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 1 Slides by Prof. Zhi-Li Zhang, UMN Advanced Networking Course CSci5221
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. - PowerPoint PPT Presentation
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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
1
Slides by Prof. Zhi-Li Zhang, UMN Advanced Networking Course CSci5221
Even within a Single Administrative Domain
• 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
2
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
• 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
• …….
4
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 Ethernetsketch
5
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
6
Interaction w/ the Upper Layer (IP)• Bootstrapping end hosts by automating host configuration (e.g., IP
• 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
21
Data Centers: Key Challenges 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?
22
Case Studies• 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, …
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
Typical Cluster at Google
Cluster Scheduling MasterLock Service GFS Master
Machine 1
SchedulerSlave
GFSChunkserver
Linux
UserTask 1
Machine 2
SchedulerSlave
GFSChunkserver
Linux
UserTask
Machine 3
SchedulerSlave
GFSChunkserver
Linux
User Task 2
BigTableServer
BigTableServer
BigTable Master
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
Google File SystemKey Design Considerations• Component failures are the norm
– hardware component failures, software bugs, human errors, power supply issues, …
• 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
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, …
GFS Architecture: Illustration
Separation of control and data flows
29
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)
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
Data Center NetworkingMajor Theme: What are new networking issues posed by
large-scale data centers?• Network Architecture?• Topology design?• Addressing?• Routing?• Forwarding?
31CSci5221: Data Center Networking, and Large-Scale Enterprise Networks: Part I
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
33
InternetInternetCR CR
AR AR AR AR…
SSLB LB
Data CenterLayer 3
Internet
SS
A AA …
SS
A AA …
…
Layer 2
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
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
36
Amortized Cost*
Component Sub-Components
~45% Servers CPU, memory, disk
~25% Power infrastructure
UPS, cooling, power distribution
~15% Power draw Electrical utility costs
~15% Network Switches, links, transit*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. Sigcomm CCR 2009. Greenberg, Hamilton, Maltz, Patel.
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 1. 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 !
2. Performance isolation– Traffic of one service should be unaffected by others
3. 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?
40
InternetInternetCR CR
AR AR AR AR…
SSLB LB
Data CenterLayer 3
Internet
SS
A AA …
SS
A AA …
…
Layer 2 Key:• CR = L3 Core Router• AR = L3 Access Router• S = L2 Switch• LB = Load Balancer• A = Top of Rack switch
• 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?
• 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
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?