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Nearcast: A Locality-Aware P2P LiveStreaming Approach for Distance Education

XUPING TU, HAI JIN, and XIAOFEI LIAOHuazhong University of Science and Technology

andJIANNONG CAO

Hong Kong Polytechnic University

• Peer-to-peer (P2P) live video streaming has been widely used in distance education applications to deliver the captured video courses to a large number of online students.

• By allowing peers serving each other in the network, P2P technology overcomes many limitations in the traditional clientserver paradigm to achieve user and bandwidth scalabilities.

• However, existing systems do not perform well when the number of online students increases, and the system performance degrades seriously.

• One of the reasons is that the construction of the peer overlay in existing P2P systems has not considered the underlying physical network topology and can cause serious topology mismatch between the P2P overlay network and the physical network.

• The topology mismatch problem brings great link stress (unnecessary traffic) in the Internet infrastructure and greatly degrades the system performance.

• In this article, we address this problem and propose a locality-aware P2P overlay construction method, called Nearcast, which builds an efficient overlay multicast tree by letting each peer node choose physically closer nodes as its logical children.

• In our previous work, we have developed a P2P media streaming system called Apple for delivering live video courses to remote online students [Jin et al. 2004]. However, Apple does not perform well when the number of the online students increases.

• The major reason is that the method for constructing the peer overlay in Apple does not consider the topology mismatch problem.

• To address the topology mismatch issue, Nearcast builds an efficient overlay multicast tree by letting each peer node choose physically closer nodes as its logical children.

• The captured video course content is delivered from the root of the overlay tree.

• Each node on the tree can receive the media data from its upper-level nodes to play back while relaying it to the lower-level nodes.

• Our proposed Nearcast also uses the tree-based method, but it differs from the above systems in that it takes into account the locality issue by assigning a well-designed, prebuilt coordinate to each peer through which it measures the distance and selects a close parent.

• Nearcast also considers the real-life network connectivity constraint issue, which is not addressed by any of the overlays already mentioned.

PRELIMINARIES AND ASSUMPTIONS

• Each end host is aware of its own network position coordinate and connectivity constraint.

• The last hop(s) to an end host exhibits (exhibit) the lowest delay.

• The design of Nearcast has not considered a long-latency link, for example, a satellite link, as the last hop.

• For the latencies on the links between the nodes in the hierarchical physical network, the triangular inequality2 [Guyton and Schwartz 1995] holds.

Tree Management

• To effectively construct and maintain the Nearcast tree, an end host should maintain a small amount of state information, including IP addresses and port numbers, layer numbers, connectivity constraints, and its network position coordinates, its parent and grandparent, the source host, and its children hosts.

• The layer number of an end host X, denoted by h(X), represents the order of the layer at which X is located in the Nearcast tree.

• The protocol for tree maintenance includes the operations for the host to join and depart from the overlay.

Host Departure.• We propose methods to handle a host departure that

occurs gracefully or accidentally. A graceful departure occurs when a host X intends to leave the overlay, while an accident departure occurs when X fails.

• The failure of X can be detected by its children since the video data stream would be interrupted. For the graceful leaving procedure, X sends out “Leave” messages to both its parent and children.

• If a child receives the “Leave” message, it immediately sends a “Rejoin” message to its original grandparent. If a child detects that its parent has failed, it immediately sends out a “Parent Leave” message to its original grandparent.

Results• Control Overhead.• From Figure 7 it can be seen that Nearcast carries much less

control overhead compared with NICE and RTT. In NICE, group merging and division and periodically sending alive messages to each other for maintaining the group size lead to a large number of control messages and cause some stress on the routers.

• In RTT, there are many delay measurement messages injected into the overlay. In Nearcast, however, groups in each layer are organized by their locations, so there is little restriction on the group sizes.

• A peer’s availability is detected by the interruption of the media data stream transmission. Therefore, the number of the control messages is greatly reduced.

Comparison of control overhead between Nearcast and NICE.

• Link Stress. • From Figure 8(a), it can be found that, as the total number

of nodes increases continuously, Nearcast has less average link stress than NICE, which slightly outperforms RTT. In NICE, the leader peer in each group knows only the distances between the peers within the group, but not the distances between the peers outside the group. Thus, while the media data stream expands outward, members in an upper-layer group may not always choose the nearest peer as their supplier. Therefore, the average link stress increases.

• In Nearcast, however, intentional reservation according to the network location value is used to organize the subtree of each layer.

• This helps the supplier selection, and improves the efficiency of the physical network.

Fig. 8. Comparison of link stress between Nearcast and NICE.

• End-to-End Delay and ADP. • Figure 9(a) shows the distribution of the average EED and the average ADP

for different group sizes. In all cases, Nearcast is better than NICE in terms of EED.

• This also is due to the inaccuracy in the supplier selection in NICE. The RTT scheme uses the RTT directly as the metric to cluster the near peers, and thus can perform better tha the other two schemes in terms of EED. However, when the number of peers increases, the average link traffic increases so the EED becomes unstable and is usually different from the estimate of the first time.

• Consequently, the EED in RTT scheme becomes larger than that in Nearcast when the number of peers increases. As we can see in Figure 9(b), when there are a few peers in the multicas tree, the absolute latency in NICE and RTT is less than that in Nearcast.

• This is mainly because that Nearcast uses the peer’s capability of reservation to organize the multicast tree, so that the capability of the upper-layered peers is not fully utilized, leading to the growth in height of the multicast tree. However, as the system scale grows gradually, the reserved capability of the upper-layere peers is utilized, which improves the absolute latency.

CONCLUSIONS

• In this article, we have presented Nearcast, a locality-aware P2P live video streaming approach to deliver video courses to a large number of remote students in distance education applications. Nearcast is a single-source overlay multicast approach that uses prebuilt network coordinates of the end hosts to organize the overlay multicast tree. It takes into consideration the topology mismatch problem and considers the network constraints, such as limited last-hop bandwidth and connectivity constraints in the tree construction and recovery procedures.We have evaluated the performance of Nearcast using both simulations and real deployment experiments. Compared to the existing approaches, Nearcast significantly improves the system performance with lower link stress and network latency.