Peer to peer cache resolution mechanism for mobile ad hoc networks

International Journal of Wireless & Mobile Networks (IJWMN) Vol. 5, No. 5, October 2013

PEER TO PEER CACHE RESOLUTION MECHANISM
FOR MOBILE AD HOC NETWORKS
Preetha Theresa Joy1 and K. Poulose Jacob2
1,2

Department of Computer Science, Cochin University of Science and Technology,
Kochi, Kerala, India.
preetha@mec.ac.in,kpj@cusat.ac.in

ABSTRACT
In this paper we investigate the problem of cache resolution in a mobile peer to peer ad hoc network. In our
vision cache resolution should satisfy the following requirements: (i) it should result in low message
overhead and (ii) the information should be retrieved with minimum delay. In this paper, we show that
these goals can be achieved by splitting the one hop neighbours in to two sets based on the transmission
range. The proposed approach reduces the number of messages flooded in to the network to find the
requested data. This scheme is fully distributed and comes at very low cost in terms of cache overhead. The
experimental results gives a promising result based on the metrics of studies.

Keywords
Cache Resolution, MANET, Cooperative caching, Wireless Networks.

1. INTRODUCTION
Recent wireless communication growth and the continuous improvement of mobile terminal
performance have enabled the use of these technologies in different fields and to consider new
applications. There are two categories of wireless mobile networks: networks with infrastructure
using access points to connect to a wide range of network and networks without infrastructure
which do not presuppose the presence of a wired infrastructure. An ad hoc network is a wireless
self configurable network made up of mobile free moving terminals interconnected by wireless
connections. Ad hoc networks provide a way of communicating which can be quickly and easily
deployed. This makes the ad hoc networks a good choice for applications in which various civil
and military fields.
The goal of a computer network is to offer a way for users with different terminals to
communicate in order to share data and access services provided by different servers. In mobile
ad hoc networks, devices generally have limited energy reserves and processing capabilities.
Bandwidth is also a scarce resource, limited by the nature of the wireless medium. In a datamanagement point of view, these restrictions introduce several issues that need to be addressed.
Data transfers must be reduced and mechanisms must be deployed to confront the frequent
disconnections and low bandwidth constraints. Therefore it is a challenging task to present the
data efficiently by reducing the delay or waiting time to the end user.
Data caching is widely used in various domains to improve data access efficiency, by reducing
the latency experienced by the end users. In wireless mobile network, holding frequently accessed
DOI : 10.5121/ijwmn.2013.5513

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data items in a mobile node’s local storage can reduce network traffic, response time and server
load. Caching in ad hoc networks is effective because a few resources are requested often by
many users, or repeatedly by a specific user, which is known as the locality of reference. To have
the full benefits of caching, the neighbor nodes can cooperate and serve each other’s misses, thus
further reducing the wireless traffic. This process is called cooperative caching. Since the mobile
nodes can make use of the data stored in another node’s cache the effective cache size is
increased.
The implementation of a good cooperative caching technique essentially involves four major
design considerations: cache placement and resolution, cache admission control, cache
replacement and cache consistency maintenance [1]. The cache admission control module decides
whether a received data item is cacheable or not, and a cache resolution module decides how to
fetch the data from the neighboring nodes when there is a local miss. Since the network node has
limited memory, it can cache multiple data items subject to its memory capacity. The objective of
cache admission control module is to store more distinct data items in the given cache space. This
reduces the data traffic across the network since more number of requests can be served from the
neighboring nodes. The cache consistency module is to maintain cache consistency, ie, ensuring
that each node caching the data item is aware of the data update at the source. Due to the limited
cache space, replacement policies decide which items should be evicted from the existing cache
to make way for new ones. In this paper we focus on cache placement and resolution.
Towards the goal of improving the performance in cooperative cache, we propose a novel cache
resolution scheme which uses a split table approach to resolve the cached data request which in
turn reduces latency and message overhead. More precisely, our algorithm splits the neighboring
nodes in to two sets based on communication range. The mobile devices keep the information
about the neighboring nodes in two tables, top level nearest node cache table and next level
nearest node cache table. The searching time and the request messages flooded in to the network
is reduced by this method. Moreover, due to simplicity, this approach does not have any
additional overhead in implementation. From the evaluation results we state that our proposal
yields significant improvements compared to the previous solutions.
The remainder of the paper is structured as follows. Section 2 investigates the problem of cache
resolution, Section 3 reviews the related works in cache resolution, Section 4 presents the system
model and assumptions made and detail the proposed cache resolution, Section 5 describes
simulation environment and metrics, section 6 gives the detailed analysis of results and section 7
concludes the paper.

2. CACHE RESOLUTION
Cache resolution in ad hoc networks constitutes an issue that several researchers are focusing on.
There are a few interesting studies on this subject. Two basic mechanisms used to discover cache
in ad hoc networks are broadcasting and cluster based approach. The first method is based on
request/response message exchanges in which a user interested in discovering a cache broadcasts
a discovery request containing information on the service requested and the node offering the data
sends a reply. In the second method, a node known as cluster head is used to temporarily register
the information about the cached data in the neighboring nodes. A node searching for data will
first look in to the cluster head node to locate the data and then directly send a request to the
corresponding node.
Although first method is the simpler version, it relays on flooding to broadcast the data request.
Flooding increases network contention and overhead when the network density is high. In
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contrast, cluster based approach has been best owed with the features of reduced overhead by
having a coordinator node which manages the cache resolution. The neighboring node which
possesses the data can be easily found out checking the lookup table maintained by the cluster
head. The disadvantage of this approach is that group maintenance is difficult due to the mobility
of nodes. The control node may get disconnected which causes excessive overhead [4].The
number of entries in the look up table increases when the network density is high. To maintain the
correct status of the network, these tables must be frequently updated. This involves information
exchange between the nodes which in turn increases the traffic overload in a dense network.
To circumvent these drawbacks we designed a cache resolution protocol among the mobile nodes.
Our approach is based on a distributed cache discovery algorithm and reduced cache table entries
to find the location of the requested data. An advantage of using this technique is that it reduces
network traffic as flooding is not used. This technique also avoids the drawback of group
maintenance by having a distributed approach. To reduce the number of entries in the look up
table we split the tables in two based on transmission range.

3. RELATED WORK
The main challenge in cooperative cache is to instantly find a cache containing the desired data.
To facilitate data discovery in cooperative cache, some schemes [2][3][5][6][7] based on
broadcasting and cluster based approach [9][12] is proposed in literature. These approaches differ
in how the data request is resolved to find the location of the data stored in the neighboring nodes.
In broadcast based approach the mobile nodes broadcast the request to find a node with the
required data. On the other hand in cluster based approach, some mobile nodes are selected as
information coordinators which deal with the task of finding the location of the required
information. We can also find some discovery mechanism [8] that doesn’t comes under this
category.Below, we describe some representative cache resolution strategies for cooperative
caching from the different groups mentioned above.
Aggregate caching scheme proposed in [5] tries to increase the data accessibility in an Internet
based mobile network. A broadcast based information search algorithm called simple search is
used to locate the required data item. Whenever a mobile nodes needs some data the request is
broadcasted to its adjacent nodes .Upon receiving the broadcast request, the adjacent nodes
replies to the request if it has already cached the data, otherwise the request is forwarded to its
neighbors until it is acknowledged by an access point or some other nodes which have the
requested data. Flooding is the technique used for broadcasting. This algorithm sets a hop limit
for the request packet to reduce the traffic in the network. A caching technique which uses cluster
based approach can be seen in [9], in which a coordinator node maintains the cluster cache state
information of different nodes within its cluster domain. If there is a local cache miss, the
coordinator node will find whether the data item is cached in other clients within its home cluster.
Another approach for data discover other than the mentioned schemes can be seen in [4] and
[10].In [4] a distributed group based approach in which each node maintains a group table and
self table. Whenever a data request comes in it first checks in its self table, if data is not found
checking is done in the group table which stores the data id and the node which posses the data.
Here the mobile nodes interchange information about where the data is present in the network.
In [10], a cache resolution technique based on adaptive flooding broadcast is used for searching
data in the network. According to this scheme a mobile node uses three schemes; adaptive
flooding, profile-based resolution and road side resolution. In adaptive flooding, a node uses
constrained flooding to search for items within the neighborhood. In profile-based resolution, a
node uses the past history of received requests. In road side resolution, forwarding nodes caching
the requested item, reply to the requests instead of forwarding them to the remote data source.
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4. SYSTEM MODEL
4.1. Basic Assumptions
The system environment is assumed to be a mobile ad hoc network which includes a collection
of nodes ni (i=1,2,……N) that communicate with each other in a coordinated manner and
Vi ⊆{n1,n2,…….nN} such that Vi is the set of neighboring nodes for a node ‘n’. Data is
originated in the server which is assumed to be in a fixed position. Mobile hosts are identified as
<Hostid, Name> for 1≤ i ≤N, where N is the density of the network which is decided by the user.
Each data item is uniquely identified by means of data item id Di for1≤ i≤ n where ‘n’ is the size
of the data base. Each node ni maintains a local cache of data items which it frequently accesses.
Whenever a node receives a request for the data item, local cache is checked. If the item is found
the request is satisfied from the local cache, otherwise the request is forwarded to the neighboring
nodes. The cache uses TTL based consistency.

4.2. Proposed Scheme
The objective of our cache resolution scheme is to reduce the overall access cost by reducing
searching time and data traffic. Most of the current cooperative caching technique uses the entire
list of one hop neighbors to find the cached data location. This will place unnecessary load on
each node because they need to search all the entries in the table or broad cast a request to all of
its neighbors to find the required data. The complexity of data discovery increases with the
number of nodes in the network. In this paper we consider a cache resolution technique which
uses a split table approach. In the given network each node Ni maintains a table ncti1 , whose
entries represent a set of top level neighbors nei1 ,which are located inside a wireless coverage of
WR1.The second table ncti2 represents a set of next level neighbors nei2 ,that are located in a
wireless range of WR2 .
The table entries in each node of an ad hoc network are maintained as follows. The nodes coming
under the wireless communication range WR1 and WR2 are taken and the distance between the
current position and the node position is calculated using the Euclidian distance [5].When a node
is farther away from the given node Ni the Euclidian distance will be more. The nodes are
arranged on the basis of distance, signifying the first entry to be closest one .The correctness of
the entries in the table is ensured by periodically updating the neighbor node information present
in the tables. The distance of each node from node Ni is determined by using the Euclidian
distance. For cache discovery, the top level neighbors are initially checked, and if we are not able
to retrieve the data, request packet is forwarded to the nodes in the range WR2. By this the
redundancies in flooding is reduced. The communication cost is also reduced by avoiding
multiple transmissions.

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4.3. Cache Admission and Replacement
In cooperative caching nodes share the cache contents of neighboring nodes to utilize the full
advantage of caching. The available cache replacement mechanisms for ad hoc network can be
categorized in to coordinated and uncoordinated depending on how replacement decision is made
[15]. In uncoordinated scheme the replacement decision is made by individual nodes. Coordinated
cache replacement considers the information present in neighboring nodes for replacement.
In order to improve the content diversity in the cooperative cache, our scheme does not cache any
data coming from the neighboring nodes. This increases the availability of information for the
user, as more data items are cached and also avoids additional request to the server. Previous
studies [16] have shown that the requests for smaller objects are more compared to bigger objects,
so the probability of achieving high hit rate is increased if we store more number of small objects.
In our proposed model we set a threshold value for the size of the data item admitted to cache.
The threshold value is set as 50% of the total cache size. Any object bigger than this threshold
isn’t brought in to cache. The cache replacement is based on a key based algorithm, which takes
in to account the inter arrival time of recent references, size and consistency for page
replacement.

5. SIMULATION SET-UP AND METHODOLOGY
We have developed a simulation model in JAVA. In our simulation, nodes are randomly placed in
an area of 800X800 m2. Each node is identified by a node id and a host name. The data server is
implemented as a fixed node in the simulation area. The data server contains all the data items
requested by the mobile nodes. The size of each data item is uniformly distributed between smin
and smax. The nodes in the network move randomly based on a random path. Simulation starts
with each node having empty caches and, then, iteratively caches the data item in each node’s
cache and subsequent request for data are served from the cache. The nodes that generate data
request are selected randomly and uniformly. The time interval between two consecutive
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queries generated from each node/client follows an exponential distribution with mean of
10sec. Each mobile node generates a single stream of read only queries. After a query is sent
out, the client does not generate new query until the pending query is served. The data
access pattern follows a Zipf distribution [14] with a skewness parameter ɵ as 0.8.

5.1. Simulation Parameters
Table.1 Simulation Parameters

Parameter
Simulation Time
Simulation Area
Database
Cache size
Smin
Smax
Nodes Density
Mobility model
Transmission Range

Value
3600sec
800X800 m2
1000 items
20 -70 % of total
database size
1
10
20-70
Random
waypoint
500m

Speed of the mobile 1-10
host
Pause time
200sec
Mean query generation 10s
time

5.2. Evaluation Metrics
Our evaluation metrics include message overhead and cache hit ratio. The evaluation of these
parameters is done by varying the number of nodes in each simulation run. The cache size is
taken as 50% of the total size of the database. The hit ratio is defined as the percentage of
requests that can be served from previously cached data. Message overhead is the overhead
messages needed to manage the cache discovery process in cooperative cache.

6. EXPERIMENTS AND RESULTS
We compared the performance of our method with broadcasting for different node densities. Fig
2-3 shows the different performance comparison of two schemes, cooperative caching with split
table approach (ccs) and cooperative caching with broadcasting (ccb), as a function of different
node densities. Fig 2 shows the message overhead for both schemes under different node
densities. The figure show that ccs outperforms ccb at all node densities. As the node density
increases, the difference become more significant which implies that ccs can benefit from larger
node density. Fig 3 depicts the comparison of cache hit ratio for different node densities. From the
figure we can see that the cache hit ratio for ccs and ccb perform quite closely. The relative
performance of cache hit ratio remains relatively stable for higher network densities.

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Fig. 2. Message overhead for different node densities.

Fig .3 Cache Hit Ratio at different node densities

7. CONCLUSION
In this paper we addressed the problem of cache resolution technique for peer to peer cooperative
caching in ad hoc networks. The objective of our problem was to minimize the number of
messages flooded in to the network, which in turn reduces the communication cost and bandwidth
utilization. We designed a data discovery process based on split table approach. We evaluated the
performance our algorithm with broadcasting approach through extensive simulations.
Experimental results show that the proposed cache resolution algorithm can significantly reduce
the message overhead when compared to broadcasting.

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