Multiple Chapters on "Evolution Over Time of Network Parameters"

Multiple Chapters 20 pages (6372 words) Sources: 20 Style: Harvard

[EXCERPT] . . . .

Evolution Over Time of Network Parameters

In this chapter, we present the definitions and background material on the topics covered in this thesis along with the relevant literature survey. In network environment, traffic analysis must be carried out in ongoing bases in order to account for surfacing applications, there effort on traffic, and their possible control. Some analyses are extensive, but most entities opt for monitoring and alarms alerting them when malicious applications have entered the network. Form General Interne traffic analysis, studies have shown that even though, many emerging application take advantage of non-connected oriented transfer mechanisms like the one offered by the User Datagram Protocol (UDP); the TCP is the dominant transmission protocol in terms of byte load. Thus, many studies concentrate on it and the applications transmitting over it.

User connectivity to computer networks and the Internet has increased dramatically in the past couple of decades. The appeals of being able to access information and share resources from geographically dispersed location have led to the proliferation of computer networks and the continuity increasing demands of network capacity. At their beginning, computer networks were composed of only a few general purpose nodes. Then they grew to bulletin boards, implementation into educational institutions, business community, organizations and individual users having multiple device networks at home (Developments in Networking Technologies). It is fair to say that networking has become an integral part of all computer systems. Any effort in the upgrade or expansion of computer systems must consider the network as well.
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Many organizations take the seat-of-the-pants approach in planning for their network systems and capacity upgrades.

During the past years the number of internet users grew extensively. In 2005, the percentage of Internet users has increased to 15.7 and by June 2008, 21.9% of the world's population had access to the Internet (Miniwatts Marketing Group, 2008). In the year 2008, the geographical regions with the largest population percentage of internet users were North America, Oceania/Australia, and Europe (Miniwatts Marketing Group, 2008).

To help in the expansion or upgrade planning, networks are often monitored and data are collected for analysis and projection. Due to the size of computer networks and overwhelming amount of data exchanged between them, monitoring, collecting and analyzing network flow data is an enormous task. Network traffic studies have attempted to model network traffic after distributions with constant arrival rates and have failed (Paxson and Floyd, 1995). Other studies have explored the characteristics of network traffic with respect to flows and their effect on network traffic (Kim, 2004).

TCP has been the dominant transport protocol in the Internet for decades. Consequently, the performance of the Internet is influenced by TCP significantly. The recent TCP standard, TCP Reno (46), is a marking/loss-based system. In this type of schemes, packet loss or packet marking in the shape f Explicit Congestion Notification (ECN) feedback offered by Active Queue Management in routers amongst a source and a destination are utilized as pointers of network congestion (59). TCP Reno necessarily does have a probing phase and a decreasing phase. The probing phase of standard TCP contains an exponential on the increase phase and of a linear increasing phase. Throughout the slow-start phase, the window size will be twice every Round Trip Time (RTT), i.e., augment the window size exponentially. The probing phase ends when overcrowding is practiced in the form of ECN, 3 replacement acknowledgements or a break. At the moment TCP Reno put into practice a multiplicative decrease behavior. The TCP Reno location of the congestion window throughout the congestion evasion phase is: on ACK welcome, cwnd, the existing window size is greater than before by; when ECN or loss happens, cwnd is bisected.

Joined with this preservative add to multiplicative decrease (AIMD) model in which marking utters cutting the window size in partially, TCP Reno undergoes from great oscillations in throughput. The preferred congestion window utilized by TCP-Reno is approximately equivalent to the bandwidth-delay produce of the association. For high bandwidth-delay product links, this preferred congestion window is fairly far above the ground high as 80,000 packets for a 10 Gbps link with 100 ms RTT! TCP-Reno's grouping of a sluggish linear boost and a speedy multiplicative decline needs an irrational quantity of time for this preferred window to be recouped following a failure. Certainly, as highlighted by Floyd, in likely circumstances it can acquire a TCP Reno stream in excess of one hour to recuperate from a single congestion occurrence. Furthermore, beneath the random packet loss model, TCP-Reno can necessitate irrationally low packet drop likelihood for these elevated bandwidth-delay-product links. Undeniably, Reno's consideration scales with the opposite square root of the loss likelihood. To set the elevated speed networks for which the bandwidth-delay product will carry on growing, TCP Reno will turn out to be a performance restricted access itself.

During the past few years, questions concerning the behavior of TCP in speedy and long distance networks have been comprehensively concentrated on in the networking investigation community, both for the reason that TCP is the most extensive transport protocol in the up-to-date Internet and as the bandwidth-delay product keeps on growing. The renowned of TCP in high bandwidth-delay item for consumption networks is that the TCP preservative boost probing device is too slow to adapt the sending rate to the accessible bandwidth.

2. Problems of the Existing Marking/loss-Based TCP Versions

To transcend the inherent limitations of the standard TCP Reno in high-speed network several marking/loss-based protocols such as High Speed TCP (HSTCP), STCP, BIC TCP and TCP Westwood + have been proposed. By more aggressively probing for available bandwidth, and by modifying the reaction to marking/loss feedback, these protocols are able to achieve much higher throughput that TCP Reno. However, these protocols are subject to a larger number of timeouts and re-transmissions that Reno, and suffer from intra-protocol Round-Trip-Time (RTT) unfairness when competing flows have varying RTTs (R.King, 2005). Moreover, all of these protocols suffer from the reverse path congestion problem in which the throughput in the forward (source to destination) direction degrades due to unrelated congestion occurring in the reverse path (ACK packet) direction.

Both HSTCP and STCP contribute to a parallel strength as regards to their move toward to fiddle with for TCP-Reno's deficiencies, and may be regarded as selected members of the similar group of high-speed loss-based protocols. Basically modification of the window update principles of TCP-Reno can in a straight line get better the protocol's capacity to employ high speed links, but might damage its evenhandedness properties. As pointed out by Harfoush (2004) in, protocols in the same class as HSTCP and STCP (protocols that make alterations to the enlarge and decrease parameters), can have unwanted RTT equality properties if they just boost more assertively when operating with larger windows. A MIMD protocol, for example STCP, uses Multiplicative Increase and Multiplicative Decrease window adjustment regulations. For each acknowledged packet, STCP augments its clogging window by 0.01 packets, and on a packet drop, the window is decreased to 0.875 times its present window. Consequently, the revival time from a drop is scale invariant, for all time necessitating a steady number of round trip times. MIMD is corresponding to a preservative augment scheme hoer the raise step size raises proportionally to the congestion window size. (L.Xu, K.Harfoush, and I.Rhee, 2004)

The TCP alternative HSTCP takes a parallel move toward to STCP, even if it scales its drop parameter from plummeting by 50% at squat window sizes to 10% at upper windows. HSTCP after that puts its augment parameter as essential to attain its preferred packet loss comeback The finish effect is that HSTCP's boost rate raises somewhat slower as compared to that of STCP, but tranquil very quickly than TCP-Reno. A more destructive TCP version guides approximately to condensed fairness, in case care is taken. Harfoush (2004) points out that both HSTCP and STCP have unwanted equality properties at what time flows with dissimilar round trip times are opposing over a communal link. HSTCP has somewhat enhanced RTT bias performance as compared to STCP.

BIC-TCP, consistent with its authors, has desirable RTT bias properties. BIC-TCP makes use of a binary boost scheme to rapidly approach a predictable secure window, and after that gradually increase above that window. However, as pointed out by (S.Mascolo, 2006), BIC-TC, as well as other high-speed loss-based protocols, exhibits an extraordinary window fluctuation behavior with being there present turn around traffic, which is a normal network operating condition. Moreover, the number of timeouts and retransmissions is very elevated contrast with TCP Reno, which means these protocols do not put into practice as efficiently as TCP Reno does.

To tackle the danger of congestion fall down, it is of importance to mention that a common feature in all of these loss-based protocols is that they add to their congestion windows by extra to Reno's quantity of 1 packet per RTT. Since of their more destructive behavior, the above stated speedy loss-based protocols persuade congestion proceedings at a greatly advanced frequency that those persuaded by… READ MORE

Quoted Instructions for "Evolution Over Time of Network Parameters" Assignment:

2 Dissertation Chapters :

1. Literature Review Chapter

2. Methodology Chapter

Evolution over time of network parameters:

Current bulk data transfers, from web browsing to peer-to-peer file exchanges, employ the use of TCP as a transmission protocol. Throughout the Internet history, TCP proved to be very robust, able to adapt the transmission pace to a wide variety of environments and conditions. While this adaptation is extremely successful for long downloads, as the protocol requires to infer the parameters of the network from the actual data packets exchanged, TCP is far less successful when dealing with short data transfers, because it starts each connection with a slow pace, in order to avoid network overloading.

In this context, this project will an***** the characteristics of data transfers in order to infer the evolution / stationarity in time of network parameters. The study aims to observe whether any predictable variations can be observed when observing the values for delay, loss, and throughput over a longer period. The method will consist of observing captured packets from real network traffic and use traffic analysis tools to extract the network parameters for each of the TCP connections observed. In order to guarantee consistent results, the above analysis will be doubled by repeated retrieval of the same objects from a list of websites.

The resulting dataset of network parameters will then be grouped using the IP addresses of the remote endpoint. The parameters will be studied using statistical methods to observe any short-term relationship between their values. If a correlation will be observed between these values, the analysis will also aim to infer the timeframe for which such values may represent a reasonably accurate prediction for successive connections.

The project will require substantial knowledge of the Linux environment. While all the tools for capturing and analysis do exist (and familiarity with programs such as tcpdump is expected), shell scripting knowledge would be an advantage when automating some of the processes. Subject to the implementation chosen for the statistical analysis, the project may also require working knowledge of Matlab in order to process the resulting data



Recommended papers from my supervisor:

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.79.1377&rep=rep1&type=pdf

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.8201

www.acm.org/sigcomm/sigcomm97/papers/p086.pdf

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