Author Topic: Two Levels TTL for Unstructured P2P Network using Adaptive Probabilistic Search  (Read 4051 times)

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Author : Yash Pal Singh, Rakesh Rathi, Jyoti Gajrani, Vinesh Jain
International Journal of Scientific & Engineering Research Volume 3, Issue 1, January-2012
ISSN 2229-5518
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Abstract— P2P networks are playing an important role in current scenario of unstructured networks. P2P network supports various applications and taking the advantage over the centralize search system .Centralize search systems suffer from the problems of single point of failure, low availability, denial of service attacks. Searching of the required data is a vital issue in the P2P network. Many methods have been implemented for searching in P2P network such as Flooding, Random Walk, Expanding Ring or Iterative deepening, K-Walker Random Walk, Two Level K Walker Random Walk, etc. These methods are based on property of randomness in the network. Some of these generate large traffic while others take long searching time. A probabilistic approach with Two Level K Walker Random Walk for searching has been implemented in this paper and comparative study has been done with other algorithms.

Index Terms— peer to peer, APS, random walk, K-walker, dynamic search, peersim, probabilistic, flooding. 

1   INTRODUCTION                                                                     
AP2P network is a collection of distributed, heterogeneous, autonomous and highly dynamic peers. Participant peer shares a part of its own resources such as processing power, storage capacity software and files. P2Pnetworks are dynamic in nature. Various types   of P2P networks are the Purely Decentralized Systems, Partially Centralized Systems, and Hybrid Decentralized Systems. According to network structure P2P network is classified as Unstructured, Structured and Loosely Structured based on their data location with respect to overlay topology. In the first case data content are totally unrelated to overlay topology. In Structured P2Pnetwork data contents are precisely defined with respect to overlay topology. In this type of network each node has the idea about data content so searching can be done easily. In last one searching can be done on the basis of routing hints. Structured P2Pnetworks are not suitable for highly transient node population where node can leave and join any time. In this paper a probabilistic approach with two levels TTL has been implemented for searching in unstructured P2Pnetwork. In this case we maintain a database and each node has the same probability of its neighbors in the database. First time we perform K-Walker Random walk because every neighbor has the same probability. After termination of the search we increase the probability of the neighbor by some amount on successful path and decrease the probability on unsuccessful path. Next time when any node wants to make query, it will choose the node with highest probability from the database and send a query message to this node. This continues until the desired content is found or TTL expires. In the case of Two Level TTL, those nodes where the search is unsuccessful a new TTL1 will be initialize which will be less than the last TTL, and at every unsuccessful node the query message will be exploded in to K number of threads. This process will be continuing like K Walker Random Walk until the desired content found or TTL1 expires. We proposed a new search algorithm which takes the advantage of these two searching techniques [1]. Implementation of this algorithm and comparative study has been done with other algorithms in the paper.

Various search protocols have been implemented for Unstructured P2Pnetwork. Basic searching techniques are  blind search and  knowledge base search. Flooding and Random Walk protocols use blind search and Adaptive probabilistic search uses knowledge based search. Mostly protocols are working for file sharing applications on the Internet. Most common examples are Napster, Gnutella, Kazaa and BitTorrent. Gnutella is based on flooding which is used for file sharing application. BitTorrent is also used for. Flooding[5]: In the case of flooding each querying node sends query message to its entire neighborhood. These neighbors also forward this query message to their corresponding neighbors until search is successful or TTL expires. If desire content is very far from querying node then number of message generated for this query will be very large. Iterative Deepening [5]: The idea of iterative deepening is taken from artificial intelligence and used in P2P searching, where the querying node issues BFS searches (in sequence) with increasing depth limits. It terminates the query either when maximum depth limit (D) has reached or result is obtained. Same sequence of depth limits is used by all nodes and same time period W between consecutive BFS searches. Local Indices [5]: In this technique a system wide policy specifies the depths at which the query should be processed. All nodes at depths not listed in the policy simply forward the query to the next depth. Routing indices [5]: Routing indices guide the entire search process like intelligent search. Intelligent search uses information about past queries which have answered by neighbors, While Routing indices stores information about the documents topic and also the number of documents stored in its neighbors. It concentrates on content queries, queries based on the file content rather than file name or file identifier. Dynamic Search [10]: It maintains user define threshold value. If the number of hop count is less than threshold, flooding will be performed otherwise K walker random walk will be perform. This will generate lesser amount of traffic than flooding. K-walker random walk and related schemes [5]: In the standard random walk algorithm, query message( also called walker) is forwarded to one randomly selected neighbor which again randomly chooses one of its neighbors and forwards the query message to selected neighbor and procedure continues until the data is found. One walker is used in standard random walk algorithm and this will reduce the message overhead greatly but causes longer searching delay. In the k-walker random walk algorithm, k walkers are deployed by the querying node which means querying node(source node)forwards k copies of the original message to k neighbors (randomly selected) and at the intermediate node it will forward single copy of the message to one randomly selected neighbor until the search is successful or TTL expires. Each walker takes its own random walk. Each walker talks with the querying node periodically to decide whether it should terminate or not. Soft states are used to forward different walkers for the same query to different neighbors. This algorithm tries to reduce the routing delay. On an average the total number of nodes reached by k random walkers in H hops is the same as the number of nodes reached by one walker in kH hops and this causes routing delay to be k times smaller. A similar scheme is the two-level random walk [7]. In this scheme in level 1, the querying node deploys k1 random walkers from the querying node with the TTL1 and perform random walk at intermediate nodes. At the nodes where TTL1 expires and search is unsuccessful level2 will start, each walker forges k2 walkers with the TTL2. Query is processed by all walkers in the path. For same number of walkers (k=k1+k2), this scheme has longer searching delays than the k-walker random walk but generates less duplicate messages. Another similar approach called “modified random BFS”, where query is forwarded only to a randomly selected subset of neighbors and when query message is received , each neighbor forwards the query to a randomly selected subset of its neighbors  (excluding the querying node). Until the query stop condition is satisfied, same method continues. This approach may visits more nodes and has a higher query success rate than the k-walker random walk. Adaptive Probabilistic Search [5][6]:  It assumes that objects and their copies in the network follows a replication distribution for storage. The number of query requests for each object follows a query distribution. This process does not affect object placement and the P2P overlay topology. APS is based on k-walker random walk and is probabilistic forwarding. The querying node deploys k walkers simultaneously and on receiving the query, each node looks up its local storage for the desired object. The walker stops successfully once the object is found otherwise it continues. Query is forwarded to the best neighbor that has the highest probability value. The probability values are computed based on the results of the past queries and are also updated based on the result of the current query. This query processing continues until all k walkers terminate either success or fail (the TTL limit is reached).


Performance of APS algorithm can be increase by using Two-Level Random Walk instead of One-Level Random Walk (K Walker Random Walk). In the case of Two Level Random Walk we generate K1 threads which will be less than K which we have used in K Walker Random Walk. At the edge nodes where TTL1 expires and the search is unsuccessful then second level will start and these K1 threads will exploit in K2 threads subsequently a new TTL2 will be initialized which will be less than TTL1. In this case we are generating fewer threads so chances of collision will decrease than other searching algorithms. Algorithm for the proposed technique is as follows:

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