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Nearcast: A Locality-Aware P2P Live
Streaming Approach for Distance Education
XUPING TU, HAI JIN, and XIAOFEI LIAO
Huazhong University of Science and Technology
and
JIANNONG 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 localityaware 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 treebased 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 longlatency 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.