Summary

With more and more departure of peers, recovery switching time reduction become more and more important, however, the traditional random approach leads to long recovery switching time, and we proposed a approach to resolve this problem, our method is practical and easy to implement and maintain. The main contribution of this thesis is:

i. Reduction of the recovery switching time.

ii. QOE enhancement.

iii. Reduction of the load of the super-peers.

Chapter 4 Simulation Evaluation and Numerical Results

We evaluate our proposed approach and traditional random approach by using OMNet++ [17] simulator. First of all in this chapter, we roughly introduce the OMNet++ tool and how it works, then we provide parameter situation and show the numerical results by graphical output of our proposed approach and traditional approach.

4.1 Simulation tool

OMNet++ is an extensible, modular, component-based C++ simulation library and framework with an Eclipse-based IDE and a graphical runtime environment. OMNet++

is similar to C++, even more suited to P2P program, but itself does not provide components specifically for computer network simulations, queuing network simulations, system architecture simulation, etc.

4.2 Simulation scenario and parameter setting

In Section 4.2, we provide the simulation scenario and parameters used in our proposed approach and traditional random approach.

4.2.1 Simulation scenario

• Peers form mesh-shape overlay.

• Partners share video chunk by round-robin simultaneously.

• There are multiple tracker servers who hold the streaming data source, peer-list and candidate partner list.

• Every partner keeps its channel, available UB, receiver, chunk.

• Every peer reserves certain upload bandwidth (UB) with factorβ.

• Most of the peers leave in normal mode, rest of the peers leave in abnormal mode.

• Proposed approach :

– Every partner keeps successor, parents sequence number, parents IP address and partner number.

– The sequence number of the successor partner = [(the sequence number of the departed partner) + 1] modulus (the number of partners).

Figure 4.1 Simulation network configuration

Asymmetric network environment in figure 4.1 means most of peers have difference gap between upload bandwidth and download bandwidth, this situation is very common in our network. For our example, partner 0 , 1 and 2 contribute its

bandwidth (video streaming rate / the number of partners, ex: 512Kbps / 3) to the new peer by using round robin way at the same time. This way, the upload bandwidth below video streaming rate still can contribute its bandwidth to transfer video chunks to other partners if it had enough available upload bandwidth.

In our proposed architecture, we use switch to connect every peers, figure 4.1 is the easiest module to implement our method, this architecture can be further expand and scaled to hundreds and thousands peers. The simulation delay and upload / download bandwidth constraint on each peer via OMNet++ tool to setup.

4.1.2 Parameter setting

We can see the parameter setting in Table 4.1, since our proposed approach is push-pull system, hence we use mesh overlay topology, we set our simulation time to 1 hour. Each fixed video chunk is 512Kbits which equals to 1 second of video for streaming rate of 512Kbps. Although higher rate means better QoE, we take lower download bandwidth user into account, and further, 512Kbps is enough for P2P IPTV with decent quality. The range of the number of partners between 3 and 8, the partner’s departure probability of partners means the possibility that partners will leave from the system is set from 0.01 to 0.5, and the upload bandwidth varies from peer to peer.

Table 4.1 System parameter setting

4.3 Numerical results

In Section 4.3, we compare our proposed approach with random approach by running simulation tool OMNet++ 4.0 with above scenario and parameters, and shows the results in graphical way via Microsoft Excel tool.

Figure 4.2 Switching time (ms) vs. Partner numbers under normal mode

Table 4.2 Improvement of the switching time (ms) under normal mode

As we observed regarding the degree of reduction of the switching time in Figure 4.2 and Table 4.2, our proposed method dramatically reduces the switching time that started from a partner leaving to a new partner successor transferring next video chunk, the gap between our proposed approach and traditional random approach grows as number of partner grows larger. However, the switching time of both approaches decrease grows as number of partners grows higher.

Figure 4.3 Switching time (ms) vs. departure probability under normal mode

As we can see in figure 4.3 and table 4.3, while the probability of the departure of peers grow up, the recovery of switching time of the traditional random method increases dramatically, and thus enlarge the gap from our proposed approach. This is because when more and more peers leave from the system, all of the request will overwhelm the tracker servers, thus the recovery switching time must be put off.

Table 4.3 Improvement of the switching time (ms) under normal mode with different departure probability

Table 4.3 shows our experiment with the number of partner 3, and our proposed method

has minor increase when the probability of the departure of peers grows higher.

Figure 4.4 Switching time (ms) vs. number of partners under abnormal mode

Table 4.4 Improvement of the switching time (ms) under abnormal mode

As we observed regarding the degree of reduction of the switching time in Figure 4.2 and Table 4.3, the degree of improvement in our proposed method dramatically reduces

the switching time much more than that on normal mode. Due to the less message transmission on abnormal mode, the timer expiration is postponed under abnormal mode.

Table 4.5 Improvement of the switching time (ms) under abnormal mode with different departure probability

Figure 4.5 Switching time (ms) vs. departure probability under abnormal mode

As we can see in figure 4.5 and table 4.5, while the probability of the departure of peers grow up, the recovery of switching time of the traditional random method increases dramatically, and thus enlarge the gap from our proposed approach on abnormal mode.

Chapter 5 Conclusion

With the widespread adoption of broadband residential access and the advances in video compression technologies, the P2P IPTV has become more and more popular.

However, the reduction of recovery switching time problem has become significant due to the frequent churn of peers.

In traditional random approach, recovery switching time is quite long due to the far distance from peers to tracker servers and hence the larger message exchanging delay needed by the complex standard operating procedure even if it is an intuitive way to implement.

In order to reduce the recovery switching time, we choose local partners as temporary successor partners until it finds one or more constant partners via tracker server in our proposed approach.

We use OMNet++ 4.0 simulator tool to evaluate our proposed approach comparing to traditional random approach. The improvement of the recovery switching time of our proposed approach is about 24~60% under normal mode, and 41~67% under abnormal mode. The improvement gap of the recovery switching time between our proposed approach and traditional random approach increases while number of partners grows up.

Our proposed approach is practical and feasible to implement and maintain.

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