Modifying the Overlay Network of Freenet-style Peer-to-Peer Systems after Successful Request Queries

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1 Modifying the Overlay Network of Freenet-style Peer-to-Peer Systems after Successful Request Queries Jens Mache, David Ely, Melanie Gilbert, Jason Gimba, Thierry Lopez and Matthew Wilkinson Lewis & Clark College Portland, OR 97219, USA {jmache, dely, mgilbert, gimba, lopez, Abstract In most peer-to-peer systems, edge resources self-organize into overlay networks. At the core of Freenet-style peer-to-peer systems are insert and request algorithms that dynamically change the overlay network and replicate files on demand. We run simulations to test how effective these algorithms are at improving the performance of subsequent queries. Motivated by the observation that the performance of the original Freenet algorithms improves less rapidly with a (more realistic) ratio of 99 requests to 1 insert than with an equal number of requests and insert, we design and test a wide range of new request algorithms that pass word of success to neighboring nodes and add additional edges to the overlay network. Our results show that performance is sensitive to (1) where to add edges and (2) how many edges to add. In comparison to the original request algorithm, our best new algorithm is able to reduce average request pathlength by up to a factor of Introduction As the Internet continues to experience rapid growth and ever increasing numbers of people request particular pieces of information, it becomes exceedingly difficult to fulfill very popular requests (cf. Slashdot effect). Yet, to fulfill all the requests for one piece of information, there is an alternative to one powerful server (or server cluster): to rely on copies of the piece of information distributed across the network, so that many computers can fulfill these requests. This scalable and decentralized approach is an example of peer-to-peer networking. In most peer-to-peer systems, edge resources self-organize into overlay networks. We are studying the performance of Freenet-style peerto-peer systems in which every participating computer (node) automatically helps to fulfill queries by forwarding queries intelligently to one other node. At the core of Freenet-style peer-to-peer systems are insert and request algorithms that dynamically change the overlay network and replicate files on demand. We ran simulations to test how effective various request algorithms are in improving the performance of subsequent queries. Motivated by the observation that the performance of the original Freenet algorithms improves less rapidly with a (more realistic) ratio of 99 requests to 1 insert than with an equal number of requests and insert, we design and test a wide range of new request algorithms that further reward the fulfillers by passing word of success to neighboring nodes and adding additional edges to the overlay network. Our results are summarized below. It is better to spread the news deep rather than broad. It is better to start spreading the news at the requester (towards other regions of the network) rather than at the fulfiller. It is better to spread the news to a random neighbor rather than to the closest neighbor (the node in the reference table with the key closest to the key of the requested document). Farthest is worst. Adding too many additional references can backfire, causing the reference table to become saturated with recently requested files due to its finite storage size. The remainder of this paper is organized as follows: In Section 2, we provide background information and survey related work. In Section 3, we describe our experimental method. The original Freenet algorithms and their performance are discussed in Section 4. We present and test a wide range of new request algorithms that add additional edges to the overlay network after successful requests in Section 5. After a discussion in Section 6, we conclude in Section /4 $17. (C) 24 IEEE 1

2 A B C D References E F G H I B Documents References J 52 - K L M C Documents References 66 - N O P Q R 93 - D Documents References S T 88 - U 94 - V 97 - W 99 - X Documents Figure 1. How a Freenet-style peer-to-peer system forwards a request (originating at node A) for the document with key Background and related work 2.1. Freenet-style peer-to-peer systems In Freenet-style systems [3, 4], every node helps to fulfill requests by forwarding queries (that it cannot fulfill itself) to the node in its reference table with the key closest to the key of the requested document. For a small example, let s consider the reference tables of Fig. 1 and a request at node A for the document with key 91. Node A forwards the request to node B because 522 is closer to 91 than 111, 115, 118, 129 or 363 are. Node B forwards to node C. Node C forwards to node D that has document 91 stored. In order to maintain the privacy of the requester, the document is sent back the same way the request was forwarded. Moreover, the overlay network self-organizes in the following way: the intermediary nodes (C and B) and the requester (node A) add the entry 91 D to their reference table and a copy of document 91 to their datastore. Essential to good performance in a Freenet-style network is specialization, meaning that a node specializes in data within a dynamic and non-deterministic range. The network s insert and request algorithms are designed so that nodes specialize. However, specialization by itself is not enough. A node must also know about data that is different from its own area of concentration thereby reducing the pathlength of request queries. These shortcut or bridge connections allow requests to traverse areas of similar concentrations. Freenet s insert and request algorithms create shortcuts dynamically. The occurrence of both shortcuts and specialization in a network results in short pathlengths, a property of smallworld networks [16]. This term was coined when describing the social phenomena that most people are linked by short chains of acquaintances (popularly known as six degrees of separation ) Related work Freenet is very different from Napster [11] and Gnutella [5]. Napster s central broker was a single point of failure and could easily be attacked or shut down. Whereas Freenet operates depth-first, Gnutella operates breadth-first; for a Gnutella request, all neighboring nodes are contacted, which may in turn contact all their neighboring nodes. This can cause thousands of messages per request, making scalability a concern. (Kazaa [7] alleviates this problem with more powerful computers becoming search hubs.) Other peer-to-peer systems that operate depth-first are Chord [15], CAN [12], Pastry [14] and Tapestry/Oceanstore [18, 8, 13]. However, these systems can be viewed as providing a distributed hash table, where nodes have fixed identities and data is placed deterministically. As a consequence, items can be located within a bounded number of routing hops. On the other hand, securing against attack, load balancing and exploiting proximity are more difficult. Whereas modifications to the Gnutella and Pastry algorithms are described in [9] and [2], related work on modified algorithms for Freenet-style systems seems to be rare. Zhang et al. [17] tested different datastore replacement schemes, but did not modify the request algorithm. 3. Experimental method In this paper, our main concern is performance. Freenetstyle peer-to-peer systems dynamically change the overlay network and replicate files on demand. We ran simulations to test how effective the request and insert algorithms are in improving the performance of subsequent queries (actions). Our measure of merit is pathlength. Pathlength is the number of hops from requester to fulfiller. Short pathlength is important for (1) less overall bandwidth consumption, and (2) reduced probability of bad performance due to slow or unreliable links /4 $17. (C) 24 IEEE 2

3 For our experiments, we use the Aurora simulator [1]. This simulator was designed to model the specifications of Freenet as described in [3]. In the results reported, we simulate a network of 1, nodes that are initially connected in a very unfavorable, regular topology with four neighbors per node. The network is evolved over a specified number of actions. are either requests or inserts. They are interspersed randomly, given a request-to-insert ratio. Random keys are inserted at random nodes. Randomly-chosen existing keys are requested at random nodes. After every actions, the performance of the overlay network is measured using a set of special probe requests (that do not modify the network). Unless otherwise specified, each node has a datastore with a capacity of 5 documents and a routing table with up to 2 references. Hops-to-live (HTL) is 2 for inserts and 2 for requests (unless they are probe requests). Results are averaged over 1 simulation runs. The same 1 seeds are used to test different algorithms. We modified the Aurora simulator [1, 6] in the following way: First, for requests, keys are drawn from an exponential distribution. That way, some documents are far more popular than others. Second, each node updates the timestamps of the references that it is using. That way, references are replaced in least recently used fashion. 4. Original algorithms and their performance The original request algorithm for Freenet can be described by the following pseudocode. request(key, Hops_to_Live, Passer) if (a document in my store matches Key) pass Document back to Passer return SUCCESS endif if (Hops_to_Live equals ) return EXPIRED endif decrement Hops_to_Live while(references left in my routing table) in routing table find node with closest key that has not previously been tried answer=request(key, &Hops_to_Live, me) if(answer==expired) return EXPIRED endif if(answer==success) add Document to my store //caching add reference pointing to Fulfiller pass Document back to Passer return SUCCESS endif endwhile return BACKTRACK endrequest The insert algorithm is very similar: each node passes the document on to the node in its routing table whose key is closest to the key of the document being inserted. Then, new references are added to the routing tables. While requests terminate upon finding a copy of the document, inserts typically fulfill their hops-to-live. An insert will only stop early when attempting to insert data to a node that already has it. This is called a collision. As more requests and inserts are processed, new references are added and the routing within the network becomes more efficient. For the example of Fig. 1, nodes A, B and C added the reference 91 D. A subsequent request for 9 would find a one-hop route to D from A, B and C. As reported in [6], over time the median (and average) pathlength improves. However, the experiments in [6] assume an equal number of inserts to requests in the network. This scenario is not very realistic: in most situations (including the Internet in general), far more users request files than contribute files. Our results show that pathlength improves much slower with a more realistic ratio of 99 requests to 1 insert than with an equal number of requests and inserts (see Fig. 2). Looking closer at the simulation statistics, we notice that (1) many requests failed, and (2) successful requests added only a few new references. For simulations involving 5% requests and 5% inserts (5:5) at 5, actions, 6.9% of request-queries failed. On average, successful request-queries are adding only about 4.5 new references. This is in comparison to 2 new references added by a non-collision insert. These observations were our motivation to design and test new request algorithms. In [1], our main contribution was the design and test of strategies to improve failed (expired) request-queries by taking advantage of the work already done by the standard request process. In this paper, we focus on improving successful requestqueries by adding additional edges to the overlay network. 5. New algorithms and their performance Motivated by our observation that Freenet s original request algorithm is not very effective in changing the overlay network (because on average only a small number of new references are added per successful request, see Section 4), we set out to design and test new request algorithms. Our idea is to further reward fulfillers by adding additional references about the fulfiller and the requested document, thereby spreading the good news of a successfully fulfilled request to other areas of the network. The fulfiller is rewarded because its community of similar nodes, a natural result from the specialization Freenet promotes, will gain a bridge to another community of nodes. Hence, future searches for similar files will be faster and more efficient. In the example of Fig. 1, the reference we would like to spread further is 91 D. 91 is the key of the document that was requested and D is the fulfiller. If this information is known more widely in the network, the performance of subsequent requests potentially improves. Whereas it is rather obvious what the additional references should be about (the fulfiller and the requested key), we are faced with the question of where such references should be added, and in what quantity. To answer these questions and to improve Freenet, we are designing and testing a broad range of new algorithms, fol /4 $17. (C) 24 IEEE 3

4 :1 Requests-To-Inserts 5:5 Requests-To-Inserts 35 3 Median Pathlength Figure 2. Pathlength decreases as the network gets used. However, the ratio of requests-to-inserts makes a big difference. Requester Fulfiller Requester Fulfiller Requester Fulfiller Requester Figure 3. Breadth at fulfiller (left) and breadth at requester (right). Fulfiller Figure 4. Depth at fulfiller (right) and depth at requester (left). lowing four main approaches: breadth at requester, breadth at fulfiller, depth at requester and depth at fulfiller. In each main approach, we have the subchoices of random, closest, and farthest Breadth The new request algorithms following the breadth approach add the additional references only to nodes that are one hop away, hence behaving in a breadth-first fashion. We can specify how these neighbors are chosen from the reference table, whether at random, the closest reference to the requested key or the farthest. As shown in Fig. 3, the breadth approach can either start at the fulfiller or at the requester. The breadth fulfiller algorithm announces the news breadth-first from the fulfiller, whereas breadth requester starts at the requester. Fig. 5 and Fig. 6 show our results. Within either the breadth fulfiller or the breadth requester approach, the new request al- gorithm that chooses neighbors at random performs best. The best breadth algorithms overall is breadth requester random, see Fig Depth The new request algorithms following the depth approach add the additional references in a depth-first fashion. As shown in Fig. 4, the depth approach can either start at the fulfiller or at the requester. Again, we can specify how the next node is chosen from the reference table, whether at random, the closest reference to the requested key or the farthest. Fig. 7 and Fig. 8 show our results. Within either the depth fulfiller or the depth requester approach, the new request algorithm that chooses neighbors at random performs best. Fig. 9 shows the performance of all random algorithms. Depth requester random performs best overall /4 $17. (C) 24 IEEE 4

5 6 5 Comparison of Breadth Fulfiller Experiments Breadth Fulfiller Closest Breadth Fulfiller Farthest Breadth Fulfiller Random Figure 5. Performance of three different breadth at fulfiller algorithms. 6 5 Comparison of Breadth Requester Experiments Breadth Requester Closest Breadth Requester Farthest Breadth Requester Random Figure 6. Performance of three different breadth at requester algorithms /4 $17. (C) 24 IEEE 5

6 6 5 Comparison of Depth Fulfiller Experiments Depth Fulfiller Closest Depth Fulfiller Farthest Depth Fulfiller Random Figure 7. Performance of three different depth at fulfiller algorithms. 6 5 Comparison of Depth Requester Experiments Depth Requester Closest Depth Requester Farthest Depth Requester Random Figure 8. Performance of three different depth at requester algorithms /4 $17. (C) 24 IEEE 6

7 6 5 Comparison of All Random Experiments Breadth Fulfiller Random Breadth Requester Random Depth Fulfiller Random Depth Requester Random Figure 9. Comparison of the four random algorithms. 6. Discussion 6.1. Where to add references Our results indicate that it is better to start adding additional references at the requester rather than at the fulfiller. An explanation is that the fulfiller and its neighborhood typically already know much more about the requested key than the requester and its neighborhood. It is beneficial to announce the news towards other regions of the network. Our results also indicate that the depth-first approach is better than the breadth-first approach. An explanation is that depth-first is better at building bridges between existing specialization clusters and other clusters. In other words, we are spreading the news of the fulfiller s cluster into other specialization clusters. Breadthfirst, however, only sends the good news to our immediate neighbors in our current cluster. Thus, we are not significantly decreasing pathlengths because bridges are not being constructed. Our results furthermore indicate that random is the best criterion for choosing nodes to add the additional references to. Random is a fusion between closest and farthest. Whereas closest only sends the news essentially within the specialization cluster, and farthest indiscriminately sends the news far away and fails to reinforce the clustering action, random is the best of both methods The bread crumb algorithm Based on the lessons we learned, we designed yet another request algorithm called bread crumb. Recall the story of Hänsel and Gretel. In that story, the two children leave a trail of bread crumbs behind them so that they can later find their way home by retracing the steps along the bread crumb trail. The bread crumb algorithm works in a similar fashion. As Freenet retraces its steps along the trail that a successful request query created, bread crumb spreads the news along branches off of the main trail. Besides specifying how the next node is chosen from the reference table (random, closest or farthest), the bread crumb algorithm family has variants that go broad and/ or deep at each node along the main trail, see Fig. 1. Fig. 11 shows that random performs better than closest, as before. The best performing bread crumb variant is 2-broad 2-deep. Fig. 12 compares bread crumb 2-broad 2-deep to Freenet s original algorithm and to depth requester random, the best algorithm so far. In comparison to Freenet s original request algorithm, bread crumb 2-broad 2-deep is able to reduce average request pathlength by up to a factor of After 11,7 actions, bread crumb 2-broad 2-deep average pathlength is 6.63, whereas Freenet s original algorithms is at How many references to add The variants of bread crumb (see Fig. 1) differ in how many additional references they add. Assuming subchoice closest and routing table capacity of only references, Fig. 13 shows that average pathlength for the 3-broad 2-deep algorithm increases considerably after 2, actions. In this experiment, adding only two additional references per hop performs best. Similar trends can be seen with the other algorithm families /4 $17. (C) 24 IEEE 7

8 Original Freenet 99:1 Depth Requester Random Bread Crumb (2-broad 2-deep) Figure 12. Comparison of the best bread crumb, depth requester random and Freenet s original algorithm (logarithmic y-axis). 7 6 Bread Crumb (2-broad) Random Closest Requester Fulfiller Requester Fulfiller Requester Fulfiller Requester Fulfiller Figure 1. Variants of the bread crumb family: 1-broad (upper left), 2-broad (upper right), 2-broad 2-deep (lower left) and 3-broad 2- deep (lower right) Figure 11. Performance of two different bread crumb algorithms /4 $17. (C) 24 IEEE 8

9 Bread crumb (closest) 1-broad (1 reference per hop) 2-broad (2 references per hop) 2-broad 2-deep (6 references per hop) 3-broad 2-deep (12 references per hop) Figure 13. Adding too many additional references can backfire (variants of bread crumb, routing table capacity of only references). Depth Requester Random 1 reference per hop 3 references per hop 6 references per hop 12 references per hop Figure 14. Adding too many additional references can backfire ( depth requester random ) /4 $17. (C) 24 IEEE 9

10 Fig. 14 shows depth requester random assuming routing table capacity of 2 references (our standard assumption). In this graph, we vary the number of additional references from one per hop to 12 per hop. (In all earlier graphs, three additional references per hop were added.) Whereas initially, average pathlength decreases at a slower pace if adding only one reference per hop, after 15, actions, average pathlength increases if 12 or 6 references are added per hop. Clearly, adding too many additional references can backfire. Since reference tables are finite, some references will be pushed out. By adding too many references, we risk placing too much emphasis on recently requested files. Future work includes further investigation into how the optimal number of additional references depends on the size of the reference table, the number of nodes in the network and the number and popularity of documents. 7. Conclusions At the core of Freenet-style peer-to-peer systems are insert and request algorithms that dynamically change the overlay network and replicate files on demand. We ran simulations to test how effective these algorithms are at improving the performance of subsequent queries. We observed that Freenet s original request algorithm is not very effective in changing the overlay network because in average only a small number of new references are added per successful request. We therefore designed and tested a wide range of new request algorithms that reward fulfillers of successful requests by adding additional edges to the overlay network. Our results showed that the performance is sensitive to (1) where to add edges and (2) how many edges to add. Our main conclusions are as follows: It is better to spread the news deep rather than broad. It is better to start spreading the news at the requester (towards other regions of the network) rather than at the fulfiller. It is better to spread the news to a random neighbor rather than to the closest neighbor (the node in the reference table with the key closest to the key of the requested document). Farthest is worst. Adding too many additional references can backfire, causing the reference table to become saturated with recently requested files due to its finite storage size. In comparison to the original request algorithm, our best new algorithm, bread crumb 2-broad 2-deep, is able to reduce average request pathlength by up to a factor of Future work includes the design of distributed algorithms that dynamically change the number of additional references. Acknowledgements We would like to thank the John S. Rogers Science Research Program and the W.M. Keck Foundation for their support. References [1] Aurora simulator. viewcvs.cgi/freenet/aurora/. [2] M. Castro, P. Druschel, Y. C. Hu, and A. Rowstron. Exploiting network proximity in peer-to-peer overlay networks. 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