# Efficient Traffic Engineering Strategies for Improving the Performance of TCP Friendly Rate Control Protocol

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## Abstract

**:**

## 1. Introduction

## 2. Literature Survey

## 3. Proposed System

#### 3.1. Enhanced RTT & RTO Calculations in TFRC

- RTO
_{new}= New RTO value; RTO_{old}= Previous RTO value.

#### 3.2. Enhanced Average Loss Interval Methods for TFRC

#### 3.2.1. ALI with Confidence Interval

_{i}}

_{i=1, 2, 3…}be the sequence of loss intervals observed by the receiver. S

_{k}is the kth loss event rate with number of events represented by n, the loss event rate can be calculated as 1/${S}_{1,n}^{^}$, Where ${S}_{1,n}^{^}$ can be calculated as weighted average of the last n intervals as follows.

_{i}is the weight for each loss history.

_{i=1, 2, 3...}as the most recent k sequences of loss intervals derived from set {S

_{i}}

_{i=1, 2, 3…}and are updated on the basis of most recent sample. The minimum and maximum loss intervals are obtained using the strategy called confidence interval.

_{i=1, 2, 3…}

_{i(min)}is the lower bound of the sequence {${S}_{i}^{~}$}

_{i=1, 2, 3…}and $\tilde{s}$

_{j(max)}is the upper bound of the sequence {${S}_{i}^{~}$}

_{i=1, 2, 3…}

- $\overline{X}$ is the mean of most recent k sequences from the set {${S}_{i}^{~}$}
_{i=1, 2, 3…}. - t is the desired confidence coefficient value (1.96 is the default value for 95% confidence interval-t-distribution table), n is the sample size.

_{i=1, 2, 3…}with $\tilde{s}$

_{i(min)}and $\tilde{s}$

_{j(max)}respectively. The new average loss interval $\tilde{S}$

_{(1,n)}can be estimated from {${S}_{i}^{~}$}. Using the basic ALI method the weighted average of the last n intervals $\tilde{S}$

_{(1,n)}and loss event rate can be calculated as 1/$\tilde{S}$

_{(1,n)}.

_{i}

_{(min)}and $\tilde{s}$

_{j}

_{(max)}are weights of min and max of k most recent sequences of {${S}_{i}^{~}$}

_{i=1, 2, 3...}By replacing the min and max intervals from the sequence {${S}_{i}^{~}$}

_{i=1, 2, 3}with end points of confidence interval in the sequence {${S}_{i}^{~}$}

_{i=1, 2, 3…}, the ALI method can produce more accurate and stable estimation of loss event rate than earlier even in network fluctuations. Alternatively, we can include $\tilde{s}$

_{i}

_{(min)}and $\tilde{s}$

_{j}

_{(max)}to the k most recent sequence {${S}_{i}^{~}$}

_{i}

_{=1, 2, 3…}to estimate loss event rate with high probability. Using the basic ALI method

#### 3.2.2. ALI with Mean

_{i}

_{=1, 2, 3…}be the sequence of loss intervals observed by the receiver. Generate a sequence {${\tilde{S}}_{i}$}

_{i}

_{=1, 2, 3...}as the most recent k sequences of loss intervals derived from set {S

_{i}}

_{i=1, 2, 3…}and are updated on the basis of most recent sample. Let us denote S

_{min}and S

_{max}are the minimum and maximum values of the sequence {${S}_{i}^{~}$}

_{i}

_{=1, 2, 3…}Find out the mean value of all loss intervals from the sequence {${S}_{i}^{~}$}

_{i}

_{=1, 2, 3…}and is denoted by S

_{mean}. Replace the minimum S

_{min}and maximum S

_{max}values from the sequence {${S}_{i}^{~}$}

_{i}

_{=1, 2, 3…}with S

_{mean}individually. Now the sequence {${\tilde{S}}_{i}$}

_{i}

_{=1, 2, 3...}contains k most recent samples with two new values. These result to estimate average loss intervals more accurately with fewer samples. The corresponding weights for S

_{mean}are weights of minimum S

_{min}and maximum S

_{max}of k most recent sequences of {${S}_{i}^{~}$}

_{i}

_{=1, 2, 3…}

_{(1,n)}and loss event rate can be calculated as 1/$\tilde{S}$

_{(1,n)}. Mathematically represented as

_{min}= minimum {${S}_{i}^{~}$}

_{i}

_{=1, 2, 3…}S

_{max}= maximum {${S}_{i}^{~}$}

_{i}

_{=1, 2, 3…}

_{mean}into the sequence {${S}_{i}^{~}$}

_{i}

_{=1, 2, 3…}along with S

_{min}and S

_{max}, then weighted average of the last n intervals $\tilde{S}$

_{(1,n)}can be calculated as

#### 3.3. Reduce Packet Loss with TTL

- TTL
_{min}= static specified minimum value; TTL_{max}= static specified maximum value - TTL
_{rt}= Recent reflect changes; - TTL
_{est(i)}, TTL_{est(i−1)}are the two most recent observations.

## 4. Simulation Environment

## 5. Results and Analysis

- Throughput: Throughput measures how fast data can be sent across the network.
- Packet Loss Rate: It can be measured as the percentage of number of packets dropped or lost by the number of packets sent over the link.
- End-to-End delay: It represents the responsiveness of the network. It can be measured as average over all surviving data packets from the sources to destinations.
- Packet Delivery Ratio: It is the ratio between the data packets delivered by the destination to number of packets generated by the sources.
- Jitter: It represents the variation of the packet arrival time. It can be calculated as the difference between the delay of the current packet and previous packet. The value of jitter should be less for streaming applications like audio or video applications.

## 6. Conclusions

## Author Contributions

## Conflicts of Interest

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**MDPI and ACS Style**

Reddy, N.R.; Reddy, P.C.; Padmavathamma, M.
Efficient Traffic Engineering Strategies for Improving the Performance of TCP Friendly Rate Control Protocol. *Future Internet* **2017**, *9*, 74.
https://doi.org/10.3390/fi9040074

**AMA Style**

Reddy NR, Reddy PC, Padmavathamma M.
Efficient Traffic Engineering Strategies for Improving the Performance of TCP Friendly Rate Control Protocol. *Future Internet*. 2017; 9(4):74.
https://doi.org/10.3390/fi9040074

**Chicago/Turabian Style**

Reddy, Nalavala Ramanjaneya, Pakanati Chenna Reddy, and Mokkala Padmavathamma.
2017. "Efficient Traffic Engineering Strategies for Improving the Performance of TCP Friendly Rate Control Protocol" *Future Internet* 9, no. 4: 74.
https://doi.org/10.3390/fi9040074