Demand Forecasting DBA Algorithm for Reducing Packet Delay with Efficient Bandwidth Allocation in XG-PON
Abstract
:1. Introduction
2. Traditional DBA Process
3. DF-DBA Process and Algorithm
4. Simulation Design Environment
4.1. Platform
- As the fundamental intention about this module was once according to analyze XG-PON Transmission Convergence (XGTC) strata then top layer issues, the physical seam is within an easy path via carrying an excellent limit budget, because of the optical allocation network.
- Module implementation does not feature Physical Layer Operations, Administration or Maintenance (PLOAM) and ONU Management yet Control Interface (OMCI) channels are now not implemented.
- XG-PON aqueduct is modelled, so an easy point-to-multipoint (P2MP) race among Down Stream (and multipoint-to-point MP2P within Up Stream) together with propagation delays and rank charges have been configured such care of standards. Conversely, the packets are expected according to arrive, barring someone XG-PON losses, at their same recipients.
- DBA at the OLT is accountable in imitation of allocating the US bandwidth according to TCONT, or US scheduler at ONU is responsible according to assign the transmission possibility regarding some TCONT according to its US XGEM Ports.
- OLT and ONU keep sufficiently significant and separated queue, because of every XGEM Port-ID.
- All ONUs are double to remain at an equal distance out of OLT.
4.2. Simulation Environment
5. Results and Discussions
5.1. Throughput
5.2. Packet Delivery Ratio
5.3. End-to-End Delay
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Symbol | Description |
---|---|
λ(QTCONT) | Instantaneous data arrival rate at the ONU TCONTs queue |
Req[i] | DBRu request sent in the ith frame |
Data[i] | Amount of data sent in the ith frame |
ΔT | Frame size, typically 125 μs |
i | frame number |
μ | Mean of last 100 values of λ(QTCONT) |
σ | S.D. of the last 100 values of λ(QTCONT) |
LTCONT | Predicted demand size |
Algorithm |
---|
//Initialization |
Globals: |
int N = 100; // buffer size |
int Req[N]; //DBRu request in the ith frame |
int Data[N]; //data sent in the ith frame |
int index = 0; |
//Step# 1 Data &request frames saved in circular buffer at OLT |
OnReceiveDataFrame(Frame) |
{ |
index = (index + 1)% N; //circular buffer |
Data[index] = Frame.ReceivedDataSize; |
Req[index] = Frame.DBRU.GetBuffOccupancy(); |
} |
//Step#2 Calculate mean, variance, S.D and predict demand size |
int PredictDemandSize() |
{ |
int Sum = 0; for(i = 0; i<N; i++) |
{ |
int i_minus_1 = ((i − 1)+N)%N; //circular buffer |
sum += (Req[i] + Data[i] − Req[i_minus_1]); |
} |
double Mean = Sum/N; // mean is calculated |
int Variance = 0; for(i = 0; i<N; i++) |
{ |
int i_minus_1 = ((i − 1) + N)%N; //circular buffer |
int Temp = (Req[i] + Data[i] − Req[i_minus_1]); |
Variance += (Mean − Temp) * (Mean − Temp); |
} |
double Var = Variance/N; // variance is calculated |
double SD = sqrt(Var); // S.D is calculated |
int PredictedSize = NormalDist_Random(Mean,SD); |
return PredictedSize + 128; // add piggybacks & predict demand size |
} |
Parameter | Value |
---|---|
OLT | 1 |
Network Load(#ONUs) | 32, 64, 96, 128, 160, 192, 224 |
Time | 30 s, 60 s |
Upstream | 2.5 Gbps |
Downstream | 10 Gbps |
Packet size | 1472 bytes |
Application | CBR, UDP |
TCONT-type | 5 |
Environment Area | 20 km |
Parameters | RR | GIANT | DF-DBA |
---|---|---|---|
Working | Uses a task-based approach and runs each task once, if having a token. | Distributes bandwidth in very first iteration based on TCONTs priority. | Predicts future ONU demands by statistical modelling |
Grant size | Less than or equal to a predetermined fixed size. | Ratio of data rate and US frame size. | Flexible, based on previous bandwidth requests and grants. |
Grant time | Depends on OLT processing and grant time. | Depends on OLT processing and grant time. | Depends on OLT processing time |
TCONT type | T1–T4 | T1–T4 | T5 |
Processing with no. of ONUs | Slow | Medium | Fast |
Throughput | Poor | Poor | High |
PDR | Low | Medium | High |
End to End Delay | High | High | Low |
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Share and Cite
Memon, K.A.; Mohammadani, K.H.; Ain, N.u.; Shaikh, A.; Ullah, S.; Zhang, Q.; Das, B.; Ullah, R.; Tian, F.; Xin, X. Demand Forecasting DBA Algorithm for Reducing Packet Delay with Efficient Bandwidth Allocation in XG-PON. Electronics 2019, 8, 147. https://doi.org/10.3390/electronics8020147
Memon KA, Mohammadani KH, Ain Nu, Shaikh A, Ullah S, Zhang Q, Das B, Ullah R, Tian F, Xin X. Demand Forecasting DBA Algorithm for Reducing Packet Delay with Efficient Bandwidth Allocation in XG-PON. Electronics. 2019; 8(2):147. https://doi.org/10.3390/electronics8020147
Chicago/Turabian StyleMemon, Kamran Ali, Khalid H. Mohammadani, Noor ul Ain, Arshad Shaikh, Sibghat Ullah, Qi Zhang, Bhagwan Das, Rahat Ullah, Feng Tian, and Xiangjun Xin. 2019. "Demand Forecasting DBA Algorithm for Reducing Packet Delay with Efficient Bandwidth Allocation in XG-PON" Electronics 8, no. 2: 147. https://doi.org/10.3390/electronics8020147
APA StyleMemon, K. A., Mohammadani, K. H., Ain, N. u., Shaikh, A., Ullah, S., Zhang, Q., Das, B., Ullah, R., Tian, F., & Xin, X. (2019). Demand Forecasting DBA Algorithm for Reducing Packet Delay with Efficient Bandwidth Allocation in XG-PON. Electronics, 8(2), 147. https://doi.org/10.3390/electronics8020147