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Sharing the Data Center Network Alan Shieh, Srikanth Kandula, Albert Greenberg, Changhoon Kim, Bikas Saha Microsoft Research, Cornell University, Windows Azure, Microsoft Bing NSDI’11
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NSLab, RIIT, Tsinghua Univ Outline Introduction Seawall Design Evaluation Discussion Summary 2
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NSLab, RIIT, Tsinghua Univ Introduction 3 Data Center Provide compute and storage resources for web search, content distribution and social networking Achieve cost efficiencies and on-demand scaling Highly-multiplexed shared environments VMs and tasks from multiple tenants coexisting in the same cluster Network performance interference and denial of service attacks is high
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NSLab, RIIT, Tsinghua Univ Introduction 4 Problem with network sharing in datacenters Performance interference in infrastructure cloud services Network usage is a distributed resource Large number of flows Higher rate UDP flows Poorly-performing schedules in Cosmos (Bing) Poor sharing of the network leads to poor performance and wasted resources
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NSLab, RIIT, Tsinghua Univ Introduction 5 Poor sharing of the network leads to poor performance and wasted resources * Optimal bandwidth shares is non-goal Require perfect knowledge about client demands Map-Reduce workloads (5 maps and 1 reduce)
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NSLab, RIIT, Tsinghua Univ Introduction 6 Magnitude of scale and churn The number of classes to share bandwidth among is large and varies frequently Cloud datacenters traffic is even harder to predict
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NSLab, RIIT, Tsinghua Univ Introduction 7 Requirements Traffic Agnostic, Simple Service Interface Require no changes to network topology or hardware Scale to large numbers of tenants and high churn Enforce sharing without sacrificing efficiency
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NSLab, RIIT, Tsinghua Univ 8 VM 1VM 2VM 3 (weight = 2) VM 2 flow 1 VM 2 flow 2 VM 2 flow 3 VM 3: ~50% VM 2: ~25% VM 1: ~25%
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NSLab, RIIT, Tsinghua Univ In-network queuing and rate limiting Network-to-source congestion control (Ethernet QCN) End-to-end congestion control (TCP) HV Guest HV Guest HV Guest HV Guest HV Guest HV Guest Throttle send rate Existing mechanisms are insufficient Detect congestion Not scalable. Can underutilize links. Requires new hardware. Inflexible policy. Poor control over allocation. Guests can change TCP stack.
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NSLab, RIIT, Tsinghua Univ Seawall Design 10 Congestion controlled hypervisor-to- hypervisor tunnels HV Guest HV Guest
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NSLab, RIIT, Tsinghua Univ Seawall Design 11 Bandwidth Allocator Weighted additive increase, multiplicative decrease (AIMD) derived from TCP-Reno Decrease: Increase: Three improvements Combine feedback from multiple destinations Modify the adaptation logic to converge quickly and stay at equilibrium longer Nest traffic
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NSLab, RIIT, Tsinghua Univ Seawall Design 12 Step 1 : Using distributed control loops to determine per-link, per-entry share Lacking of XCP, QCN, SideCar
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NSLab, RIIT, Tsinghua Univ Seawall Design 13 Step 2 : Convert per-link, per-entity shares to per- link, per-tunnel shares Use β=0.9, allocates β fraction of the link bandwidth proportional to current usage and the rest evenly across destinations The allowed share of the first destination converges to within 20% of its demand in four iterations Orange entity has demands (2x, x, x) to the three destinations
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NSLab, RIIT, Tsinghua Univ Seawall Design 14 Improving the Rate Adaptation Logic Use control laws from CUBIC to achieve faster convergence, longer dwell time at the equilibrium point, and higher utilization than AIMD If switches support ECN, Seawall also incorporates the control laws from DCTCP Smoothed multiplicative decrease Concave or convex increase
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NSLab, RIIT, Tsinghua Univ Seawall Design 15 Less than goal, concave increase Above goal, convex increase
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NSLab, RIIT, Tsinghua Univ Seawall Design 16 Nesting traffic – deferring congestion control If a sender always sends less than the rate allowed by Seawall, she can launch a short overwhelming burst of traffic UDP and TCP flows behave differently: full burst UDP flow immediately uses all the rate and a set of TCP flows can take several RTTs to ramp up TCP flow queries rate limiter
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NSLab, RIIT, Tsinghua Univ Evaluation 17 Traffic-agnostic network allocation Selfish traffic = Full-burst UDP
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NSLab, RIIT, Tsinghua Univ Evaluation 18 Selfish traffic = Many TCP flows
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NSLab, RIIT, Tsinghua Univ Evaluation 19 Selfish traffic = Arbitrarily many destinations
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NSLab, RIIT, Tsinghua Univ Discussion 20 Seawall and cloud data centers Sharing policies Work-conserving, max-min fair Achieve higher utilization Dynamic weight changes System architecture Support rate- and window-based limiters Based on both hardware and software Partitioning sender/receiver functionality Receiver-driven approach customized for map-reduce
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NSLab, RIIT, Tsinghua Univ Summary 21 Seawall is a first step towards providing data center administrators with tools to divide their network across the sharing entities without requiring any cooperation from the entities Well-suited to emerging hardware trends in data center and virtualization hardware
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