Frenetic: Programming Software Defined Networks

Jennifer Rexford

Princeton University

http://www.frenetic-lang.org/

Joint with Nate Foster, David Walker, Rob Harrison, Chris Monsanto, Cole Schlesinger, Mike Freedman, Mark Reitblatt, Joshua Reich

Traditional Networks

2

Data Plane (hardware)Forwards, filters, buffers, tags,rate-limits; collects statistics

Control Plane (software)Tracks topology; computesroutes; modifies data plane

Management PlaneMonitors traffic,

configures policy

Software Defined Networking (SDN)

3

API to the data plane(e.g., OpenFlow)

Logically-centralized control

Switches

Smart,slow

Dumb,fast

Momentum

• Everyone has signed on– Google, Facebook,

Microsoft, Yahoo, Verizon, Deutsche Telekom

• New applications– Host mobility– Server load balancing– Network virtualization– Dynamic access control– Energy-efficiency

• Real deployments

Programming OpenFlow Networks

5

Images by Billy Perkins

• The Good– Simple data plane abstraction– Logically-centralized architecture– Direct control over switch policies

• The Bad– Low-level programming interface– Functionality tied to hardware– Explicit resource control

• The Ugly– Non-modular, non-compositional– Programmer faced with challenging

distributed programming problem

Language-Based Abstractions

• Benefits– Modularity– Portability– Efficiency– Assurance– Simplicity

Simple, high-level abstractions are crucial for achieving the vision of software-defined networking.

OpenFlow Networks

7

Data-Plane: Simple Packet Handling

• Simple packet-handling rules– Pattern: match packet header bits– Actions: drop, forward, modify, send to controller – Priority: disambiguate overlapping patterns– Counters: #bytes and #packets

8

1. src=1.2.*.*, dest=3.4.5.* drop 2. src = *.*.*.*, dest=3.4.*.* forward(2)3. src=10.1.2.3, dest=*.*.*.* send to controller

1. src=1.2.*.*, dest=3.4.5.* drop 2. src = *.*.*.*, dest=3.4.*.* forward(2)3. src=10.1.2.3, dest=*.*.*.* send to controller

Controller: Programmability

9

Network OS

Application

Events from switchesTopology changes,

Traffic statistics,Arriving packets

Commands to switches(Un)install rules,Query statistics,

Send packets

E.g.: Server Load Balancing• Pre-install load-balancing policy

• Split traffic based on source IP

src=0*

src=1*

Seamless Mobility/Migration

• See host sending traffic at new location

• Modify rules to reroute the traffic

11

Programming Abstractions for Software Defined Networks

12

Three Main Abstractions

13

Reading state

OpenFlowSwitches

Writingpolicies

Composing modules

Reading State: Multiple Rules

• Traffic counters– Switch counts bytes and packets matching a rule– Controller application polls the counters

• Multiple rules– E.g., Web server traffic except for source 1.2.3.4

• Solution: predicates– E.g., (srcip != 1.2.3.4) && (srcport == 80)– Run-time system translates into switch patterns

14

1. srcip = 1.2.3.4, srcport = 802. srcport = 80

Reading State: Unfolding Rules

• Limited number of rules– Switches have limited space for rules– Cannot install all possible patterns

• Must add new rules as traffic arrives– E.g., histogram of traffic by IP address– … packet arrives from source 5.6.7.8

• Solution: dynamic unfolding– Programmer specifies GroupBy(srcip)– Run-time system dynamically adds rules

15

1. srcip = 1.2.3.41. srcip = 1.2.3.42. srcip = 5.6.7.8

Reading: Extra Unexpected Events

• Common programming idiom–First packet goes to the controller–Controller application installs rules

16

packets

Reading: Extra Unexpected Events

• More packets arrive before rules installed?–Multiple packets reach the controller

17

packets

Reading: Extra Unexpected Events

• Solution: suppress extra events–Programmer specifies “Limit(1)”–Run-time system hides the extra events

18

packets

not seen byapplication

Frenetic SQL-Like Query Language

• Get what you ask for– Nothing more– Nothing less

• SQL-like query language– Familiar abstraction– Returns a stream– Intuitive cost model

• Minimize controller overhead– Filter using high-level patterns– Limit the # of values returned – Aggregate by #/size of packets

19

Select(bytes) *Where(in:2 & srcport:80) *GroupBy([dstmac]) *Every(60)

Select(packets) *GroupBy([srcmac]) *SplitWhen([inport]) *

Limit(1)

Learning Host Location

Traffic Monitoring

Composition: Multiple Modules

• Networks have multiple policies–Routing–Traffic monitoring–Access control

• Challenges–Common set of rules in the switches–Processing the same packets

• OpenFlow API is not modular–Programmer must combine the logic

20

Composition: Simple Repeater

def switch_join(switch): # Repeat Port 1 to Port 2 p1 = {in:1} a1 = [out:2] install(switch, p1, DEFAULT, a1) # Repeat Port 2 to Port 1 p2 = {in:2} a2 = [out:1] install(switch, p2, DEFAULT, a2)

def switch_join(switch): # Repeat Port 1 to Port 2 p1 = {in:1} a1 = [out:2] install(switch, p1, DEFAULT, a1) # Repeat Port 2 to Port 1 p2 = {in:2} a2 = [out:1] install(switch, p2, DEFAULT, a2)

Simple Repeater

1 2

Controller

When a switch joins the network, install two forwarding rules.

Composition: Web Traffic Monitor

22

def switch_join(switch)): # Web traffic from Internet p = {in:2, srcport:80} install(switch, p, DEFAULT, []) query_stats(switch, p) def stats_in(switch, p, bytes, …) print bytes sleep(30) query_stats(switch, p)

def switch_join(switch)): # Web traffic from Internet p = {in:2, srcport:80} install(switch, p, DEFAULT, []) query_stats(switch, p) def stats_in(switch, p, bytes, …) print bytes sleep(30) query_stats(switch, p)

Monitor “port 80” traffic

1 2

Web traffic

When a switch joins the network, install one monitoring rule.

Composition: Repeater + Monitor

def switch_join(switch): pat1 = {in:1} pat2 = {in:2} pat2web = {inport:2, srcport:80} install(switch, pat1, DEFAULT, None, [out:2]) install(switch, pat2web, HIGH, None, [out:1]) install(switch, pat2, DEFAULT, None, [out:1]) query_stats(switch, pat2web)

def stats_in(switch, xid, pattern, packets, bytes): print bytes sleep(30) query_stats(switch, pattern)

def switch_join(switch): pat1 = {in:1} pat2 = {in:2} pat2web = {inport:2, srcport:80} install(switch, pat1, DEFAULT, None, [out:2]) install(switch, pat2web, HIGH, None, [out:1]) install(switch, pat2, DEFAULT, None, [out:1]) query_stats(switch, pat2web)

def stats_in(switch, xid, pattern, packets, bytes): print bytes sleep(30) query_stats(switch, pattern)

Repeater + Monitor

Must think about both tasks at the same time.

Composition: Frenetic is Modular

24

# Static repeating between ports 1 and 2def repeater(): rules=[Rule(in:1, [out:2]), Rule(in:2, [out:1])] register(rules)

# Static repeating between ports 1 and 2def repeater(): rules=[Rule(in:1, [out:2]), Rule(in:2, [out:1])] register(rules)

# Monitoring Web trafficdef web_monitor(): q = (Select(bytes) * Where(in:2 & srcport:80) * Every(30)) q >> Print()

# Monitoring Web trafficdef web_monitor(): q = (Select(bytes) * Where(in:2 & srcport:80) * Every(30)) q >> Print()

# Composition of two separate modulesdef main(): repeater() web_monitor()

# Composition of two separate modulesdef main(): repeater() web_monitor()

Repeater

Monitor

Repeater + Monitor

Composition: Reactive Run-Time

• Microflow-based– Send first packet to

the controller– Install rule if possible

• Check all policies– Accumulate actions to

perform on packet

• Check all queries– If no matches: install a

rule to handle remaining packets of the flow

25

Composition: Proactive [POPL’12]

• Proactive, wildcard rules– Keep packets in the “fast path”

• “Cross-product” of predicates

• Translate predicates into rules– Convert each predicate to one or more rules– Minimize to produce a smaller set of rules

• Reactive specialization– Dynamically expanding the policy as packets arrive 26

in:1in:2*

in:2 & srcport=80*

X

in:1in:2 & srcport=80in:2*

=

Writing Policy: Avoiding Disruption

Writing Policy: Avoiding Disruption

Reasons• Routine maintenance• Unexpected failure• Traffic engineering• Fine-grained security

Invariants• No forwarding loops• No black holes• Access control• Traffic waypointing

Writing Policy: Traffic Engineering

• Shortest-path routing–Controller computes shortest paths–… based on preconfigured link weights

29

1

1

3

1

1

Writing Policy: Traffic Engineering

• Transient loop–Update top switch to forward down–… while bottom switch still forwards up

30

1 5

1

3

1

1

Writing Policy: Path for a New Flow

• Rules along a path installed out of order?–Packets reach a switch before the rules do

31Must think about all possible packet and event orderings.

packets

Writing Policy: Update Semantics

• Per-packet consistency– Every packet is processed by– … policy P1 or policy P2, – … but not a mixture of the two– E.g., access control, no loops

or blackholes during routing change

• Per-flow consistency– Sets of related packets are processed by– … policy P1 or policy P2,– … but not a mixture of the two– E.g., server load balancing, in-order delivery, …

P1

P2

Writing Policy: Policy Update

• Simple abstraction– Update the entire configuration at once– E.g., per_packet_update(P2)

• Cheap verification– If P1 and P2 satisfy an invariant– Then the invariant always holds

• Run-time system handles the rest– Constructing schedule of low-level updates– Applying optimizations to limit the number of rules– Using only OpenFlow commands!

33

P1

P2

Writing Policy: Two-Phase Update

• Version numbers– Stamp packet with a version number (e.g., VLAN tag)

• Unobservable updates– Add rules for P2 in the interior– … matching on version # P2

• One-touch updates– Add rules to stamp packets

with version # P2 at the edge

• Remove old rules– Wait for some time, then

remove all version # P1 rules34

Writing Policy: Optimizations

• Avoid two-phase commit– Naïve version touches every switch– Doubles rule space requirements

• Limit scope of two-phase commit– Affects only a portion of the traffic– Affects only a portion of the topology

• Simple policy changes– Extension: strictly adds paths– Retraction: strictly removes paths

• Run-time system applies optimizations35

Frenetic Abstractions

36

SQL-likequeries

OpenFlowSwitches

ConsistentUpdates

Policy Composition

Ongoing Work

• Network virtualization– Applications see abstract topology– E.g., one big switch

37

Ongoing Work

• Network virtualization– Applications see abstract topology– E.g., one big switch

• Joint host-network management– Measurement and control– … through local host agent

38

Ongoing Work

• Network virtualization– Applications see abstract topology– E.g., one big switch

• Joint host-network management– Measurement and control– … through local host agent

• Policy transformation– Spread rules over many switches– E.g., distributed firewall/load-balancer

39

Related Work

• Programming languages– FRP: Yampa, FrTime, Flask, Nettle– Streaming: StreamIt, CQL, Esterel, Brooklet, GigaScope– Network protocols: NDLog

• OpenFlow– Language: FML, SNAC, Resonance– Controllers: ONIX, Nettle, FlowVisor, RouteFlow– Testing: MiniNet, NICE, FlowChecker, OF-Rewind,

OFLOPS

• OpenFlow standardization– http://www.openflow.org/– https://www.opennetworking.org/ 40

Conclusion

• SDN is exciting–Enables innovation–Simplifies management–Rethinks networking

• SDN is happening–Practice: useful APIs and good industry traction–Principles: start of higher-level abstractions

• Great research opportunity–Practical impact on future networks–Placing networking on a strong foundation 41

Concern

Assembly Languages

Programming Languages

x86 NOX Java/ML Frenetic

Resource Management

Move values to/from register

Declare/use variables

ModularityUnregulated

calling conventions

Calling conventions

managed automatically

ConsistencyInconsistent

memory model

Consistent (?) memory model

PortabilityHardware dependent

Hardware independent

Concern

Assembly Languages

Programming Languages

x86 NOX Java Frenetic

Resource Management

Move values to/from register

(Un)Install policy

rule-by-rule

Declare/use variables

Declare network policy

ModularityUnregulated

calling conventions

Unregulated use of

network flow space

Calling conventions

managed automatically

Flow space managed

automatically

ConsistencyInconsistent

memory model

Inconsistentglobal

policies

Consistent (?) memory model

Consistent global policies

PortabilityHardware dependent

Hardware dependent

Hardware independent

Hardware Independent

Thanks to My Frenetic Collaborators

44

Nate Foster Dave Walker Chris Monsanto Mark Reitblatt

Mike FreedmanRob Harrison Alec Story Josh Reich