Towards Resilient Networks using Programmable Networking Technologies Linlin Xie, Paul Smith, Mark Banfield, Helmut Leopold, James Sterbenz and David Hutchison

Towards Resilient Networks using Programmable Networking

TechnologiesLinlin Xie, Paul Smith, Mark Banfield, Helmut Leopold,

James Sterbenz and David Hutchison

Computing DepartmentLancaster University

Department of Electrical Engineering and

Computer ScienceUniversity of Kansas

Telekom Austria AG

Linlin Xie IWAN 2005 2

Presentation Outline

• Introduction to resilience networking– Motivation– Resilient networks– Aims– Approaches

• Scenario– Flash Crowd Event to Web Servers– Ill-Effect Detection– Remediation


Motivation

• The Internet is a utility– Consumers, businesses, governments

• Failures & attacks are inevitable– Hurricane Katrina, 9/11, NE blackout… – Link/device failures, DDoS…

• Current Internet and applications not resilient• Need networking effort: network providers

should take the responsibility– to protect network resources and optimize the

utilization– to protect cross traffic as well as stricken customers


Resilient Networks

• Ability of network to maintain or recover an acceptable level of service in the face of challenges to normal operation in an acceptable period of time

• Example challenges to normal operation:– Unusual traffic load (e.g. flash crowds) – High-mobility of nodes and sub-networks – Weak and episodic connectivity of wireless channels – Long delay paths – Large-scale natural disasters – Attacks against the network hardware, software, protocol

infrastructure – Natural faults of network components


General Resilience Aims

• Provide acceptable services to applications– Ensure information is accessible– Maintain end-to-end communication when possible– Operation of distributed processing and networked

storage

• Resilient services must remain accessible– Can degrade gracefully when necessary, but

ensure correctness– Recover rapidly and automatically when challenges

dissipate


Role of Programmable Networks

• Challenges to normal operation will rapidly change over time and space

• Prescribed solutions cannot be deployed• Therefore, resilient networks must:

– Operate in real-time– Be autonomic– Be context-aware and “intelligent”– Be dynamically extensible

• Programmable networking technologies are key to enabling these facilities


Programmable Networking Facilities

• Dynamic extensibility and self-organisation– Programmability allows dynamic response to challenges by

altering its behaviour– But need to be controlled in order to avoid misuse and potential

harm (e.g., stealthy interfaces)– Service to determine suitable locations to deploy services is

required

• Traffic and network environment awareness– Packet inspection at line speed– Network information collection

• Cross layer awareness and interaction– Avoid waste of resources and enhance coordination– How and the possible consequences need further study


Related Work

• Knowledge Plane (“KP”, David Clark et al. MIT)– Part of the KP purpose is to detects faults &intrusion and

mitigate the ill-effects– It proposes to add a new plane into the Internet architecture– The supporting technology is cognitive AI– The purpose of KP covers a very broad range– Cognitive AI is still in its initial stage of development– No concrete mechanisms for resilience maintenance yet

• Autonomic Communications– Efforts largely focused on self-configuring, self-managing, and

self-healing networked server systems– Initiatives now on making communications system autonomic

• Learn network context and automatically adapt


Related Work (Cont’d)

• COPS (Checking, Observing and Protecting Services) (Randy Katz, UCB)– Propose to protect network using iBoxes on the network edge– Propose an annotation layer between IP and transport layers to

carry information along the traffic

• Other similar/related efforts– Disruption Tolerant Network (DTN)

• Mean to provide stable end to end paths for applications when network connectivity faces challenges

– Survivability• Enable the system to fulfil its mission even in the presence of

attacks or failures (CMU)

– Resilience covers a broader range including protection against unusual traffic load (e.g., FC)


Resilience Networking Scenario

• Demonstrate the applicability of programmable networks

• Flash Crowd Event– Although flash crowd requests are legitimate, the

damage caused is equally as bad as malicious attacks

• Two activities investigated:– Detecting ill-effects of a flash crowd on Web servers– Remediation of a flash crowd event


Network Model

We take the role of network provider, i.e. ISP, to detect and mitigate the ill-effects occurred to the web servers network (which subscribes such service), and protect resources and cross traffic in the network of its own

ISP Core Network

E1

E2

E3

E4

R1

R2R3

R4

Web Servers Network

ISP

LAN Network

Dial Network

LAN Network

Core Router

ISP Network

Edge Router (Ingress, Egress,

Border)

Ei

Ri


Ill-Effects Detection

• Detection basis: – An increase of request rate in an association with a

decrease or level-off of response rate

• Detection location: – The edge router that connects the web server

network to the ISP network

• Algorithm overview: – compare actual observed response rate with the

expected one


Ill-Effect Detection (Cont’d)Mechanism based on the formulae:

Where the sizes of response objects are estimated according to the size distribution calculated from sampling the “content-length” domain in HTTP header of the response traffic


Simulation Setup

• Based on ns-2 • Topology• α chosen to 0.2• Detection interval t set

to be 30s

...

20 clients

Ingress Edge Router

Egress Edge Router

Web Server

LAN: 10Mb

/s

WAN: 50Mb/s

LAN: 15Mb/s


Simulation Setup (Cont’d)

Parameters set up as follows


Simulation Results

Flash crowd traffic simulationFlash crowd starts at 500s

We use access link congestion to simulate the server-side behavior


Simulation Results (Cont’d)

Detection results

Ratio of the actual response volume over the expected one


Simulation Results

Statistical distribution of ratio samples of background traffic:

N(1.10817, 0.2274772)

The 95% confidence range of this distribution is [0.662315, 1.554025]


Remediation• Drop excessive requests at the ingress edges of

the network– Pushback-similar mechanism

• Opportunistic multiple-routing of large response traffic that is packet-sequence-tolerant to protect cross traffic from degrading QoS too much– Multiple routes database– Path bandwidth information collection– Split the response traffic in proportion to the available

bandwidth of each path• Must consider the possibility of having zero or

just a few of programmable routers in the core network


Scenario Conclusions

• Contributions– Cross-layer coordination in detection– Cross traffic protection in the network

• Future work– Mitigation mechanism and experiments– Design and improve a resilient network

infrastructure and architecture


Conclusions

• Resilient networks are crucial for the future information society

• Programmable networking technology is appropriate for building resilient networks

• Example flash crowd scenario demonstrates the need for programmability, namely:– cross-layer interaction

– dynamic extensibility


Thanks!

Questions?

Documents

Towards Resilient Networks using Programmable Networking Technologies Linlin Xie, Paul Smith, Mark Banfield, Helmut Leopold, James Sterbenz and David Hutchison