*, Eric Keller*, Fernando Ramos · Oliver Michel*, Eric Keller*, Fernando Ramos^...
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Oliver Michel*, Eric Keller*, Fernando Ramos^
*University of Colorado Boulder, ^University of Lisbon
Network Defragmentation in Virtualized Data Centers
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Virtualized Data Centers
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HardwareServers, Networking Equipment, Cabling, etc.
Hypervisor
Virtual Machine
Services /
Applications
Virtual Machine
Services /
Applications
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• Cornerstone of cloud computing
• Coexistence of tenants on shared infrastructure
• Scaling of resources based on demand
Elasticity is one of its key drivers
Virtualized Data Centers
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• Different cloud applications require different topologies and different network guarantees
• A reality today: VMware NVP [NSDI’14], Microsoft AccelNet [NSDI’18], Google Andromeda [NSDI’18]
New requirement: network virtualization
Application BApplication A Application C
virtual
networks
substrate
network
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• Core algorithmic challenge: Virtual Network Embedding (VNE)
• Map the virtual network requests onto the substrate infrastructure
Existing VNE approaches lack a fundamental requirement: elasticity
Limitations of existing approaches
Application BApplication A Application C
virtual
networks
substrate
network
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Contribution #1: new scaling primitives to VNE
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Expand VN / Contract VN
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Problem: network fragmentation
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Embed
VN
Remove
VN
Expand
VN
Contract
VN
Virtual Network Lifecycle
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• Fragmentation introduces several problems
• Longer path lengths, higher latencies
• Harder to map new VNs, lower acceptance ratios
Problem: network fragmentation
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expand()
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• Extended an existing VNE algorithm [Ballani11] with
expand and contract primitives
• Cloud simulator
• Leaf-Spine physical topology
• Different virtual topologies
Implementation
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Virtual Network Topologies: VC, VOC, 3T
Leaf-Spine Physical Network Topology
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• Simulation: leaf-spine topology; 28 switches and 384 servers; adding, deleting, expanding, or contracting virtual networks 1000 times (averaged over 10 runs; randomized workloads)
• Longer path lengths -> higher latencies and cost, poorer application performance, prolonged job completion times
• Lower acceptance ratios -> reduced revenue
Costs of fragmentation
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Contribution #2: new management primitive
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Expand VN / Contract
VN
Network
defragmentation
+
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Network Defragmentation
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• Optimize VNE algorithms to avoid fragmentation?
• Hard: no prior knowledge of upcoming request
We explore a diferente approach: explicit
defragmentation by periodically running a network
migration algorithm (e.g., LIME [SOCC14])
defrag()
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• (Naive) remap of all virtual networks
1. Unmap all VNs
2. Sort by VN size (biggest to smallest)
3. Re-embed each VN in order
Network defragmentation heuristic
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Evaluation: mean path length stretch
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Lower path lengths reduce latencies and cost, improve application performance, and by improving efficiency increase provider profit
Evaluation: fraction of paths by path length
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before
defrag
after
defrag
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• Future work
• better defragmentation heuristics that minimize migration cost
• integration with network migration
Summary
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THANKS.
QUESTIONS?
Oliver Michel
Fernando Ramos