Data Center Networks. Networking Case Studies. Cloud CompuMng. Cloud CompuMng. Cloud Service Models. Cloud Service Models

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1 Networking Case tudies Center Center Networks Enterprise Backbone Jennifer Rexford CO 461: Computer Networks Lectures: MW 10-10:50am in Architecture N101 Cellular hfp:// Wireless 2 Cloud CompuMng Cloud CompuMng ElasMc resources Expand and contract resources Pay- per- use Infrastructure on demand MulM- tenancy MulMple independent users ecurity and resource isolamon AmorMze the cost of the (shared) infrastructure Flexible service management 3 4 Cloud ervice Models owware as a ervice Provider licenses applicamons to users as a service E.g., customer relamonship management, e- mail,.. Avoid costs of installamon, maintenance, patches, PlaZorm as a ervice Provider offers plazorm for building applicamons E.g., Google s - Engine Avoid worrying about scalability of plazorm Cloud ervice Models Infrastructure as a ervice Provider offers raw compumng, storage, and network E.g., Amazon s ElasMc CompuMng Cloud (EC2) Avoid buying servers and esmmamng resource needs 5 6 1

2 Enabling Technology: VirtualizaMon MulMple virtual machines on one physical machine licamons run unmodified as on real machine VM can migrate from one computer to another MulM- Tier licamons licamons consist of tasks Many separate components Running on different machines Commodity computers Many general- purpose computers Not one big mainframe Easier scaling 7 MulM- Tier licamons Front end erver Center Network 9 10 Virtual witch in erver Top- of- Rack Architecture Rack of servers Commodity servers And top- of- rack switch Modular design Preconfigured racks Power, network, and storage cabling

3 Aggregate to the Next Level Modularity, Modularity, Modularity Containers Many containers Center Network Topology Capacity Mismatch Internet ~ 200:1 ~ 40:1 ~ 5:1 ~ 1,000 servers/pod Key = Core Router AR = Access Router = Ethernet witch A = Rack of app. servers A A A A A A Internet DC- Layer 3 DC- Layer Center RouMng ~ 1,000 servers/pod == IP subnet Key = Core Router (L3) AR = Access Router (L3) = Ethernet witch (L2) A = Rack of app. servers Reminder: Layer 2 vs. Layer 3 Ethernet switching (layer 2) Cheaper switch equipment Fixed addresses and auto- configuramon eamless mobility, migramon, and failover IP roumng (layer 3) calability through hierarchical addressing Efficiency through shortest- path roumng MulMpath roumng through equal- cost mulmpath o, like in enterprises Connect layer- 2 islands by IP routers 18 3

4 licamons Inside Centers Case tudy: Performance Diagnosis in Centers.... hfp:// ~minlanyu/writeup/nsdi11.pdf Front end erver s Challenges of center Diagnosis Diagnosing in Today s Center MulM- Mer applicamons Hundreds of applicamon components Tens of thousands of servers Evolving applicamons Add new features, fix bugs Change components while app is smll in operamon Human factors Developers may not understand network well Nagle s algorithm, delayed ACK, etc. logs: #Reqs/sec Response Vme 1% req. >200ms delay Host O NAP: Diagnose net- app interacvons Packet sniffer Packet trace: Filter out trace for long delay req. witch logs: #bytes/pkts per minute Problems of Different Logs TCP tamsmcs logs: licavon- specific Host O NAP: Generic, fine- grained, and lightweight Packet sniffer Packet trace: Too expensive witch logs: Too coarse- grained Instantaneous snapshots #Bytes in the send buffer CongesMon window size, receiver window size napshots based on random sampling CumulaMve counters #FastRetrans, #Timeout RTT esmmamon: #amplertt, #umrtt RwinLimitTime Calculate difference between two polls Runs everywhere, all the Vme

5 IdenMfying Performance Problems NAP Architecture ender end Buffer Network Receiver Not any other problems end buffer is almost full #Fast retransmission #Timeout Direct measure RwinLimitTime Delayed ACK diff(umrtt)/diff(amplertt) > MaxDelay ampling Inference 25 At each host for every connecvon Collect data Direct access to O - Polling per- connecvon stavsvcs: napshots (#bytes in send buffer) CumulaVve counters (#FastRestrans) - AdapVve tuning of polling rate 26 NAP Architecture NAP Architecture At each host for every connecvon Collect data Performance Classifier At each host for every connecvon Collect data Performance Classifier Cross- connecvon correlavon Classifying based on the life of data transfer - Algorithms for detecvng performance problems - Based on direct measurement in the O 27 Direct access to data center configuravons - Input Topology, rouvng informavon Mapping from connecvons to processes/apps - Correlate problems across connecvons haring the same switch/link, app code 28 NAP Deployment ProducMon data center 8K machines, 700 applicamons Ran NAP for a week, collected petabytes of data ender Characterizing Perf. LimitaMons #s that are limited for > 50% of the time 551 s BoFlenecked by CPU, disk, etc. low due to app design (small writes) IdenMfied 15 major performance problems Operators: Characterize key problems in data center Developers: Quickly pinpoint problems in app sowware, network stack, and their interacmons 29 end Buffer Networ k Receiver 1 6 s end buffer not large enough Fast retransmission Timeout 8 s Not reading fast enough (CPU, disk, etc.) 144 s Not ACKing fast enough (Delayed ACK) 30 5

6 Delayed ACK Delayed ACK caused significant problems Delayed ACK was used to reduce bandwidth usage and server interrupmon A +ACK. ACK B B has data to send B doesn t have data to send 200 ms Delayed ACK should be disabled in data centers 31 Diagnosing Delayed ACK with NAP Monitor at the right place calable, low overhead data collecmon at all hosts Algorithms to idenmfy performance problems IdenMfy delayed ACK with O informamon Correlate problems across connecmons IdenMfy the apps with significant delayed ACK issues Fix the problem with operators and developers Disable delayed ACK in data centers 32 Conclusion Cloud compumng Major trend in IT industry Today s equivalent of factories center networking Regular topologies interconnecmng VMs Mix of Ethernet and IP networking Modular, mulm- Mer applicamons New ways of building applicamons New performance challenges Load Balancing Load Balancers pread load over server replicas Present a single public address (VIP) for a service Direct each request to a server replica ervers Wide- Area Network Centers ervers Virtual IP (VIP) DN erver Internet DN-based site 36 selection Clients 6

7 Wide- Area Network: Ingress Proxies ervers Centers ervers Proxy Proxy Clients 37 7

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