1. Controlling High Bandwidth Aggregates in the Network Ratul Mahajan, Steven M. Bellovin, Sally Floyd, John Ioannidis, Vern Paxson, and Scott Shenker AT&T Center for Internet Research at ICSI (ACIRI) and AT&T Labs Research Presented by Scott McLauren

2. Overview Introduction Overview of ACC Local ACC Pushback Simulations Discussion Related Work Conclusions

3. Introduction Overloads can result from a single flow not using congestion control. These flows continue to transmit, despite packet drops DoS – when a large amount of traffic is directed at a network link or server Flash crowd – A large number of users try to access a server. They can overload the server and network link, which interferes with unrelated traffic

4. Introduction ACC – Aggregate-based Congestion Control Aggregate – a collection of packets from one or more flows that have some property in common  Source or destination addresses, application type, TCP traffic, HTTP traffic to a specific server Local ACC and Pushback  Expected to be invoked rarely

5. Overview of ACC 1. Am I seriously congested? 2. If so, can I identify an aggregate responsible for an appreciable portion of the congestion? 3. If so, to what degree do I limit the aggregate? 4. Do I also use pushback? 5. When do I stop? When do I ask upstream routers to stop?

6. Policies Very large number of possible policies  Protect high bandwidth aggregates  Punishing some aggregate when congestion starts  Fairness  Restricting max throughput of an aggregate Policies are left as future work

7. Detecting congestion Apply ACC only when output queue has sustained severe congestion Monitor loss rate at the queue, and looking for an extended high loss rate period

8. Types of Congestion Undifferentiated congestion  Under-engineered network  Fiber cut Traffic clustering to form aggregates  Flash crowds, flooding attacks, application types (email worms) DDoS attacks – the attacker can vary the traffic to escape detection

9. Identifying Responsible Aggregates Congestion signature  The router does not need to make any assumptions about the malicious or benign nature of the aggregate Collateral damage  Signature is too broad – traffic beyond the aggregate is included in the signature

10. Determining the Rate Limit for Aggregates Rate limit is determined such that a minimum level of service is guaranteed for the remaining traffic Completely shutting off traffic is not used because of:  Flash crowds  An aggregate for a DDoS attack will also contain innocent traffic

11. Pushback Used to control an aggregate upstream Congested router asks (recursively) its neighbors to rate-limit the aggregate Can be invoked by a router, or a server connected to a router

12. Reviewing Rate-limiting Rate-limiting is updated periodically, to update the limit based on current conditions, and to release aggregates that start to behave Decisions are easy for local ACC, difficult with pushback An attacker could predict these decisions to evade ACC

13. Local ACC Triggered when the output queue experiences sustained high congestion Using the packet drop history of the last K seconds, the ACC agent tries to identify the high bandwidth aggregates, and the limit to which they should be restricted

14. Identification of High Bandwidth Aggregates Expectation is that most aggregates will be based on either a source or destination address prefix Detection based on destination address is presented, other algorithms require further research

15. Identification of High Bandwidth Aggregates From the drop history, extract a list of high-bandwidth addresses (32-bit) Cluster these into 24-bit prefixes  For each of these, try obtaining a longer prefix that still contains most of the drops

16. Determining the Rate Limit for Aggregates ACC agent sorts the list of aggregates based on the number of drops Uses the total arrival rate at the output queue and the drop history to estimate the arrival rate ACC agent calculate the excess arrival rate at the output queue  Traffic that would be dropped at the rate limiter to bring the drop rate down to the target drop rate Compute rate-limit L for each aggregate, such that:  Aggregate[k].arr is the arrival rate of the kth aggregate

17. Rate-limiter Controls the throughput of the aggregates, and estimates arrival rate using exponential averaging It is in the forwarding fast path, so it must be light-weight Once a packet is past the rate-limiter, packets lose their identity as part of an aggregate Implemented as a virtual queue

18. Narrowing the Congestion Signature Goal is to drop more of the attack traffic  Based on dominant signature within an aggregate  Drop more heavily from this subset Flow-aware rate-limiting during flash crowds  Drop more heavily from SYN packets, so connections that are established get better service  Dangerous in DDoS attacks, the attacker could just send the packets that are being favored (TCP above)

19. Simulations Aggregates 1-4 are composed of multiple CBR flows. Aggregate 5 is a VBR source whose sending rate increases at t=13, decreases at t=25

20. Invoking Pushback Invoked if the drop rate for an aggregate remains high for several seconds  The high drop rate indicates the router hasn’t been able to control the aggregate by preferential dropping (RED)

21. Sending Pushback Requests Upstream Each upstream link is classified as  Non-contributing – send a small fraction of aggregate’s traffic  Contributing – send a large fraction of aggregate’s traffic Non-contributing aggregates do not receive pushback requests, only limit those aggregates sending most of the traffic Algorithm used:  max-min Arrival rates of 2, 5, and 12 Mbps Desired arrival rate of 10 Mbps Limited to 2, 4, and 4 Mbps  Non-contributing neighbors could start sending more traffic, but it doesn’t matter because they are using rate-limiting Protocol defined in IETF draft, since deleted

22. Feedback to Downstream Routers Upstream routers send status messages to downstream routers  Report total arrival rate for that aggregate  Messages enable congested router to decide if it want to continue pushback Ending pushback may result in larger arrival rate  Because dropping is no longer contributing to congestion control Solid lines indicate arrival rate estimate in the status message Dashed lines did not receive pushback requests Labels indicate arrival rate estimate

23. Simulations Simple Intended to illustrate some of the basic functionality of the ACC mechanisms Bad sources – send attack traffic to victim D Poor sources – innocent sources sending traffic to D Good sources – send traffic to destinations other than D

24. Local ACC Good and Poor aggregates contain 7 infinite demand TCP connections Bad sources use a UDP flow with equal on-off sending times, randomly chosen between 0 and 4 seconds  1 MBps during on period

25. DDoS Attacks 10 good sources & 4 poor sources spawn web-like traffic Sparse-attack – 4 random 2 MBps on-off bad sources Diffuse-attack – 32 UDP 0.25 MBps on-off sources

27. Flash Crowds Flash traffic from 32 sources sending web traffic to the same destination Good traffic from ten other sources sending web traffic to other destinations  Accounts for 50% link utilization without flash

28. Pushback Discussion Advantages  Prevents scarce upstream bandwidth from being wasted on packets that will eventually be dropped  When traffic can be localized spatially, pushback can effectively concentrate rate-limiting on attack traffic within aggregate Disadvantages  For DDoS attacks uniformly distributed across inbound links, pushback is not effective at rate-limiting  May overcompensate, especially during flash crowds, dropping extra traffic resulting in link being underutilized  Can sometime increase damage done to legitimate traffic – when legitimate and attack sources are within the same aggregate and the sources are in a edge network without pushback

29. Pushback Implementation Identification of aggregates can be done as a background task, or on a separate machine, so processing power is not an issue Router needs to determine if a packet is part of an aggregate. If number of aggregates is large, router has a large lookup table. The lookup-time increases with the number of aggregates These should not be an issue, pushback will only be used occasionally, on a handful of aggregates

30. Pushback Deployment Estimating Upstream Contribution  Difficult for routers joined by LANs, VLANs, or frame relay circuit – multiple routers attached to interface  Downstream router my not be able to distinguish between upstream routers  Workaround – send dummy pushback request that doesn’t rate-limit, status messages with estimated arrival rate are returned, then actual pushback requests can be sent to the necessary routers. Deployment  Incrementally at the edges of an island of routers

31. Related Work Ingress Filtering  Attempts to stop the attacks, ACC doesn’t Traceback  Attempts to find the sources of the attacks, ACC doesn’t IDS  Protocol for interaction between routers  Does not deal with identification or rate-limiting CDNs and Multicast  Prevent flash crowds by mirroring data  What about traffic not yet cached? Traffic not suitable for multicast? Flow-based congestion control  Doesn’t handle aggregates of many flows that are low-bandwidth CBQ  Used for fixed definitions of aggregates, not dynamic aggregates

32. Conclusions Local and cooperative mechanisms for aggregate-based congestion control have potential to control DDoS attacks and flash crowds More research needs to be done  Need to understand pitfalls and limitations of ACC  How frequently is sustained congestion caused by aggregates, and not by failures?  What do attack traffic and topologies look like?  Policy decision will play a role in shaping ACC mechanisms

33. Questions