This is a new strategy called CoRe for media retrieval in P2P systems.

1. A New Retrieval Strategy for P2P Video-on-Demand Systems
Ashwini R. More
CS Graduate student, SDSU
San Diego, CA 92182
ash.more@yahoo.co.in
Mounika Eluri
CS Graduate student, SDSU
San Diego, CA 92182
mounikaeluri28@gmail.com
Abstract
Delivering the media content over the network with
best response time has been a popular topic [2] [4] [5]
[8]. There are many efficient algorithms that have
addressed this issue such as the Least Load First
algorithm [4]. As its name states, Least Load First
algorithm selects a serving peer having the least load
for delivering the media content. Since only one peer is
responsible for servicing the request, it takes more
time to respond to the request, thereby affecting
response time. To address this limitation, we,
therefore, propose a novel and efficient strategy for
retrieving the videos. This scheme is called CoRe
abbreviated from our technical theme of
“collaborative retrieval”. Our objective is to achieve
minimum response time and maximize the overall
throughput of the system. In this strategy, we partition
the workload between peers over the network. There
are two main characteristics of CoRe: first, it selects
multiple peers for servicing the requests, and second, it
takes into account the distance factor from the
querying source while selection of these serving peers.
This helps to further maximize throughput and
minimize the mean response time. Moreover,
experimental results will show that CoRe performs
significantly better than existing Least Load First
algorithm even in the case of heavy workload.
1. Introduction
The VoD (Video on Demand) is a most popular
system [6] which allows users to select and
watch/listen to video content when they choose to,
rather than having to watch at a specific broadcast
time. There are many applications of the VoD service
namely digitally transmitted movies, local news and
weather forecasting, live streaming videos etc. VoD is
also used in educational institutions (viewing training
videos) and can enhance presentations in
videoconference environments.
In today’s busy world, every person desires
instantaneous response and so, fast response time is the
necessity. A good example is the online streaming
video-sharing website www.youtube.com. Videos on
such popular video-sharing website usually experience
a heavy demand and generate significantly large
amounts of traffic over the network. In this scenario,
response time is of paramount importance to the
impatient end users. When large numbers of consumers
demand multiple videos on a continuous basis, the total
amount of data involved (in terms of megabytes) can
overwhelm network resources. The performance of
VoD applications heavily depend on the underlying I/O
systems that enable the ability to serve user requests
almost instantaneously. An important consideration of
these VoD systems is to minimize the response time.
On the other hand, the system administrator’s main
goal is to maximize throughput – the total amount of
user requests that can be processed over a fixed period
of time. Response time and throughput are often
closely related since in most cases, the overall
throughput can be maximized by minimizing the
response time for individual requests.
Several architectures [2] [5] [8] [12] [13] have been
designed for VoD systems to gain efficiency. The
current Video on Demand systems are designed based
on the client server architecture. In this architecture,
the video server stores the videos and whenever the
client sends request for a video to the server, video
stream is downloaded on the client system and is
available for playback. As the number of client request
increases, the load on the server increases and due to
this there is rapid consumption of bandwidth.
Therefore, the design of the VoD system requires high
data rates to serve the large number of concurrent
clients. So, the design of such VoD systems faces
significant challenges.
Parallel [5] and distributed server architectures [8]
are used to balance the load on the server by
accommodating additional servers. Multiple servers are
involved in servicing the incoming requests thereby
partitioning the workload. The drawback of these

2. servers is the cost of upgrading the servers are high
since it requires high end hardware with large amount
of bandwidth and buffer.
Multiple approaches such as Content Delivery
Network (CDN), Proxy based system, Peer to Peer
system are used to decrease the cost factor required for
upgrading video servers. Content Delivery Network
(CDN) [13] approach makes use of web caching to
improve the client response times. In this approach it
reduces server load, (1)can deliver content faster and
improves availability. However, it is not cost-effective
due to high maintenance cost of servers and so
impractical for many organizations. In proxy based
system [12], the proxy server acts as an intermediary
between a web browser and the internet and improves
web performance as it uses web cache to store
frequently accessed web content. However, this
approach suffers from scalability problem. In P2P
(peer to peer) approach [2] [9], the load on the media
server is reduced as it partitions the workload between
peers. This approach is more reliable and the overall
cost of building and maintaining this kind of system is
comparatively very less. However, all the peers over
the network are not involved in streaming of the videos
which affects response time. In this paper, we address
this issue and propose an optimized algorithm, named
CoRe (Collaborative Retrieval) that tends to minimize
the response time. Another drawback of peer to peer
systems is that they have less bandwidth and also have
smaller storage capacity.
Researchers have developed several segment-based
proxy caching schemes [2] [3] to handle the large sizes
of media objects. In these, the media objects are cached
partially instead of entirely. Although the segment-
based caching strategy has shown its effectiveness for
media streaming, it fails to provide the quality of
service to clients due to following reasons. First, the
storage capacity of proxy is limited which will restrict
the amount of media content it can cache for users.
Second, a proxy becomes a system bottleneck, and also
forms a single point of failure. On the contrary, the
resources such as storage, bandwidth and CPU cycles
are richly available and under-utilized among the peers
in the P2P system.
A client side caching scheme called, earthworm [3]
was proposed to further minimize the load on the VoD
server. In this scheme, the client plays the video and
also forwards the video streams to another client with
sufficient buffer and delay, which is known as basic
chaining. This earthworm scheme is further extended
as forward, backward, optimal and adaptive chaining
[1] [3] [10] [11]. But, the client resources such as
uplink bandwidth and buffer are exploited. The
demand for high quality videos along with longer
duration of videos is increasing to a great extent.
Therefore, for such applications with high demand, the
existing chaining schemes fail to meet the scalability
requirements like buffer and bandwidth.
The chaining schemes mentioned above mainly
focus on the server load. To help improve the
reliability of media content in the peers, the redundant
movies are placed in the peers over the network.
Redundancy of movies can be implemented by several
different methods such as replication technique in
which multiple copies of movies are stored in the peers
across the network. The copies of the movies are
referred as replicas.
The proposed strategy of Mohamed and Bharat [7]
uses the special kind of control messages known as
heart beats for recovery from peer failures. Each peer
over the network sends the heart beat messages to its
parent and backup peers. This heart beat messages are
short messages which are sent over the reliable control
connection. Failure of a node is detected with the loss
of one or two heart beat messages. If the parent does
not receive two successive heart beats, the sender is
assumed to be failed. The drawback of this strategy is a
large overhead of control messages that are exchanged
over a longer period of time. In our approach we are
not using any kind of control messages; instead we are
selecting the optimal path for movie transmission with
lesser duration of time.
In our approach, we first build a scalable, reliable
and cost effective VoD system. To decrease the load
on the server, we combine peer to peer system and
proxy based system. Unlike client server architecture,
video streams are duplicated and placed in all the peers
over the network in such a way that none of the serving
peers store multiple copies of the same movie.
However, designing a reliable VoD system is a great
challenge; we investigated the streaming, i.e.
scheduling the video streams from the closest clients.
The CoRe strategy makes the selection of the serving
peers based on the cost factor measured with respect to
bandwidth and distance.
To prove the merit of CoRe over other algorithms,
we perform a comparison against Least Load First
algorithm, which will be discussed in detail later in the
Related Work section. In addition, results will show
that CoRe continues to perform well as the workload
increases.
To achieve these goals, performance will be
measured for Least Load First and CoRe strategies.
Next, results of the two algorithms will be compared to
show that CoRe strategy for responding to the request
provides performance improvements over Least Load
First.
The rest of this paper is organized as follows.
Section 2 gives a brief summary of the Least Load
First algorithm. Section 3 discusses the CoRe

3. algorithm in detail. Section 4 presents the experimental
results for both CoRe and Least Load First algorithm.
We have used a realistic simulation model for the
performance study. The performance metrics and the
experimental parameters that are used to generate the
workload are explained. Finally, Section 5 concludes
the paper.
2. Related Work
There are generally three major steps involved in
designing and building of any peer to peer VoD (Video
on Demand) system. Firstly, determine the copies of
videos i.e. replicas to be placed on different peers over
the network. Secondly, a data placement policy is
required to decide, how many number of replicas
should be stored in different peers. Thirdly, a selection
policy is required for selecting the peers that contains a
replica of the movie to serve the incoming requests.
The first step of determining the number of replicas
required for a particular movie is calculated using the
replication strategy proposed by Ashok Kumar and
Ganesan [4]. The second step of identifying the peers
for placement of the replicas follows the smallest load
first strategy. The peers having the least load are
identified first and the replicas are stored on them
accordingly. For the third step, several algorithms for
selection of the serving peers are proposed to attain
minimum response time. One example, Least Load
First algorithm, selects the least loaded peer for
servicing the request. Whenever a non-serving peer
makes a request for a movie with its existing resource
information to the proxy server, then proxy server
replies to this non-serving peer with the list of all
active serving peers which contains the requested
movie in it. Now the non-serving peer should make a
decision in selecting a serving peer from the list . In
Least Load First, the non-serving peer checks for the
least loaded serving peer to receive the requested
movie. The non-serving peer makes a request of
currently available resource information to all the
serving peers in the list. Then the requested non-
serving peer receives currently available resource
information from all the requested serving peers and it
sorts all the requested serving peers in non-increasing
fashion based on the available resource of the
requested serving peers. Finally, the non-serving peer
will select a serving peer with the highest resource
availability and least load for the reception of the
movie. The drawback of this strategy is that, since only
one peer is responsible for servicing the movie request,
the time it takes to respond the querying source is
more. Also, the serving peer may be far distant from
the querying source. Our proposed CoRe strategy
addresses these two problems with a goal of
minimizing system response time and maximizing
throughput.
3. The CoRe algorithm
The proposed model is a mixture of peer to peer
systems and proxy-based architecture, combining the
efficiency of both architectures.
The model consists of following components as shown
in Fig.1.
 Multimedia server
 Proxy servers and
 Peer-to-peer systems
Fig 1. Architecture of P2P VoD System.

4. Fig 2. Popularity vs. no. of replicas.
Multimedia server holds movie files with a variety
of information such as index, popularity, size, duration,
minimum buffer, and maximum bandwidth of the
movies. The overall load of the system is uniformly
divided among the clusters. A cluster is composed of
group of peers which are connected to the proxy
server. The proxy server maintains a database of all the
available peers in the cluster. In the peer-to-peer
system, a peer is of two types- serving peer and a non-
serving peer.
We consider M to be the set of movies {m1, m2,..
mm} and the size of the mth
movie is denoted by Sm.
Each of these movie m is equally divided into V video
blocks such that m= ∑
Sm
i=1 Vi.
These video blocks are duplicated on all the peers of
the cluster, so when any of the peer requests for the
movie file, the peers holding the video block of the
requested movie will collectively contribute to serving
the request. Intuitive behind this idea is to improve the
mean response time. Here, the work load on the main
multimedia server is relaxed because of the load
distribution among the peers.
The request arrival rate for the mth movie is
exponentially distributed with a mean rate Λm and the
popularity of the mth movie; pm is derived from zipf’s
law. At the beginning, the proxy server maintains a list
of serving peers in its database. This list consists of
numerous parameters such as requested movie, CPU
speed, bandwidth, distance factor and storage capacity
of the serving peer. Whenever a peer sends a new
request to the main server, this request is redirected to
the proxy server where the peer belongs to that same
proxy server. In response, the proxy server replies back
with a dynamic list of available serving peers that have
maintained a copy of the requested movie to the
requesting peer. If the requested movie is available, the
querying peer selects the serving peers according to the
CoRe algorithm. If the requested movie is unavailable
in the proxy server or in any of the serving peers then
Input: Batch of movie requests, list of available
peers, list of movie replicas distributed across multiple
peers.
Output: response time for each request
1. for each request ri do
2. size = getSize(ri)
3. Get list of available peers containing the movie
and store in list Lp
4. Total = count (Lp)
5. for each peer pi in list Lp do
6. Set distance with respect to the request source
7. end for
8. Sort the list Lp according to the distance factor in
ascending order
9. for each peer pi in list Lp do
10. Calculate the cost, cost [pi]=distance[pi]/Total
subjected to that the sum of the costs of all
peers is approximately equal to 1. Also, the
peer with small distance value should have cost
value maximum and decreases linearly for
others as the distance increases.
11. Request_service_time = size *
cost [pi]/transfer_rate (31Kbps assumed)
12. end for
13. Record start time and end time for request ri
14. end for
Fig 3. Collaborative Retrieval (CoRe) Algorithm.
the initial portion of the video blocks are directly
streamed from the main server. The later portion of the
video blocks are first downloaded and buffered in the
proxy server itself which is then streamed directly from
the proxy server to the querying peer. With this
strategy, the load on the main multimedia server is
reduced due to the load sharing amongst the peers.
Also, the overall system resources are utilized quite
efficiently because it manages to use only the residual
bandwidth and buffer of the serving peers to serve non-
serving peers.
The objective of replication strategy is to ensure the
availability of the movies in the cluster, even in case of
serving peer failures. This is achieved by replicating
the copies of the movies in the serving peers. Various
replication strategies have been proposed to increase
the availability of videos. We have generated replicas
according to the replication strategy proposed in [4]. A
copy of the movie is always maintained in the proxy
server and replicas are also stored in different serving
peers while transmitting the movie in the cluster. This
helps in maximizing the availability of the movies in
the cluster. The peers with sufficient storage space are

5. identified and the copies of the movies are placed in
these serving peers accordingly. With this replication
strategy, the highest popular movies will have large
number of replicas and the replicas decreases as the
popularity decreases. Fig. 2 clearly illustrates this
characterization. After generating the number of
replicas to be placed, these replicas are further placed
in the serving peers according to the Smallest Load
First strategy [4].
Further, during servicing the request, the selection of
the serving peer is based on the least load of the peer
along with consideration of the closest ones. The
reason for the serving peer not being available can be
network failure, software or hardware failure,
insufficient network bandwidth or insufficient storage
space to store replicas. In our project, we have assumed
that peer is always available for responding to the
request.
Our main goal of minimizing the response time is
met by the Collaborative Retrieval (CoRe) Algorithm
(see Fig. 3). Based on the popularity and inter arrival
request rate of the movie, we have generated the
replicas of the movies. These duplicated copies are
stored in all the available serving peers in such a way
that the peer with smallest load is selected first to place
the replica. The popularity of the movie is derived
from zipf’s law. We have used the technique that
generates more number of replicas for the most popular
videos and the number of replicas decreases linearly
for the least popular videos [4]. Whenever a proxy
server receives request for a movie from the peer, it
replies back with the list of serving peers available for
servicing the request to the non-serving peer. While
responding to the incoming request, we first find out
the size of the requested movie. Then we find the total
number of peers available for serving the request, i.e.
the peers containing the requested movie. The distance
of the available peers from the request source is taken
into consideration while selecting the peers for
servicing the request. The selection of the nearer
clients is based on the cost factor measured with
respect to distance and the total number of peers that
are available for servicing the request (see step 9). The
percentage of a movie file to be downloaded from
these selected clients is decided on the cost factor (see
step 11). The transfer rate of a movie file is assumed to
be 31Kbps. The total time taken by the peers to serve
the request is calculated (see step 13). The details of
this calculation are described below.
In its most general form, a peer to peer system can
be represented by a group P of independent
homogeneous peers: P = {p1,…,pn}. The movies M =
{m1,…,mn} are to be placed on these peers. A peer pi
can be represented by pi = (ci, ti), where ci is the
capacity of the peer in GByte and ti is the available
time of the serving peer. The assumption is peer
storage capacities are quite large enough to store all of
the movies. A movie file mi consists of (ui, λi, si, li)
where ui is the popularity of the movie, λi is the mean
arrival rate of requests to a movie file, si is the size of
the movie file and li is the number of replicas to be
stored in different peers. For this research, λi is the
mean arrival rate of requests coming in for a movie file
mi. Accesses to a file exhibit a Poisson distribution
with a mean arrival rate λi. To calculate the percentage
of movie file to be downloaded from a particular peer,
first the distance factor is set from the querying source.
A distance array D is represented by D = (pi, di, ci)
where pi is the peer on which the replica of movie
request is present, di is the distance of the peer from the
requested source, ci is the cost in terms of percentage
of the movie file to be downloaded from peer pi. This
cost factor is calculated for each of the peer pi selected
for servicing the particular request. The sum of the cost
calculated for each of the serving peers should be equal
to 1, which represents the entire movie file. We assume
the distance between the source and destination node to
be constant.
A request set R consists of n total number of
requests and can be modeled as R = {r1,…,rq}.Each
request can be represented by rk = (midk, atk), where
midk, atk are the movie file identifier targeted by the
request, the arrival time of the request. When an
incoming request arrives, the CoRe algorithm finds
what peer the target file is placed upon. The request is
then directed to the peer’s local scheduling queue.
Two important parameters that are considered here
are the start time and the end time of a request rk. Start
time and end time are represented by sti(rk) and eti(rk),
respectively. The start time and end time must be
calculated in order to get the response time of a request
rk. Both the times are derived as below. When a request
rk arrives, two cases can be described as follows. The
first is when pi is idle, and the second is when pi is
busy.
sti(rk)={
atk,if pi
is idle
atk+ri,if pi
is busy
(1)
where atk is the arrival time of the request and ri is the
remaining service time of the request that is currently
running on peer pi.
It then follows that the eti (rk) can be represented by
eti (rk) = sti (rk)+ t midk (2)
where t midk is the service time of the movie file that is
targeted by request rk.
The response time of request rk can be calculated by
rti (rk) = eti (rk) - sti (rk) (3)

6. The mean response time for the request set R can be
calculated by
mrt( 𝑅)=
1
n
∑ rti(rk)
n
k=1,1≤i≤n
(4)
where n is the total number of requests.
4. Performance Evaluation
In this section, we evaluate the performance of both
Least Load First algorithm and CoRe using a synthetic,
yet realistic workload. Initially, we will present data
that compares the response times of CoRe with Least
Load First strategy over different aggregate access
rates. Also, we present the response improvement of
the proposed CoRe algorithm in terms of percentages.
Results will clearly show that CoRe performs
significantly better than Least Load First algorithm
especially under heavy workload conditions.
4.1. Simulation setup
In this section, we use simulation to evaluate the
performance of the proposed technique and compare
the results with the existing technique. We have used
Java to evaluate the performance of the system and
MATLAB software to plot the resulting graphs. We
evaluate the effectiveness of the proposed CoRe
(Collaborative Retrieval) algorithm by comparing it
with Least Load First algorithm. The algorithm is
described below.
Least Load First: When a peer sends a movie request,
the serving peer having the least load is chosen for
serving the request. Only one peer is involved in
processing the request.
Our proposed algorithm guarantees minimal
response time as it involves multiple peers in streaming
of videos. We have concentrated on the average
Fig 4. Average response time for 70/30 skew.
response time as the primary performance metric. The
performance metrics by which we evaluate system
performance include:
Mean response time: average response time for all
video access requests that are sent to the simulated
peer-to-peer system.
Mean response time improvement: decrease in
percentage of mean response time gained by CoRe
compared with Least Load First.
Before presenting empirical results, we present the
simulation model as follows. Table 1 summarizes the
configuration parameters of simulated peer-to-peer
system used in our experiments. Despite the fact that
the workload was synthetically generated, all
parameters were carefully controlled in order to model
the workload as accurately as possible. The sizes of the
files were distributed according to a zipfian distribution
with a skew parameter θ = (log X/100) / (log Y/100),
where X percent of all accesses were directed to Y
percent of files. We conducted experiments with the
skew parameter θ corresponding to either 70-30, 60-40
or 50-50 distributions. We tested the response times
with the number of requests varying from 2000 to
15000. The size of the movie is variable. We provided
input of 500 movies stored across 100 peers over the
network. The replicas of these movies are placed in the
peers according to smallest load first strategy.
Table 1. System parameters.
Parameter Value
Number of requests 2000-15000
Number of peers 100
Number of movies 500
Skew 50-50, 60-40, 70-30
Aggregate access 50, 100, 150, 200, 250, 300
(1/second) rate
Fig 5. Average response time for 60/40 skew.

7. Fig 6. Average response time for 50/50 skew.
Fig 8. Response Improvement (%) for 60/40 skew.
4.2. Impact of aggregate access rate
We observe from Fig. 4 that CoRe consistently
provides best response time compared to the Least
Load First algorithm for skew θ corresponding to
70/30 distribution. For example, for aggregate access
rate, λ = 200s-1
, CoRe provides a 20 percent
improvement over the response time of Least Load
First algorithm. The differences in response times of
the two algorithms increase as the aggregate access
rate increases. Similarly, Fig. 5 and Fig. 6 illustrates
the response times of both algorithms corresponding to
60/40 and 50/50 distribution respectively. It clearly
depicts that the CoRe strategy performs significantly
better than Least Load First in both graphs. Thus, the
experiments justify our claims about the importance of
involving multiple peers in the task of video retrieval.
Fig. 4, Fig. 5 & 6 also shows that, the higher the
aggregate access rate is, the more significant is the
improvement achieved by CoRe algorithm.
The differences in response times increase slowly
with smaller values of the skew parameter θ. However,
Fig 7. Response Improvement (%) for 70/30 skew.
Fig 9. Response Improvement (%) for 50/50 skew.
for θ corresponding to 60/40 distribution and aggregate
access rate λ = 200s-1
, CoRe still provides a 34 percent
improvement over Lead Load First algorithm.
Fig. 7 illustrates the response improvement
percentage of CoRe over the Least load First algorithm
for skew θ corresponding to a 70/30 distribution. The
results show that the greatest improvement of response
time occurs initially at λ = 50 (23%), decreases till λ =
150 (15%). The response time improvement increases
from λ = 150 as the aggregate access rate increases.
The highest response improvement of 36 percent
occurred at λ = 300. Similarly, Fig. 8 and Fig. 9
illustrates the response improvement percentage of
CoRe over the Least load First algorithm for skew θ
corresponding to a 60/40 and 50/50 distribution
respectively. Skew 60/40 shows highest response
improvement of 54 percent at λ = 50, while in skew
50/50, the highest response improvement is of 64
percent at λ = 100.

8. 5. Conclusions
Fast response time is an essential technology factor
in today’s world. Considerable research work has been
done to find more efficient ways of dealing with video
on demand systems in such a way that fast response
time can be achieved [2] [4] [5]. In this paper, we
studied the existing retrieval strategy, Least Load First,
which does not involve all the peers over the network
in streaming of videos, thereby affecting the response
time. In this project, we address this issue by involving
multiple peers based on their distances from the
querying source in serving the requests with an
objective of minimizing the response time.
Consideration of the optimal distance factor
contributed significantly in achieving efficiency. We
proposed a new retrieval strategy, CoRe (Collaborative
Retrieval) for peer-to-peer video-on-demand systems.
Our experimental results show that CoRe strategy
noticeably outperforms the Least Load First strategy.
The CoRe algorithm effectively reduced mean
response time. Intuitively, it follows that overall
system throughput would also be increased.
Further studies in this research can be performed by
taking into consideration the issues like, data
corruption, peer or network failure and recovery which
can contribute to performance improvement.
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