This paper proposed an Integrated Random Early Detection (IRED) method that aims to resolve the problems of the queue-based AQM and load-based AQM and gain the benefits of both using indicators from both types. The arrival factor (e.g., arrival rate, queue and capacity) and the departure factors are used to estimate the congestion through two integrated indicators. The utilized indicators are mathematically calculated and integrated to gain unified and coherent congestion indicators. Besides, IRED is built based on a new dropping calculation approach that fits the utilized congestion indicators while maintaining the intended buffer management criteria, avoiding global synchronization and enhancing the performance. The results showed that IRED, compared to RED, BLUE, ERED, FLRED, EnRED and DcRED, decreased packet delay and loss under various network status. Specifically, the results showed that in heavy and moderate traffic, the proposed IRED method outperformed the state-of-the-art methods in loss and delay by 18% and 10.6%, respectively.

The widespread of computer networks increased data communication massively in the last decades. Data is communicated as packets transmitted over various network resources (e.g., links, switches and routers) before reaching its destination [

Active Queue Management (AQM) methods manage the router buffer and maintain the queue at a managed length to avoid packet loss, decrease delay and avoid congestion [

Random Early Detection (RED) [

In this paper, a new Active Queue Management (AQM) method that integrates queue-status and load-status to manage queue in the router buffer is proposed. The contributions of this paper are as follows: 1) Resolve the queue-based AQM and load-based AQM, 2) Integrate the current and previous statues of the queue and the network through developed congestion indicators, 3) Develop a new AQM method for congestion indicator based on innovative

Although the AQM method works at each router individually and independently, it depends on, besides the status of the queue, the estimated status of the network as a whole. The calculated or estimated statuses are used to calculate the

As more AQM methods were developed to solve RED limitations, various congestion indicators have been used. Based on these indicators, the existing AQM methods can be classified into queue-based, load-based and hybrid-based, ass illustrated in

In the queue-based category, Suthaharan [

Long, Zhao [

LUBA [

Hybrid-based methods were proposed in the literature to solve the problems of the queue-based and load-based methods. Hybrid-based methods, such as REM [

In summary, RED depends on the

Method | Indicator(s) | AvoidSynch. | EaseParamet. | ReduceDelay | ReduceLoss | ReduceDropping | FairAllocat. |
---|---|---|---|---|---|---|---|

RED [ |
AVG | ✓ | ✗ | ✗ | ✓ | ✓ | ✗ |

FRED [ |
AVG | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ |

DSRED [ |
AVG | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ |

ARED [ |
AVG | ✓ | ✗ | ✓ | ✓ | ✗ | ✗ |

GRED [ |
AVG | ✓ | ✗ | ✓ | ✓ | ✗ | ✗ |

SRED [ |
Q | ✓ | ✗ | ✗ | ✓ | ✗ | ✓ |

ERED [ |
AVG & Q | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |

BLUE [ |
PL | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |

MRED [ |
PL | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |

AVQ [ |
Arrival Rate | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ |

SAVQ [ |
Arrival Rate | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ |

EAVQ [ |
Arrival Rate | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ |

LUBA [ |
Load Rate | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ |

REM [ |
Arrival Rate & Q | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ |

REAQM [ |
Arrival Rate & Q | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ |

SVB [ |
Arrival Rate & Q | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ |

RAQM [ |
Arrival Rate & Q | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ |

RaQ [ |
Arrival Rate & Q | ✓ | ✗ | ✗ | ✗ | ✓ | ✗ |

PI [ |
Load Rate & Q | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ |

Yellow [ |
Arrival Rate & Q | ✓ | ✗ | ✗ | ✗ | ✓ | ✗ |

FLRED [ |
AVG & Delay | ✓ | ✓ | ✓ | ✗ | ✗ | ✗ |

EnRED [ |
AVG & Q | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ |

DcRED [ |
Delay | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ |

Generally, existing AQM methods differ in the goal to be achieved and the utilized congestion indicators. In terms of similarities, AQM methods have successfully adapted the RED-like mechanism for using the indicators to calculate the dropping probability. However, a new challenge raised in the attempt to use multiple indicators under the RED-like mechanism. ERED [

The proposed AQM method, Integrated Random Early Detection (IRED), uses both queue-status and load-status to estimate congestion and manage the queue at the router buffer. Accordingly, two indicators are mathematically calculated and integrated to gain a singly and coherent congestion indicator at the router buffer. Besides, IRED proposed a new

The queue-status and load-status, used for congestion estimation in the router buffer, are estimated using two calculated factors: the integrated arrival factor and the integrated departure factor. These two factors differ from the commonly used arrival and departure rates in that both consider the status of the queue beside the rates.

The integrated arrival factor is calculated based on two parameters, the arrival rate and the queue length. The arrival rate is calculated as a weighted average of the current arrival value and the previous arrival value. The weight variable, w, specifies the share of each of these values, current and previous, towards the output value. Accordingly, with a value of weight above 0.5, the current rate takes more preferences over the old rates and vice versa. The queue length is calculated as the current queue combined with a pre-determined threshold value divided by the buffer’s total capacity. Accordingly, both parameters: arrival rate and queue length, are proportional to the integrated arrival value. Accordingly, the first factor, the integrated arrival factor, IA, is calculated as given in

where _{i} is the arrival rate at the time _{i-1} is the average integrated arrival rate before the time

The second factor, the integrated departure factor,

where, _{i-1} is the current departure rate, which is calculated at the time _{i-2} is the previous average integrated departure rate, _{n} occurs before the arrival, and the departure of slot

For the integrated arrival factor, the combination of both arrival rate and queue length affects the buffer as follows: If the arrival rate is high, while the queue length is small, the router is considered non-congested, and there is a high chance that the increase in the arrival rate is due to short false congestion. On the other hand, when the rate is high while the queue length is medium or high, either heavy congestion is approaching or considerable delay is facing. Different congestion status occurs with different arrival rates; each required a different decision about dropping vs accommodating the arrival packets, as given in

Similarly, different congestion status occurs with different departure rates; each required a different decision about dropping vs accommodating the arrival packets, as given in

According to the congestion estimation in

Three equations for

According to

The parameter, _{max} and t_{min} vales are determined empirically, and the relation “0 < t_{min} < t_{max} < 1” should always be true.

As an AQM, the proposed IRED is implemented by deciding about packet accommodation and dropping as each packet arrives at the buffer. This process required calculations of the congestion indicator and investigation of the buffer status, as discussed earlier. The complete process for IRED is implemented as given in

1. |
_{max}, _{min}, _{min}_{min} _{max} _{max}_{min}_{min} _{max} _{min} _{max}_{max}_{max}_{min} _{max} _{min}_{max}_{min} _{max} _{max} |

As given in Algorithm 1, there are four scenarios for

The proposed IRED is simulated according to the discrete-time queue model, which is commonly used to simulate AQM for its capabilities in capturing its performance accurately, compared to the continuous model. The discrete-time queue model uses multiple slots, representing equal periods, to capture the network performance [

The following settings were implemented for the simulation environment; a single router buffer of twenty packets capacity is used with the first-in-first-out (FIFO) queuing model. The number of slots used is 2,000,000 slots for warm-up (800,000) and result collection (1,200,000). The warm-up period is enforced at the beginning of the experiments to ensure that the system reached a steady-state. Accordingly, no performance metrics are calculated during the warm-up period. The arrival rates that were implemented are 0.30–0.95 with intervals of 0.05, which generated different congestion status, given that the departure rate is set to a single value of 0.5. In the low arrival rate, non-congestion status is created, the average arrival rate creates light congestion, and the high rate creates a heavy congestion state. The minimum threshold and maximum threshold are set to 3 and 9, respectively, as determined by RED and ERED methods. The parameters of the IRED is set as follows: the threshold and the weight that are used to calculate the indicators are set to 3 and 0.5, respectively, and the minimum and maximum thresholds are set to 0.3 and 0.6, respectively.

The experiment is conducted by initializing and setting the values of the parameters. Then, in each slot, the following processes are implemented, the packet is generated stochastically based on the arrival rate value. If any, the generated packet is loosed, dropped or accommodated depends on the buffer and the running AQM method. Finally, after the pre-determined slots are completed, the results are collected, and the performance is evaluated. The implementation processes are illustrated in

To evaluate the proposed method’s performance, in comparison with the existing AQM methods RED, BLUE and ERED and the recently developed methods FLRED and EnRED, the evaluation measures, packet loss, packet dropping, delay, were used. Packet loss is the consequences of buffer overflow and late congestion status. Dropping can be a prevention strategy implemented by AQM, or it can be a consequence of the wrong diagnosis of congestion when it is not occurring. Delay is the common consequences of congestion phenomena.

In the first case, with an arrival rate equal to 0.9 and a departure rate equal to 0.5, heavy congestion is expected to occur at the router buffer. Accordingly, loss, dropping and delay are expected to be faced in the router buffer. In results comparison, as illustrated in

These results showed that the proposed IRED method outperformed the compared methods RED, ERED and BLUE. Finally, the proposed IRED method outperformed the compared methods ERED in terms of the delay, as given in

In the second case, with an arrival rate equal to 0.5 and a departure rate equal to 0.5, light congestion is expected to occur at the router buffer. In results comparison, as illustrated in

In the third case, with an arrival rate equal to 0.3 and a departure rate equal to 0.5, no congestion is expected to occur at the router buffer. Thus no delay, loss or dropping are expected in the ideal scenario. In results comparison, as illustrated in

Accordingly, the proposed IRED method achieved the intended goal to enhance the network performance in heavy and light congestion while preserving smooth network flow in the non-congestion state. As noted in the results, the proposed method outperformed the compared methods, the original RED, which is the commonly used method for buffer management and the state-of-the-art ERED method. Thus, IRED is a promising AQM method used in various network architectures for best performance results.

Compared to the recently developed AQM method, the proposed IRED method is evaluated with FLRED and EnRED with an α value of 0.3, 0.5 and 0.9, as shown in

As noted, the proposed IRED outperformed the compared FLRED and EnRED in loss and dropping. At the same time, it is expected to have a higher delay, especially while the IRED try to avoid unnecessary loss in congested status.

α | Measure | FLRED | EnRED | IRED | Best | Worst | Improvement (%) |
---|---|---|---|---|---|---|---|

0 | 0 | 0% | |||||

0 | 0 | 0% | |||||

3.90 | 3.92 | 3.92 | 3.5 | 10% | |||

0.03 | 0 | 0.03 | 3% | ||||

0.6 | 0.4 | 0.6 | 33% | ||||

21.32 | 14.35 | 9.85 | 21.32 | 46.2% | |||

0.18 | 0 | 0.18 | 18% | ||||

0.26 | 0.26 | 0.44 | 0% | ||||

28.23 | 25.58 | 17.31 | 28.23 | 10.6% |

This paper aimed to develop a congestion-aware AQM method based on integrated congestion indicators that speculate the queue and network statuses. Accordingly, integrated indicators, which speculate queue and load collectively, were proposed to manage the router buffer and achieve the computer network’s demanded optimized performance. The proposed IRED method extends the well-known RED method. It uses integrated indicators as a combination of the indicators used individually in various AQM methods: BLUE, SRED, AVQ, SAVQ and others. Accordingly, IRED combined the advantages of these methods and eliminated many of the AQM methods’ disadvantages, especially RED. IRED was simulated, and the performance results of IRED compared to the results obtained for RED and ERED. The simulation results showed that IRED outperformed both RED and ERED by decreasing packet loss and delay when heavy congestion occurs, while equally performed with RED and ERED in non-congestion status. Thus, it can be concluded that combining multiple indicators for congestion estimation and dropping calculation can be implemented by creating integrations of these indicators and logically combine them into a single framework. A mathematical investigation of the Dp calculation is also required to fit the developed indicators. Thus, relying on the existing well-known algorithms should be confined by implementing the necessary modification to such algorithms to fit the developed indicators. Future work will focus on eliminating the parameter-setting problem by extending IRED into Fuzzy-logic based technique (FIRED). Moreover, both IRED and FIRED will be implemented in the real environment. These methods’ performance will be compared by their influence on the TCP protocol in various network types, such as wireless networks and multi-hop network.