Data Center Networks Jennifer Rexford COS 461 Computer
- Slides: 37
Data Center Networks Jennifer Rexford COS 461: Computer Networks Lectures: MW 10 -10: 50 am in Architecture N 101 http: //www. cs. princeton. edu/courses/archive/spr 12/cos 461/
Networking Case Studies Data Center Enterprise Backbone Cellular Wireless 2
Cloud Computing 3
Cloud Computing • Elastic resources – Expand contract resources – Pay-per-use – Infrastructure on demand • Multi-tenancy – Multiple independent users – Security and resource isolation – Amortize the cost of the (shared) infrastructure • Flexible service management 4
Cloud Service Models • Software as a Service – Provider licenses applications to users as a service – E. g. , customer relationship management, e-mail, . . – Avoid costs of installation, maintenance, patches, … • Platform as a Service – Provider offers platform for building applications – E. g. , Google’s App-Engine – Avoid worrying about scalability of platform 5
Cloud Service Models • Infrastructure as a Service – Provider offers raw computing, storage, and network – E. g. , Amazon’s Elastic Computing Cloud (EC 2) – Avoid buying servers and estimating resource needs 6
Enabling Technology: Virtualization • Multiple virtual machines on one physical machine • Applications run unmodified as on real machine • VM can migrate from one computer to another 7
Multi-Tier Applications • Applications consist of tasks – Many separate components – Running on different machines • Commodity computers – Many general-purpose computers – Not one big mainframe – Easier scaling
Multi-Tier Applications Front end Server Aggregator … … Aggregator … Worker 9 Worker … Worker
Data Center Network 10
Virtual Switch in Server 11
Top-of-Rack Architecture • Rack of servers – Commodity servers – And top-of-rack switch • Modular design – Preconfigured racks – Power, network, and storage cabling 12
Aggregate to the Next Level 13
Modularity, Modularity • Containers • Many containers 14
Data Center Network Topology Internet CR CR . . . AR AR S S S A A … A ~ 1, 000 servers/pod 15 AR AR . . . • • Key CR = Core Router AR = Access Router S = Ethernet Switch A = Rack of app. servers
Capacity Mismatch CR AR AR S S CR ~ 200: 1 AR AR S S S A A … A ~ 40: 1 S A 16 ~ S 5: 1 A … A S S A A … A . . .
Data-Center Routing Internet CR DC-Layer 3 CR . . . AR AR SS SS SS A A … A DC-Layer 2 ~ 1, 000 servers/pod == IP subnet 17 AR AR . . . • • Key CR = Core Router (L 3) AR = Access Router (L 3) S = Ethernet Switch (L 2) A = Rack of app. servers
Reminder: Layer 2 vs. Layer 3 • Ethernet switching (layer 2) – Cheaper switch equipment – Fixed addresses and auto-configuration – Seamless mobility, migration, and failover • IP routing (layer 3) – Scalability through hierarchical addressing – Efficiency through shortest-path routing – Multipath routing through equal-cost multipath • So, like in enterprises… 18 – Connect layer-2 islands by IP routers
Case Study: Performance Diagnosis in Data Centers http: //www. eecs. berkeley. edu/~minl anyu/writeup/nsdi 11. pdf 19
Applications Inside Data Centers …. …. Front end Aggregator Server …. Workers 20
Challenges of Datacenter Diagnosis • Multi-tier applications – Hundreds of application components – Tens of thousands of servers • Evolving applications – Add new features, fix bugs – Change components while app is still in operation • Human factors – Developers may not understand network well – Nagle’s algorithm, delayed ACK, etc. 21
Diagnosing in Today’s Data Center App logs: #Reqs/sec Response time 1% req. >200 ms delay Host App OS SNAP: Diagnose net-app interactions Packet trace: Filter out trace for long delay req. Packet sniffer Switch logs: #bytes/pkts per minute 22
Problems of Different Logs App logs: Application-specific Packet trace: Too expensive Host App OS SNAP: Generic, fine-grained, and lightweight Runs everywhere, all the time Packet sniffer Switch logs: Too coarse-grained 23
TCP Statistics • Instantaneous snapshots – #Bytes in the send buffer – Congestion window size, receiver window size – Snapshots based on random sampling • Cumulative counters – #Fast. Retrans, #Timeout – RTT estimation: #Sample. RTT, #Sum. RTT – Rwin. Limit. Time – Calculate difference between two polls 24
Identifying Performance Problems Sender App – Not any other problems Send Buffer – Send buffer is almost full Network Receiver – #Fast retransmission – #Timeout – Rwin. Limit. Time – Delayed ACK Sampling Direct measure Inference diff(Sum. RTT)/diff(Sample. RTT) > Max. Delay 25
SNAP Architecture At each host for every connection Collect data Direct access to OS - Polling per-connection statistics: • Snapshots (#bytes in send buffer) • Cumulative counters (#Fast. Restrans) - Adaptive tuning of polling rate 26
SNAP Architecture At each host for every connection Collect data Performance Classifier Classifying based on the life of data transfer - Algorithms for detecting performance problems - Based on direct measurement in the OS 27
SNAP Architecture At each host for every connection Collect data Performance Classifier Crossconnection correlation Direct access to data center configurations - Input • Topology, routing information • Mapping from connections to processes/apps - Correlate problems across connections • Sharing the same switch/link, app code 28
SNAP Deployment • Production data center – 8 K machines, 700 applications – Ran SNAP for a week, collected petabytes of data • Identified 15 major performance problems – Operators: Characterize key problems in data center – Developers: Quickly pinpoint problems in app software, network stack, and their interactions 29
Characterizing Perf. Limitations Sender App #Apps that are limited for > 50% of the time 551 Apps – Bottlenecked by CPU, disk, etc. – Slow due to app design (small writes) Send Buffer 1 App – Send buffer not large enough Network 6 Apps – Fast retransmission – Timeout Receiver 8 Apps – Not reading fast enough (CPU, disk, etc. ) 144 30 Apps – Not ACKing fast enough (Delayed ACK)
Delayed ACK • Delayed ACK caused significant problems – Delayed ACK was used to reduce bandwidth usage and server interruption A B Data B has data K C to send Data+A …. Data Delayed ACK should be disabled in data centers B doesn’t have data to send 200 ms ACK 31
Diagnosing Delayed ACK with SNAP • Monitor at the right place – Scalable, low overhead data collection at all hosts • Algorithms to identify performance problems – Identify delayed ACK with OS information • Correlate problems across connections – Identify the apps with significant delayed ACK issues • Fix the problem with operators and developers – Disable delayed ACK in data centers 32
Conclusion • Cloud computing – Major trend in IT industry – Today’s equivalent of factories • Data center networking – Regular topologies interconnecting VMs – Mix of Ethernet and IP networking • Modular, multi-tier applications – New ways of building applications – New performance challenges 33
Load Balancing 34
Load Balancers • Spread load over server replicas – Present a single public address (VIP) for a service – Direct each request to a server replica 10. 10. 1 Virtual IP (VIP) 192. 121. 10. 10. 10. 2 10. 10. 3 35
Wide-Area Network Servers Router DNS Server DNS-based site 36 selection Data Centers Servers Router Internet Clients
Wide-Area Network: Ingress Proxies Data Centers Servers Router Proxy Clients 37
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