Topology Evolution in P2P Distributed Networks

Transcription

1 Topology Evolution in P2P Distributed Networks German Sakaryan Computer Science Dept. University of Rostock 1851 Rostock, Germany Herwig Unger Computer Science Dept. University of Rostock 1851 Rostock, Germany ABSTRACT P2P-networks become more and more interesting not only because of the emergence of file sharing systems like Gnutella. Such decentralized systems have a lot of advantages compared to centralized client-server systems. However, information search is still a central problem in such P2P web communities, since the used complete search with message chains may cause high search response times. In this paper we emphasize that these search times can be significantly influenced by the message forward algorithm and by the topology of the community, which can be easily changed by respective, local executed algorithms. For doing so, content and traffic oriented methods were proposed. Their efficiency has been proved through a simulation and the respective results. KEY WORDS P2P, Information Search, WEB Communities the algorithms of Content Oriented Information Search as well as algorithms to change the topology of the community based on Content and Traffic Oriented Restructuring were presented. We demonstrate the impact of the proposed algorithms on the information search and proof that it becomes more effective. Finally, it was shown that traffic problems caused by overloaded nodes may be avoided. For this goal, in this paper static communities are considered, i.e. the number of nodes in the community is a constant one. Therefore, the paper is structured as following. The below section 2 gives a general definition of the community system, describes our model of the information used, and presents model of the community s node. Section 3 introduces algorithm of the information search, and approaches to topology changes based on content and traffic restructuring. In the Section 4 we present used simulation tool and discuss the achieved empirical results. 1 Introduction Significant research efforts have recently been invested in the development of peer-to-peer (P2P) computing systems, not only due to the high popularity of file sharing systems like Gnutella [1], Napster [2], Freenet [3]. Due to its flexibility, reliability and adaptivity P2P solutions can overcome a lot of disadvantages known from client-server systems, and can fit to dynamically changing Internet environment [4]. Also, P2P communities open new opportunities for Internet marketing and business oriented approaches [5], [6]. However, the efficient management of distributed P2P networks and especially the organization of an efficient search result in a lot of new problems and tasks to be solved. The big share of Internet users still using dial-up modems, which makes a lot of connections slow and unreliable. They can hardly efficiently handle incoming information requests, analyze them and proceed. In addition to dynamically changeable structure of such Web communities (nodes can join and leave community), all this leads to killed requests and increasing of search time. We intend to show that the topology of the community plays an important role for community operations, since it significantly influences on the time needed for information search and on traffic in the community. In the given paper a conceptual approach for managing the topology of a P2P community, 2 P2P Web Community 2.1 Structure of the Community P2P Communities are groups of objects (e.g. computers) sharing a common interest. In such a manner, at least a communication between the community members is possible. Communities are created as logical structure over the Internet and includes different members - nodes. Mostly the community topology is defined by the neighborhood relations kept in every node s local warehouse. The topology can be modeled as a directed graph [7], which can be characterized by diameter, node s degree, and connectivity. Different models can be used to build and to analyze community topology: random graphs, Erdos and Renyi [8] model, edge-reassigning Small-World Network by Watts and Strogatz [9] and others. For computation purposes each node in the community has a modular structure, additionally (see. Fig 1) containing the following components: Node s profile (PR). Contains basic data about the node: informational profile (see Sec.(2.2)), address of the node (IP), and bandwidth. Queue Module (QM). Includes buffer to store incoming messages.

2 7 Processing module (PM). Includes functions and procedures for community operations like: information search, communication with user, restructuring, traffic handling and others. Warehouse module (WM). Contains list of neighbors, their informational profiles and bandwidth. Informational Module (IM). Contains information which is stored locally on the given node and shared between other community members. User Profile. Is used to simulate activity of the node s user during community simulation. W M C I M C U s e r P r o f i l e Q M I M U S ER N O D E P R P M WM Figure 1. Community Node Model. W M C I M C Normally, message chain mechanisms are used for communication in distributed P2P networks. A message chain circulates between community nodes and can be modified. It is born on starting node to fulfill particular task. Message has a limited size plan including the addresses of the visited nodes, some parts of their informational profiles, bandwidth, and time spent there. In addition to plan, message may keep extra data like starting time, hop counter, priority etc. Important to note that anonymity of the nodes can be maintained by reducing size of the message chain s plan. Messages can be distinguished by the task they are generated for. Information messages (IMC) are used for search, warning messages (WMC) used to inform others about critical situations, clustering messages (CMC) for participating in clustering, and propagation message chains (PMC) to propagate new information. 2.2 Information in the Community All information kept in the community is limited to the community interests and can be categorized for limited number of categories and represented as informational hierarchy. Here only a short summary of what is really needed for this paper is given. Each category of the information can be structured and described using the extensible Markup Language (XML) tags [1]. The tags are organized in hierarchical manner(tree). At the top of the tree there is a general category of information. Subcategories are represented on the lower levels of the tree. All information in the community can be presented as set: (1) where, j =1..n -set of information pieces which belong to the same category, and n- total number of tags. Information is represented by following triplet:! #"$" (2) where T- is sequences of tags (XML) in the information hierarchy, V- is the real informational content and P(T,V) - is the probability corresponding to how often this information is requested by an user. Probability is used to create an adequate simulation model. Empirical results obtained from the analysis of the request structure in a Gnutella network [11], show the strong correspondence between information and its popularity. In real communities these probabilities can be only estimated by analyzing information requests within representative subgroup of users/nodes. Any information is described by profile, which includes position in the hierarchy and probability: '&() *" +),!)"$" Informational profile.- of profiles of the informations kept on the given node. /- 1&() (3) / of the node / is defined as a set / "$" &(9:;"= >@?,8 /ACBEDCF GIH For the creation of the informational profile an information with J 9K"ML1NO PRQ S is considered. 3 Main Algorithms 3.1 Content Oriented Information Search To locate information in accordance with user s request a message chain travels over the community to find at least one node with the required data. The hop counter is used to limit the life time of the message and prevent extra meaningless network load. At the moment of the message generation the counter is initialized by value bigger than zero and is decremented every hop. As soon as a target information is found the message is sent back to its starting node informing it about the IP address of the found node. The path of the message is stored in the plan. Two main concepts for forwarding message over the community are applicable: Random forwarding. The message is forwarded randomly. For big communities the probability of locating the required information by one message is very low. The only solution to be done is to broadcast many message chains like in Napster or Gnutella. (4)

3 Content oriented forwarding. The basic idea behind content oriented forwarding is that the profile of the requested information and the informational profiles of the neighbor nodes #- are considered. The algorithm forwards the message to the neighbor with the most similar informational profile compared to requested info. The neighbor is chosen as the best node if: - " - As it will be shown later the community topology is evolving and nodes with similar profiles organize closer connected subcommunities. The system uses not only the local warehouse to choose the best node, but also analyzes the plans of the messages pending in the incoming queue. These messages organize a kind of virtual warehouse. 3.2 Principals of Topology Evolution The topology (link structure) of the community can be modeled as a directed graph, which has a number of specific features to be considered: Changing with time (evolution). This trend is caused by mainly two reasons. First, because of the uncoordinated and unpredictable addition and removal of nodes. These processes can be modeled by Poisson distribution and an exponential distribution accordingly [12]. Second, algorithms may run and self-organize a community so that topology can be changed (cluster building, content oriented structure creation etc.). 9IP (5) Due to lack of global knowledge, only local knowledge about community graph can be used for any structuring algorithms. Structuring algorithms should consider the distribution of the information between community nodes. The effectiveness and time taken for information search highly depends on the topology of the community and the actual traffic situations. In real networks messages on overloaded nodes are lost, that requires extra time to locate information. In order to target these two issues two restructuring approaches are proposed: Content oriented restructuring. Links between nodes shall be changed in order to organize local, stronger connected subcommunities of nodes with similar interests. Content restructuring deals with two issues: internal and external restructuring. First organizes groups of machines with the very similar information interests. External restructuring targets to organize links between different groups/ subcommunities. Traffic oriented restructuring. This kind of restructuring is caused by local traffic problems and leads to the creation of alternative paths around the overloaded nodes. Both approaches change the topology of the community dynamically, thus the changeable part of the P2P communities is ideally organized. Content restructuring uses knowledge about nodes visited by traveling messages, in such a way extra traffic for only restructuring purposes is avoided. These changes are faster than global topology changes caused by the dynamics of the P2P Web communities. The creation of the content oriented topology may lead to a traffic concentration around nodes with the most requested information. In this case, the traffic restructuring is required. And vice versa only traffic restructuring partially breaks content oriented topology and may lead to increasing search time. That is why combination of the two approaches is mostly indicated Content Oriented Restructuring The important task of this algorithm is to organize subgroups of nodes with similar informational interests and create intergroup connections. Accordingly, the warehouse of a node is divided in two parts: internal (neighbors with similar info) and external (neighbors from different groups). The executed locally algorithm should have following features: Information about other nodes in the community is taken from the plans of the traveling search messages generated by given node. The algorithm starts if either searching time is bigger than it was expected or the number of hops made bigger then it should be. Algorithm analyses the plan of the message chain, extracts candidates for neighbors and updates the content of the internal and external part of the node s warehouse. The distance between candidate and given node is calculated using formula: / /- - " /- /- (6) - where /- - info profile of the given node, and #- is a neighbors informational profile. The internal part of the node s warehouse is updated by links to new nodes with similar informational profile to the considered one. The external part by nodes with lowest similarity Traffic Oriented Restructuring Restructuring based on traffic leads to dynamical but local changes around the overloaded node. Every node in the community runs the same algorithm, which allows to detect critical situations and informs pre-neighbors about it. The following steps are executed: 1. Check, if the number of messages in the incoming queue is bigger than a critical value.

4 2. If yes, generate a warning messages (WMC) and send them out to pre- neighbors. The data carried by the WMC include the expected handling time of the pending message, which was sent by preneighbor to overloaded node. The data also include list of outgoing neighbors of the overloaded node. The list is used for the creation of roundabout ways. Outgoing neighbors are chosen randomly from the node s internal warehouse, so the pre-neighbor may reinstall its link to the new node with the similar information (as overloaded). In that way, content oriented structure of the community is left almost unchanged. When a node receives a WMC it deletes the link to the overloaded node with some probability and add a new link to the internal and external warehouses. In both cases the best nodes from information point of view are chosen. New neighbors are picked up from received list of neighbors. The probability of the link deletion can be either dependable on an expected handling time or to be a fixed parameter (empirically obtained). The probability of deletion prevents overloaded node from being isolated. Traffic restructuring helps to spread local traffic more equally. 4 Simulating Community 4.1 Concept and Principals of Simulation The main task of the simulation is to investigate how the proposed algorithms of content search, content and traffic restructuring influence on the information search in P2P communities. The developed simulation tool includes the following components Fig (2): Virtual Network Generator and Statistic Collector (VNGSC). (see Fig (2))VNGSC generates a community consisting of the nodes with assigned informational profiles and initial topology and collects statistic about the community operations. Simulation Model of the node. The model simulates the behavior of community nodes and generates information requests (through User Profile module). Message Chain Delivery Mechanism (MCDM). The MCDM is used for message chain delivery and routing (analog to physical infrastructure). N O D E i S e n d R e s e a r c h e r s V i r t u a l N e t w o r k G e n e r a t o r a n d S t a t i s t i c C ol l e c t o r ( V N G S C ) P 2 P ( v i r t u a l ) R e c e i v e R e c e i v e Me s s a g e C h a i n D e l i v e r y Me c h a n i s m ( MC D M) ( r o u t i n g ) Figure 2. Simulation Tool. NO DE k S e n d IMC and WMC types messages were used. The number of messages generated within one simulation run was limited to the number of nodes in the community. However, these limitations are close to the reality because a user mostly places another information request when it has processed the reply from the previous one. 4.2 Experimental Results To get reliable results the size of the community should be large enough to create several reasonably big subgroups (about 1 to 15 groups with 1 participants). On the other hand, simulating large communities becomes a problem, since simulation times will rapidly increase. Considering both arguments, a community size of 248 nodes was chosen. In such a manner, 2 warehouse entries equally divided between internal and external parts generate a graph close to real conditions. The incoming queue size is equal to 2 with a critical level (more messages than critical level node is overloaded) of 15. All messages may make 2 hops. The plan size was set to 2. These values were chosen to prevent messages to come back to the starting node too often and to decrease the time of the restructuring. During the experiments all combinations of different approaches (table 1) were investigated for the same initial community topology. All information in the simulated community is divided on to 4 subgroups A...D [11]. Information of the group A is requested 4 times, B-3, C-2 and D-1. Two level information hierarchy was considered. The information requests are generated randomly with given probability. The initial topology of the community was created as a random graph, which may not be strongly connected. We assume that, all nodes have an identical characteristics (warehouse size, incoming queue size, and identical software). All messages in the system have the same plan length and initial value of the hop counter. For simulation Algorithm Random Forward Content Forward No Restructuring Content Restructuring Traffic Restructuring Both Restructurings Table 1 Abbreviation RF CF NR CR TR CR+TR

5 SUM OF HOPS RF,NR RF,CR RF,TR RF,CR+TR Figure 3. Average hops per positive result. RF,NR RF,CR RF,TR RF,CR+TR Figure 6. Killed vs. generated requests. 31 NUMBER OF MESSAGES RF, TR CF, TR S IMC WMC SUM OF HOPS Figure 4. Overload in the Community. Figure 7. Content Oriented Forwarding Hops/Result RF,NR RF,CR RF,TR RF,CR+TR Figure 5. Positive results vs. generated requests. Figure 8. Content Oriented Forwarding Killed/Requests.

6 Each simulation run consists of the 248 control cycles, so that messages have at least theoretical possibility to visit all nodes. When node receives the control it performs actions according to the simulated combination. At the end of one run statistics is collected. The next run starts with topology achieved during the last run. Each combination was tested sequentially 4 runs and average statistic data was calculated. Different methods were compared using following parameters: 1. Ratio of total number of hops vs. number of positive results (messages which found requested info). 2. Ratio of positive results vs. number of generated requests. Value of 1 means all requests are satisfied. 3. Ratio of killed messages vs. number of generated requests. The conducted experiments demonstrate advantages of the content forwarding over random one. The average number of hops is at least 1 times less (see Fig(3)). The ratio of successful results is hereby 9 compared to 4 of the random oriented search (see Fig. (5)). The number of killed messages is extremely low vs. those in random search systems (see Fig. (6)). Experiments have shown that the use of traffic restructuring considerably decreases number of killed messages (lost users requests) (see Fig. (6, 8)) in both combinations with random and content forwarding. The additional traffic (see Fig.(4)) created by warning messages is estimated for combination RF+TR -is about 14, for RF+CR+TR- 47, for CR+TR- 9, and for CF+CR+TR - is only 3 more messages. The application of content restructuring makes no sense for systems which do not use content structure of the community (like random forwarding search). In the content search systems it will create content oriented topology, therefore information is located faster, number of hops and number of killed messages are decreased (see Fig. (7, 8)). Experiments proved our expectations that due to mutual influences the use of content forward, content and traffic restructuring gives the best results. 5 Conclusion and Outlook. In given paper several approaches to improve the search efficiency in P2P communities were presented. The main principals of a content oriented search as well as the principals of a topology evolution were described. Two new algorithms which care about content oriented structure of the community and traffic were introduced. With the presented community simulation concept and the respective simulation tool some empirical experiments were done. The results proved our expectations and demonstrated effectiveness of the proposed methods. Experiments showed that the combination of all three approaches would considerably improve average system performance. In our future work it is intended to investigate the dynamics of the community structure more detailed, especially with respect to the steady changing number of participating nodes. In addition, formation and management of subcommunities must be discussed, since the needed quality of service cannot be guaranteed in too large communities. References [1] Gnutella. June 22. [2] Napster [3] Ian Clarke, Oskar Sandberg, Brandon Wiley, and Theodore W. Hong. Freenet: A distributed anonymous information storage and retrieval system. Lecture Notes in Computer Science, 29:46 59, 2. [4] Herwig Unger and Markus Wulff. Cluster-building in P2P-community networks. In Parallel and Distributed Computing and Systems (PDCS 22), pages , Cambridge, USA, 22. [5] Rudiger Grimm and Jurgen Nutzel. A friendly peerto-peer file sharing system with profit but without copy protection. In Second International Workshop, IICS 22, pages , Kuhlungsborn, Germany, June 22. [6] Ulrike Lechner. Peer-to-peer beyond file sharing. In Second International Workshop, IICS 22, pages , Kuhlungsborn, Germany, June 22. [7] Narsingh Deo and Pankai Gupta. World Wide Web: A graph-theoretic perspective. Technical Report CS- TR-1-1, School of Computer Science, University of Central Florida, Orlando, FL 32816, USA, March 21. [8] P. Erdos and A. Renyi. On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Science, 5:17 61, 196. [9] D. Watts and S. Strogatz. Collective dynamics of small-world networks. Nature, 393:44 442, June [1] A. Ceponkus and F. Hoodbhoy. Applied XML. Wiley Computer Publishing USA, USA, [11] Nesreen Zadah. Searching in P2P-network communities. Technical report, Department of Computer Science, Rostock University, September 22. [12] Gopal Pandurangan, Prabhakar Raghavan, and Eli Upfal. Building low-diameter P2P networks. In 42th IEEE Symp. on Foundations of Computer Science, pages 56 64, Las Vegas, USA, 21.