Future Nets: Beyond IP Networking

1. Future Nets: Beyond IP Networking Building Large Networks (at the edge)… • Large Scale Ethernets and enterprise networks - Scaling Ethernets to millions of nodes • Building networks for the backend of the Internet – networks for cloud computing and data centers Slides by Prof. Zhi-Li Zhang, UMN Advanced Networking Course CSci5221

2. Large ISPs and enterprise networks Large data centers with thousands or tens of thousands machines Metro Ethernet More and more devices are “Internet-capable” and plugged in Likely rich and more diverse network topology and connectivity Even within a Single Administrative Domain

3. Data Center Networks • Data centers • Backend of the Internet • Mid- (most enterprises) to mega-scale (Google, Yahoo, MS, etc.) • E.g., A regional DC of a major on-line service provider consists of 25K servers + 1K switches/routers • To ensure business continuity, and to lower operational cost, DCs must • Adapt to varying workload Breathing • Avoid/Minimize service disruption (when maintenance, or failure)  Agility • Maximize aggregate throughput  Load balancing

4. Challenges posed by These Trends • Scalability: capability to connect tens of thousands, millions or more users and devices • routing table size, constrained by router memory, lookup speed • Mobility: hosts are more mobile • need to separate location (“addressing”) and identity (“naming”) • Availability & Reliability: must be resilient to failures • need to be “proactive” instead of reactive • need to localize effect of failures • Manageability: ease of deployment, “plug-&-play” • need to minimize manual configuration • self-configure, self-organize, while ensuring security and trust • …….

5. Quick Overview of Ethernet • Dominant wired LAN technology • Covers the first IP-hop in most enterprises/campuses • First widely used LAN technology • Simpler, cheaper than token LANs, ATM, and IP • Kept up with speed race: 10 Mbps and now to 40 Gbps • Soon 100 Gbps would be widely available Metcalfe’s Ethernet sketch

6. Ethernet Frame Structure • Addresses: source and destination MAC addresses • Flat, globally unique, and permanent 48-bit value • Adaptor passes frame to network-level protocol • If destination address matches the adaptor • Or the destination address is the broadcast address • Otherwise, adapter discards frame • Type: indicates the higher layer protocol • Usually IP

7. Interaction w/ the Upper Layer (IP) • Bootstrapping end hosts by automating host configuration (e.g., IP address assignment) • DHCP (Dynamic Host Configuration Protocol) • Broadcast DHCP discovery and request messages • Bootstrapping each conversation by enabling resolution from IP to MAC addr • ARP (Address Resolution Protocol) • Broadcast ARP requests • Both protocols work via Ethernet-layer broadcasting (i.e., shouting!) • Ethernet broadcast domain - A group of hosts and switches to which the same broadcast or flooded frame is delivered • Too large a broadcast domain leads to • Excessive flooding and broadcasting overhead • Insufficient security/performance isolation

8. State of the Practice: A Hybrid Architecture Enterprise networks comprised of Ethernet-based IP subnets interconnected by routers Ethernet Bridging - Flat addressing - Self-learning - Flooding - Forwarding along a tree R R IP Routing (e.g., OSPF) - Hierarchical addressing - Subnet configuration - Host configuration - Forwarding along shortest paths R R Broadcast Domain (LAN or VLAN) R

9. Ethernet Bridging: “Routing” at L2 • Routing determines paths to destinations through which traffic is forwarded • Routing takes place at any layer (including L2) where devices are reachable across multiple hops P2P, or CDN routing App Layer Overlay routing IP routing IP Layer Ethernet bridging Link Layer

10. Ethernet (Layer-2) “Routing” • Self-learning algorithm for dynamically building switch (forwarding) tables • “Eavesdrop” on source MACs of data packets • Associate source MACs with port # (cached, “soft-state”) • Forwarding algorithm • Forwarding algorithm • If dst MAC found in switch table, send to the corresp. port • Otherwise, flood to all ports (except the one it comes from) • Dealing with “loopy” topologies • Running (periodically) spanning tree algorithm to convert it into a tree (rooted at an “arbitrary” node) • 802.11 Wireless LANs use somewhat similar methods • Use the same 48-bit MAC addresses more complex frame structures; • End hosts need to explicitly associate with APs

11. Layer 2 vs. Layer 3 Again Neither bridging nor routing is satisfactory. Can’t we take only the best of each?

12. SEATTLE (Scalable Ethernet ArchiTecTure for Larger Enterprises) • Plug-and-playable enterprise architecture ensuring both scalability and efficiency • Objectives • Avoiding flooding • Restraining broadcasting • Keeping forwarding tables small • Ensuring path efficiency • SEATTLE architecture – design principles • Hash-based location management • Shortest-path forwarding • Responding to network dynamics (reactive location resolution and caching) • Lessons • Trading a little data-plane efficiency for huge control-plane scalability makes a qualitatively different system

13. Seattle Optimized forwarding directly from D to A y Deliver to x x C Host discovery or registration Traffic to x A Tunnel to egress node, A Hash(F(x) = B) Tunnel to relay switch, B Hash (F(x) = B) D Entire enterprise (A large single IP subnet) LS core Notifying<x, A> to D B Store<x, A> atB E Switches End-hosts Control flow Data flow

14. Cloud Computing and Data Centers: • What’s Cloud Computing? • Data Centers and “Computing at Scale” • Case Studies: • Google File System • Map-Reduce Programming Model Optional Material • Google Bigtable

15. Why Study this: they represent part of current and “future” trends how applications will be serviced, delivered, … what are important “new” networking problems? more importantly, what lessons can we learn in terms of (future) networking design? closely related, and there are many similar issues/challenges (availability, reliability, scalability, manageability, ….) (but of course, there are also unique challenges in networking) Cloud Computing and Data Centers

16. Internet and Web • Simple client-server model • a number of clients served by a single server • performance determined by “peak load” • doesn’t scale well (e.g., server crashes), when # of clients suddenly increases -- “flash crowd” • From single server to blade server to server farm (or data center)

17. Internet and Web … • From “traditional” web to “web service” (or SOA) • no longer simply “file” (or web page) downloads • pages often dynamically generated, more complicated “objects” (e.g., Flash videos used in YouTube) • HTTP is used simply as a “transfer” protocol • many other “application protocols” layered on top of HTTP • web services & SOA (service-oriented architecture) • A schematic representation of “modern” web services database, storage, computing, … web rendering, request routing, aggregators, … front-end back-end

18. Data Center and Cloud Computing • Data center: large server farms + data warehouses • not simply for web/web services • managed infrastructure: expensive! • From web hosting to cloud computing • individual web/content providers: must provision for peak load • Expensive, and typically resources are under-utilized • web hosting: third party provides and owns the (server farm) infrastructure, hosting web services for content providers • “server consolidation” via virtualization Under client web service control App Guest OS VMM

19. Cloud Computing • Cloud computing and cloud-based services: • beyond web-based “information access” or “information delivery” • computing, storage, … • Cloud Computing: NIST Definition "Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction." • Models of Cloud Computing • “Infrastructure as a Service” (IaaS), e.g., Amazon EC2, Rackspace • “Platform as a Service” (PaaS), e.g., Micorsoft Azure • “Software as a Service” (SaaS), e.g., Google

20. With thousands of servers within a data center, How to write applications (services) for them? How to allocate resources, and manage them? in particular, how to ensure performance, reliability, availability, … Scale and complexity bring other key challenges with thousands of machines, failures are the default case! load-balancing, handling “heterogeneity,” … data center (server cluster) as a “computer” “super-computer” vs. “cluster computer” A single “super-high-performance” and highly reliable computer vs. a “computer” built out of thousands of “cheap & unreliable” PCs Pros and cons? Data Centers: Key Challenges

21. Google File System (GFS) a “file system” (or “OS”) for “cluster computer” An “overlay” on top of “native” OS on individual machines designed with certain (common) types of applications in mind, and designed with failures as default cases Google MapReduce (cf. Microsoft Dryad) MapReduce: a new “programming paradigm” for certain (common) types of applications, built on top of GFS Other examples (optional): BigTable: a (semi-) structured database for efficient key-value queries, etc. , built on top of GFS Amazon Dynamo:A distributed <key, value> storage system high availability is a key design goal Google’s Chubby, Sawzall, etc. Open source systems: Hadoop, … Case Studies

22. Google Scale and Philosophy • Lots of data • copies of the web, satellite data, user data, email and USENET, Subversion backing store • Workloads are large and easily parallelizable • No commercial system big enough • couldn’t afford it if there was one • might not have made appropriate design choices • But truckloads of low-cost machines • 450,000 machines (NYTimes estimate, June 14th 2006) • Failures are the norm • Even reliable systems fail at Google scale • Software must tolerate failures • Which machine an application is running on should not matter • Firm believers in the “end-to-end” argument • Care about perf/$, not absolute machine perf

23. Cluster Scheduling Master Lock Service GFS Master Machine 2 Machine 3 Machine 1 BigTableServer UserTask 1 BigTableServer BigTable Master UserTask User Task 2 SchedulerSlave GFSChunkserver SchedulerSlave GFSChunkserver SchedulerSlave GFSChunkserver Linux Linux Linux Typical Cluster at Google

24. Google: System Building Blocks • Google File System (GFS): • raw storage • (Cluster) Scheduler: • schedules jobs onto machines • Lock service: Chubby • distributed lock manager • also can reliably hold tiny files (100s of bytes) w/ high availability • 5 replicas (need majority vote) • Bigtable: • a multi-dimensional database • MapReduce: • simplified large-scale data processing

25. Google File System Key Design Considerations • Component failures are the norm • hardware component failures, software bugs, human errors, power supply issues, … • Solutions: built-in mechanisms for monitoring, error detection, fault tolerance, automatic recovery • Files are huge by traditional standards • multi-GB files are common, billions of objects • most writes (modifications or “mutations”) are “append” • two types of reads: large # of “stream” (i.e., sequential) reads, with small # of “random” reads • High concurrency (multiple “producers/consumers” on a file) • atomicity with minimal synchronization • Sustained bandwidth more important than latency

26. GFS Architectural Design • A GFS cluster: • a single master + multiple chunkservers per master • running on commodity Linux machines • A file: a sequence of fixed-sized chunks (64 MBs) • labeled with 64-bit unique global IDs, • stored at chunkservers (as “native” Linux files, on local disk) • each chunk mirrored across (default 3) chunkservers • master server: maintains all metadata • name space, access control, file-to-chunk mappings, garbage collection, chunk migration • why only a single master? (with read-only shadow masters) • simple, and only answer chunk location queries to clients! • chunk servers (“slaves” or “workers”): • interact directly with clients, perform reads/writes, …

27. GFS Architecture: Illustration Separation of control and data flows

28. GFS: Summary • GFS is a distributed file system that support large-scale data processing workloads on commodity hardware • GFS has different points in the design space • Component failures as the norm • Optimize for huge files • Success: used actively by Google to support search service and other applications • But performance may not be good for all apps • assumes read-once, write-once workload (no client caching!) • GFS provides fault tolerance • Replicating data (via chunk replication), fast and automatic recovery • GFS has the simple, centralized master that does not become a bottleneck • Semantics not transparent to apps (“end-to-end” principle?) • Must verify file contents to avoid inconsistent regions, repeated appends (at-least-once semantics)

29. Google MapReduce • The problem • Many simple operations in Google • Grep for data, compute index, compute summaries, etc • But the input data is large, really large • The whole Web, billions of Pages • Google has lots of machines (clusters of 10K etc) • Many computations over VERY large datasets • Question is: how do you use large # of machines efficiently? • Can reduce computational model down to two steps • Map: take one operation, apply to many many data tuples • Reduce: take result, aggregate them • MapReduce • A generalized interface for massively parallel cluster processing

30. Data Center Networking Major Theme: What are new networking issues posed by large-scale data centers? • Network Architecture? • Topology design? • Addressing? • Routing? • Forwarding? CSci5221: Data Center Networking, and Large-Scale Enterprise Networks: Part I

31. Data Center Interconnection Structure • Nodes in the system: racks of servers • How are the nodes (racks) inter-connected? • Typically a hierarchical inter-connection structure • Today’s typical data center structure Cisco recommended data center structure: starting from the bottom level • rack switches • 1-2 layers of (layer-2) aggregation switches • access routers • core routers • Is such an architecture good enough?

32. Cisco Recommended DC Structure: Illustration Internet Internet CR CR Data Center Layer 3 … AR AR AR AR Layer 2 LB LB S S • Key: • CR = L3 Core Router • AR = L3 Access Router • S = L2 Switch • LB = Load Balancer • A = Rack of 20 servers • with Top of Rack switch … S S S S … … A A A A A A

33. Data Center Design Requirements • Data centers typically run two types of applications • outward facing (e.g., serving web pages to users) • internal computations (e.g., MapReduce for web indexing) • Workloads often unpredictable: • Multiple services run concurrently within a DC • Demand for new services may spike unexpected • Spike of demands for new services mean success! • But this is when success spells trouble (if not prepared)! • Failures of servers are the norm • Recall that GFS, MapReduce, etc., resort to dynamic re-assignment of chunkservers, jobs/tasks (worker servers) to deal with failures; data is often replicated across racks, … • “Traffic matrix” between servers are constantly changing

34. Data Center Costs • Data centers typically run two types of applications • outward facing (e.g., serving web pages to users) • internal computations (e.g., MapReduce for web indexing) • Workloads often unpredictable: • Multiple services run concurrently within a DC • Demand for new services may spike unexpected • Spike of demands for new services mean success! • But this is when success spells trouble (if not prepared)! • Failures of servers are the norm • Recall that GFS, MapReduce, etc., resort to dynamic re-assignment of chunkservers, jobs/tasks (worker servers) to deal with failures; data is often replicated across racks, … • “Traffic matrix” between servers are constantly changing

35. Data Center Costs • Total cost varies • upwards of $1/4 B for mega data center • server costs dominate • network costs significant • Long provisioning timescales: • new servers purchased quarterly at best • *3 yr amortization for servers, 15 yr for infrastructure; 5% cost of money Source: the Cost of a Cloud: Research Problems in Data Center Networks. SigcommCCR 2009. Greenberg, Hamilton, Maltz, Patel.

36. Overall Data Center Design Goal Agility – Any service, Any Server • Turn the servers into a single large fungible pool • Let services “breathe” : dynamically expand and contract their footprint as needed • We already see how this is done in terms of Google’s GFS, BigTable, MapReduce • Benefits • Increase service developer productivity • Lower cost • Achieve high performance and reliability These are the three motivators for most data center infrastructure projects!

37. Achieving Agility … • Workload Management • means for rapidly installing a service’s code on a server • dynamical cluster scheduling and server assignment  • E.g., MapReduce, Bigtable, … • virtual machines, disk images  • Storage Management • means for a server to access persistent data • distributed file systems (e.g., GFS)  • Network Management • Means for communicating with other servers, regardless of where they are in the data center • Achieve high performance and reliability

38. Networking Objectives • Uniform high capacity • Capacity between servers limited only by their NICs • No need to consider topology when adding servers => In other words, high capacity between two any servers no matter which racks they are located ! • Performance isolation • Traffic of one service should be unaffected by others • Ease of management: “Plug-&-Play”(layer-2 semantics) • Flat addressing, so any server can have any IP address • Server configuration is the same as in a LAN • Legacy applications depending on broadcast must work

39. Is Today’s DC Architecture Adequate? Internet • Hierarchical network; 1+1 redundancy • Equipment higher in the hierarchy handles more traffic • more expensive, more efforts made at availability  scale-up design • Servers connect via 1 Gbps UTP to Top-of-Rack switches • Other links are mix of 1G, 10G; fiber, copper Internet CR CR Data Center Layer 3 … AR AR AR AR • Uniform high capacity? • Performance isolation? • typically via VLANs • Agility in terms of dynamically adding or shrinking servers? • Agility in terms of adapting to failures, and to traffic dynamics? • Ease of management? Layer 2 LB LB • Key: • CR = L3 Core Router • AR = L3 Access Router • S = L2 Switch • LB = Load Balancer • A = Top of Rack switch S S … S S S S … … A A A A A A

40. Case Studies • A Scalable, Commodity Data Center Network Architecture • a new Fat-tree “inter-connection” structure (topology) to increases “bi-section” bandwidth • needs “new” addressing, forwarding/routing • VL2: A Scalable and Flexible Data Center Network • consolidate layer-2/layer-3 into a “virtual layer 2” • separating “naming” and “addressing”, also deal with dynamic load-balancing issues Other Approaches: • PortLand: A Scalable Fault-Tolerant Layer 2 Data Center Network Fabric • BCube: A High-Performance, Server-centric Network Architecture for Modular Data Centers

41. A Scalable, Commodity Data Center Network Architecture • Main Goal: addressing the limitations of today’s data center network architecture • single point of failure • oversubscription of links higher up in the topology • trade-offs between cost and providing • Key Design Considerations/Goals • Allows host communication at line speed • no matter where they are located! • Backwards compatible with existing infrastructure • no changes in application & support of layer 2 (Ethernet) • Cost effective • cheap infrastructure • and low power consumption & heat emission

42. Fat-Tree Based DC Architecture • Inter-connect racks (of servers) using a fat-tree topology • Fat-Tree: a special type of Clos Networks (after C. Clos) K-ary fat tree: three-layer topology (edge, aggregation and core) • each pod consists of (k/2)2 servers & 2 layers of k/2 k-port switches • each edge switch connects to k/2 servers & k/2 aggr. switches • each aggr. switch connects to k/2 edge & k/2 core switches • (k/2)2 core switches: each connects to k pods Fat-tree with K=2

43. Fat-Tree Based Topology … • Why Fat-Tree? • Fat tree has identical bandwidth at any bisections • Each layer has the same aggregated bandwidth • Can be built using cheap devices with uniform capacity • Each port supports same speed as end host • All devices can transmit at line speed if packets are distributed uniform along available paths • Great scalability Fat tree network with K = 3 supporting 54 hosts

44. Cost of Maintaining Switches

45. Fat-tree Topology is Great, But … Does using fat-tree topology to inter-connect racks of servers in itself sufficient? • What routing protocols should we run on these switches? • Layer 2 switch algorithm: data plane flooding! • Layer 3 IP routing: • shortest path IP routing will typically use only one path despite the path diversity in the topology • if using equal-cost multi-path routing at each switch independently and blindly, packet re-ordering may occur; further load may not necessarily be well-balanced • Aside: control plane flooding!

46. FAT-Tree Modified • Enforce a special (IP) addressing scheme in DC • unused.PodNumber.switchnumber.Endhost • Allows host attached to same switch to route only through switch • Allows inter-pod traffic to stay within pod • Use two level look-ups to distribute traffic and maintain packet ordering • First level is prefix lookup • used to route down the topology to servers • Second level is a suffix lookup • used to route up towards core • maintain packet ordering by using same ports for same server

47. More on Fat-Tree DC Architecture Diffusion Optimizations • Flow classification • Eliminates local congestion • Assign to traffic to ports on a per-flow basis instead of a per-host basis • Flow scheduling • Eliminates global congestion • Prevent long lived flows from sharing the same links • Assign long lived flows to different links What are potential drawbacks of this architecture?