TCP Performance :Permutation Vs Combination

1. TCP Performance :Permutation Vs Combination Sriram Lakshmanan Zhenyun Zhuang

2. Context • Use of wireless networks for internet access • Wired Networks themselves have considerably evolved from low bandwidth lines (about 56Kbps) to the Cable lines (several Mbps) • Ubiquitous computing and communication requirements of wireless networks • Thus future networks will have both wired and wireless links connecting a server with an end user • Further the wired parts themselves may have different delays and bandwidths

3. Problem Statement • In a network with heterogenous links,what impacts TCP Performance?Is it the combination of links that’s important or does the order of links also affect performance of TCP? • The most important metrics for performance we consider here are the throughput, delay and delay jitter.

4. Motivation • Consider a mobile user making a chat with a friend in another country. • He may connect through a WLAN, which further connects through Fiber to a larger server ,further, the packets may be carried over satellite to the other country, where it reaches the host (who connects through cable to an ISP) after passing through various gateways • Thus so many links with differing capacities, Delays are involved in this communication • While specific solutions have been suggested to tackle problems in each medium type, an overall look at what happens to a TCP connection which involves so many links, is not discussed to a significant extent in the literature • Thus the need to look at TCP performance in such a heterogeneous setting

5. Work Outline • Theoretical Analysis: Determine from a queuing theory perspective if the order can matter at all with tandem queues and if so when ? • Simulation: Determine the effects of ordering by simulation in NS2.

6. Queue 1, T1+Q1 Queue 2, T2+Q2 Theoretical Issues • Formulation as series of queues • Closed form solution known only when arrival is Poisson and service times exponentially distributed at the queues. • Analytical unsolvability of the problem for other distributions • Queue 1 can be modelled as M/M/1.But how do we model queue 2? • Even with Poisson arrivals (due to correlated nature of packet arrivals at the second queue) no closed form analytical solution for series of queues in general.

7. Related results • Order doesn’t matter with either completely deterministic or completely exponential servers at all queues, irrespective of arrival process. • However, with both deterministic and exponential queues together , order could matter. • Cases for other distributions not known well. • Approximate methods of analysis suggest that order could matter for series of queues. • “variability in service times” is the key factor to be considered. • Some heuristic principles are suggested such as arranging in the order of increasing service time variability based on approximate analysis and simulation. • Thus ,Queuing theory says that order could affect the average delay (waiting time) of a packet and some orders could be better to reduce the overall delay (and consequently throughput)

8. Reasoning for service time variability • Variability is characterized by the coefficient of variation(variance/Mean2) • Variability at the first queue propagates to the subsequent queues. • Large variability means that the actual service time can be many times greater or lesser than the mean. This happens when the service time distributions are bimodal or even multimodal. • When the service time is much greater than the mean, it causes queue buildup at that queue and idling at the other queue. For a finite size queue packet drop may occur at that queue and unnecessary idling at the second queue. This idle period is a period of resource wastage and cannot be recovered when a burst comes next. • When the service time of a packet is much less than the mean, it gets through the queue quickly and reaches the next queue. If this happens for successive packets then a burst of packets may leave one queue and reach the next where, it may find a limited size queue and get dropped. • Large variance increases the chance of a short job getting stuck behind a large job

9. D M M D Illustrative case • Deterministic queue has variability 0 and exponential queue has variability 1. • In the first case there are packets coming out at a constant rate and there is practically no idle period at the second queue although the output from that queue will not be smooth. • Second queue could be idle waiting for another packet arrival when the first queue is exponential because of large variability at the first queue. • Thus in this case its better to have the low variability (deterministic queue) first. These arguments may be extended to the case of multiple queues with different service time variability. Thus idle periods can be minimized with this arrangement. • Further if we assume finite queue lengths ,while the large packet is being serviced, smaller packets will buildup in the queue and could even be dropped. • It is the large service times at the exponential servers that matters.Also one has to note that the rate at the output of the exponential queue is typically higher than the input rate?(In the steady state service rate is higher than the arrival rate)But the deterministic queue outputs at a constant rate. • Thus in the second case the bunching of packets and the higher rate could cause increased packet drops at the second queue reducing throughput

10. Inferences • Queuing theory does not rule out the possibility of order affecting the average delay. In fact the ordering of queues in series could cause the throughput to vary by an order of magnitude. Direct analytical results are not available even for simple cases. • The first two moments of the service time distribution (particularly the coefficient of variation) are the most important parameters to consider • It is important to start looking at protocols which operate over more than one type of link and leverage the benefits of ordering • Establishes the counter-intuitive result that average throughput could vary significantly (even with infinite buffering and more so with finite buffering)

11. Network Setup • 4 links (WLAN, WWAN, DSL, Wired) • 4! = 24 • 4 TCP flows • 1000bytes, FTP • Background Traffic • UDP (125 bytes, CBR)

12. Observation 1:It is not combinations!

13. Observation 2:LAN-to-WAN is better • The TCP aggregate throughput of LAN-first network is always larger than WAN-first network • Simpler networks: • Two links: WLAN link and WWAN link • Name them: • LAN First Network (LAN link + WAN Link) • WAN First Network (WAN link + LAN link)

14. Explanations • Several factors contribute • Interactions of Bandwidth/Packet size/ Dropping policy • Link Loss rates • Link Delay Variance • Three Types of Network • Network with End-to-end Background Traffic • Network with Hop-by-hop Background Traffic • Network with partially ordered link properties

15. Network with End-to-end traffic • To increase the throughput differences between these two networks • Use Large TCP pkt. • Reduce the bandwidth difference. • Increase Background Traffic. • Use DropTail instead of RED.

16. Queue Lengths • LAN-first Network: • First queue has length close to 0. • Second queue has length close to limit. • WAN-first Network • Just the Opposite • First queue has length close to limit. • Second queue has length close to 0.

17. LAN First Network

18. WAN First Network

19. Reasons • For WAN First Network • The TCP pkt are competing with UDP pkt with no advantage! • For LAN First network • TCP pkt has advantage! • When TCP pkt are in the previous link, it will arrives at the destination after a period of time, which depends on the link bandwidth and pkt size • During that period time, the next queue is very likely reduced by the second link’s processing • LAN + WAN > WAN + LAN

20. Effect Link loss rate • Observation • Putting lossy links before less-lossy links has slightly better performance • Why? • If a packet anyway is going to be dropped along the path, the earlier it is dropped, the better. • Smaller queue length Smaller RTT Larger Throughput • WAN + LAN > LAN + WAN

21. Networks with hop-by-hop traffic • Delay Variance Effect • Putting smaller delay variance link before links with larger variances is better. • The delay is “enlarged” by the following links with the presence of cross traffic • LAN+WAN > WAN + LAN

22. Summary and Future work • Summary • TCP Performance depend on order of links with different characteristics. • Since TCP is self-adaptive protocol, the performance affected by the link properties is complex. • Future Work • Relate background traffic intensity to variability in service times • Include MAC delay which happens in a wireless link (not captured in a wired abstraction)