Bandwidth Consumption Efficiency Using Collective Rejoin in Hierarchical Peer-To-Peer

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1 908 JOURNAL OF NETWORKS, VOL. 9, NO. 4, APRIL 2014 Bandwidth Consumption Efficiency Using Collective Rejoin in Hierarchical Peer-To-Peer Sri Wahjuni, A.A.Putri Ratna, and Kalamullah Ramli Department of Electrical Engineering, Faculty of Engineering, University of Indonesia, Depok, Indonesia {sri.wahjuni, anak.agung, Abstract Having the advantage of scalability and efficiency in performing a succesful lookup query, a hierarchically structured P2P is a promising architecture for heterogeneous networks. However, in addition to potentially decrease the system performance, the presence of a superpeer failure event also forces normal peers under its influence to disconnect from the system. Optimally, the disconnected normal peers should perform rejoin, otherwise they will loose the granted access to the required service. Currently, the most common rejoin algorithm is based on the flat Chord maintenance algorithm, which is an individual rejoin. Addressing the high bandwidth consumption in individual rejoin, we propose a new approach, termed the collective rejoin algorithm. The analytical results, as well as simulation outputs, show that the rejoin process using the collective rejoin algorithm produces less traffic load than the individual rejoin algorithm. Index Terms Collective Rejoin; Churn; Heterogeneous Network; Superpeer Failure I. INTRODUCTION According to Kangasharju [1] the P2P architectures are categorized as unstructured and structured. From the unstructured architecture, the most well-known centralized architecture is Napster [2], while the one from fully distributed architecture is Gnutella [3]. The compromised architecture between the centralized and fully distributed structures is known as a hybrid architecture, such as Kazaa [4]. The natural benefits of the unstructured P2P architecture are simplicity and stability even though they suffer from a high traffic load as the impact of the use of flooding search method. The later generation of P2P is structured architecture, which is based on a distributed hash table (DHT) for indexing both the peer and the shared-object. Some examples are CAN [5], Chord [6], Pastry [7], and Tapestry [8]. The use of DHT addresses the efficiency and scalability problems, which are the main problems in unstructured architures. However, structured P2P suffers from the dynamics of peers, which is a natural characteristic of heterogeneous networks. The scalability of Chord is related to its indexes distribution approach, that shares the indexes among the nodes. The maximum nodes needed to retrieve to find the object is O(log n), for an overlay network formed by n peers. Chord implements consistent hashing to define an identifier of a node and the object key of the shared objects/services. The identifier of a node is obtained from the hash value of the node identity (using a function such as SHA-1), and the key identifier is obtained from the object identity (such as the file s name). The identifiers are ordered in a modulo 2 m ring size for m-bit identifier. The key k is assigned to the first node that has the same value or follows the identifier k in the identifier space. This node is the successor node of key k, called successor (k). Suppose the identifiers are represented in a ring numbered of 0 to 2 m -1, then the successor(k) is the first node (succeed k) in the clockwise direction. Each node has a finger table and a successor list. As the device s capability in accessing networked application grows, the hierarchical P2P architecture [9-13] is a suitable architecture for handling the heterogeneity of the peer participants. Garces-Erice et al. [9] proposed the generic framework of the two-level hierarchical architecture for DHT-based P2P. Higher capability devices (the ones with longer connection times) form the first level of the hierarchy, and connect to each other in a ring-like structure (such as Chord), while the lower ones are grouped into clusters that form the second level. The peers in clusters communicate with their upper level peers through various schemes and communicate with other peers in other clusters through the gateway peer. The superpeer concept, where a peer with a higher capacity handles more responsibilities than others, was used in the architecture proposed by Montresor [10]. Pandey et al. [11] implemented the Chord-based structure for the top layer of the hierarchical P2P as well as for the lower layer, while Peng et al. [12] implemented unstructured architecture for the lower layer. A formal analysis to compare various lower level peers organizations as proposed by Garces-Erice et al. [9] was presented by Zoels et al. [13]. The authors concluded that a simple star-like design is optimal for two-level hierarchical P2P. Based on this star-like design [13], the works of [14] and [15] are performed on finding the optimal proportion of the superpeers and the regular peers to reach a better performance. Although the hierarchical P2P architecture accommodates the heterogeneity of networks, the diversity of the peers capabilities may cause a higher probability of churn. Churn is a dynamic condition of the P2P network when a node joins or leaves the system [16]. The higher frequency of the churn event (churn rate) will doi: /jnw

2 JOURNAL OF NETWORKS, VOL. 9, NO. 4, APRIL potentially damage the stability of the overlay. This condition is a critical one in a Chord-based P2P, since the validity of its finger table is an important point in supporting a successful lookup query [6]. When a superpeer fails, besides degrading the system performance, this condition also forces the normal peers to be disconnected from the system. In an online groupbased application such as chatting or voice conferencing, the disconnected normal peers must reestablish the connection in order to remain joined to the service. According to Wahjuni et al. [17], the join request for a Chord ring produces a significant amount of latencies, which in turn degrade the successful lookup query rate. Therefore, it is important to focus attention on executing an efficient rejoin process. To address churn issue, Peng et al. [12] applied multiple superpeer in each cluster instead of a single superper. As a consequence, this design has an additional table to maintain by each superpeer, that is list of superpeer in cluster. Normally, in hierarchical Chordbased P2P, a superpeer has two table to maintain, successor list table and normal peers table. Moreover, the flooding mechanism applied in each cluster lead to lower effiency than Chord s as stated by the author. The works of Zoels et al. [13] concluded that simple star-like design is an optimum design for the lower layer of hierarchical P2P. Unfortunately, to our opinion, this design has not address the importance of efficient rejoin process yet. In other publication [18], the authors suggested to let each normal peer has a copy of successor list from its static peer if needed. When a superpeer fails, each normal peer send a rejoin request to the first superpeer in the list (perform rejoin individually). We argue that this rejoin mechanism, will produce a very high volume of rejoin traffic, specially when cluster/group size is big. Moreover, since the disconnected normal peers from the previous superpeer (which is the failed superpeer) will join to the same superpeer (which is the successor of the failed superpeer), the superpeer may overloaded. Interested in this optimum design [13], we proposed a low traffic rejoin approach, termed collective rejoin algorithm, in order to improve benefits of this design. In this paper we describe the efficiency of bandwidth consumption if the rejoin process is performed using our proposed collective rejoin algorithm rather than an individual rejoin algorithm. II. RESEARCH METHOD The proposed rejoin algorithm was implemented on a hierarchically structured P2P, and is part of a complete P2P protocol for heterogeneous networks [19]. A bandwidth consumption model was utilized to analyze the efficiency of the collective rejoin algorithm in consuming bandwidth and compared to that of the individual rejoin algorithm. A. System Architecture The hierarchical architecture implemented is a twolevel hierarchical P2P as ilustrated in Fig. 1. The top layer is a group of superpeers, which are nodes with high capacity, whereas the lower layer consists of groups of normal peers. Nodes in the lower layer are organized in a simple star-like shape as suggested by Zoels et al. [13]. Figure 1. Hierarchical architecture A new attribute, namely neighbor_table, was created in order to support the proposed collective rejoin algorithm. When a new normal peer joins the group, the superpeer should update the list and send it to the newly joining normal peer in response to its join request. Other normal peers within the group will receive this updated list as a response message during periodic maintenance. This list is used to record members of the group and the capacity of each node. In the generic framework, the categoriy of the capacity is based on electric power capacities, which are categorized into three categories: the node with an unlimited electric power resource such as a desktop PC (cap1); a node with a moderate limited battery life, such as a computer notebook (cap2); and a node with a very limited battery life, such as a smartphone (cap3). A node has the chance to have role as a superpeer if it is in cap1 or cap2 category although nodes in the cap1 category have a higher probability of being assigned as a new superpeer than nodes in the cap2 category. We do not consider a cap3 category node to be a superpeer, as Zoels et al. [20] do, because its limited computing resources and electric power are not suitable for handling other peers. Another proposed attribute is time (age), which refers to how long the node has been joining the system. This information is required to guarantee that a peer that sends a request to join message with age > 0 (which is a peer that performs the rejoin process) will be assigned as a superpeer. B. Collective Rejoin Algorithm In the current hierarchical P2P architecture, when a superpeer experiences a failure, the disconnected normal peers should perform a rejoin process using the successor list which is copied from their superpeer. Using this approach, since each normal peer has to perform the rejoin mechanism individually, the join traffic increases linearly to the number of disconnected normal peers. In contrast, in the proposed approach during which the rejoin process is performed collectively, the amount of join traffic only depends on the number of failed superpeers. This benefit occurs because in a group that must perform a rejoin process, an elected normal peer performs the process on behalf of other members within

3 910 JOURNAL OF NETWORKS, VOL. 9, NO. 4, APRIL 2014 that group. The elected normal peer is a normal peer with highest capability in the group. C. Bandwidth Consumption Model Bandwidth consumption is the number of messages produced by the stabilization and rejoin process when a superpeer failure occurs. Basically, a network with N peers and superpeer ratio will be grouped into G, as in G N,0 1 (1) Superpeer ratio is a proportion between the number of superpeers to all peers in an overlay network. Value of α=1 means the system is a flat Chord. In the implemented architecture as ilustrated in Fig.1, these groups form the upper layer of the hierarchical P2P, and G is the ring size. Suppose the normal peers are distributed uniformly among the superpeers, then each group has a size of 1/. This means that each superpeer is responsible for handling normal peers, which is 1 1 (2) Zoels et al. [14] found that the optimal operation for this type of architecture is for the superpeer ratio to be up to 25%. The basic Chord formula described by Stoica et al. [6] is used for traffic calculations that only consider the type of operation and not the number of messages needed to perform each operation. The idea behind this consideration is to form a generic bandwidth consumption analysis of the proposed algorithm. In the implemented hierarchical architecture, when a node joins as a superpeer, it follows the Chord join algorithm. To find its right place in the ring, the node uses a mechanism similar to Chord's lookup query, which is costs logn. In a ring with N peers, and superpeer ratio, the cost for finding its position in the ring (finding_pos) becomes: finding_pos = log(g). (3) Once the node finds its position, it has to build its finger_table and successor list. In Chord [6], the cost for maintenance is log 2 N, for P2P overlay contains N peers. Therefore, the traffic produced by the joining node for this process is maint = log(g) log(g). (4) From (3) and (4) we can summarize that if a node joins as a superpeer, it will generate traffic by the amount of join s = finding_pos + maint = (log(g) (1+log(G)). (5) On the other hand, according to Zoels et al. [18], if a node joins as a normal peer it only needs to send a request to the nearest static peer. For the system overall, this means sending one response message per rejoining normal peer. Suppose f is the ratio of superpeer failure, then the number of disconnected normal peers will be 1 D f N ( 1) f N(1 ) (6) The collective rejoin traffic (rejoin c ) will follow (5) with the multiplier factor of f N, that is rejoin f G join (7) c The individual rejoin will follow both (5) and (6) with a proportion rejoining as superpeers and others rejoining as normal peers. Suppose p is the proportion of D that rejoin as normal peers, then the individual rejoin will be rejoin (1 p) D join p D (8) i In the case of all the disconnected normal peers performing rejoin as normal peers (p=100%), then the rejoin traffic depends only on (6). According to individual rejoin that described by Zoels et al. [18], the number of normal peers of the successor of the failed superpeer will double. Suppose is maximum capacity of superpeers, then a load balancing mechanism must be executed in order not to overload the current superpeer. Otherwise, suppose all the normal peers are distributed randomly, then the new group size will be 1 f N (1 j) 1 (1 j ) N 1 f which is still bigger than and a load balancing mechanism is needed. III. RESULTS AND DISCUSSION As shown in (7) and (8), the difference between the traffic of individual and collective rejoin depends on the following factors: number of peers, superpeer ratio, superpeer failure ratio, and the proportion of disconnected normal peers performing rejoin as normal peers. The analysis will be focused on the impact of the aforementioned factors on the amount of traffics generated by the rejoin process. The simulation was performed to validate the formal analytical results. Based on (7) and (8), varying and N will have similar impacts. Therefore, without loss of generality, a static number of peers is used in the whole analysis as well as in the simulation. Otherwise stated, we set the network size to 1000 peers. A. Impact of Ratio of Superpeer (α) on Bandwidth Consumption In this section, the impact of the superpeer ratio to the bandwidth consumed by both rejoin algorithms is analyzed. Suppose a P2P overlay with N peers and superpeer ratio experiences a failure on 10% of its superpeers, then the amount of failed superpeers is 0.1( N). The graph in Fig. 2 depicts traffic produced by individual rejoin (8) as suggested in [18] compare to our proposed collective rejoin (7), in various superpeer ratios ( ). The higher the value of means the smaller the size of the group (or in other words, the lower the workload of the superpeer). The value of is varied from 1% to 10%, based on experimental results reported by Silverston et al. [21] that recorded no more than 10% of peers staying connected during the application session. Zoels et al. [14] s s (9)

4 JOURNAL OF NETWORKS, VOL. 9, NO. 4, APRIL also suggested keeping the superpeer ratio below 25% of the network size in order to achieve optimum operation costs. It is assumed that 90% of all the disconnected normal peers will rejoin the system as normal peers (a more complete analysis for various normal peers join proportions are presented in the following section). The collective rejoin plots follow (7), while the individual ones follow (8) with p 90%. Although the increase of the bandwidth consumption of individual rejoin is slower than that of collective rejoin, the value is still higher. This is the normal impact, since in individual rejoin, each peer must perform a separate rejoin request. While in our proposed collective rejoin, the rejoin request is performed by the new superpeer on behalf of other peers in the group. B. Impact Analysis of Ratio of Superpeer Failure (f) on Bandwidth Consumption Fig. 4 and Fig. 5 represent the correlation between the superpeer failure ratio and the bandwidth consumption of both rejoin algorithms. As suggested by Silverston et al. [21], the value of is set to 10%. As in part A of this section, p value of 90% is used. For all plots, the higher failure ratio of the superpeer produces higher rejoin traffic, which is a logical consequence of the increase in the number of failed superpeers. Since more failed superpeers cause more disconnected normal peers, the ratio of superpeer failures impacts individual rejoin more significantly than collective rejoin (in Fig. 4 this conclusion is indicated by the higher gradient value of the individual rejoin graph than that of the collective rejoin). Figure 2. Number of rejoin traffic in various To have a representative comparison, the proportion of disconnected normal peers that perform individual rejoin as normal peers (p) is varied for various superpeer ratios ( ). The same range of as shown in Fig. 2 (1% - 10%) is used. Fig. 3 shows the plots of total individual rejoin when 50%, 75%, 90%, and 100% of the disconnected peers rejoin the system as normal peers (inversely 50%, 25%, 10%, and 0% join as superpeers). The graph shows that the higher the proportion of disconnected normal peers which perform rejoin as normal peers, the less bandwidth that is consumed. Moreover, it shows that at p 100%, the amount of traffic is likely to decline, as depicted with equation (8). In complete experimental results for N = 1000, the amount of individual rejoin traffic for this proportion will be lower than the collective one at 15%. This eagers the result in [14], which concluded that the maximum value of superpeer ratio for optimum operational cost is 25%. Nevertheless, the amount of new joining peers may lead to an overload of the superpeer, as discussed in Section 2, and an additional load balancing mechanism should be executed to redistribute the normal peers. Figure 3. Number of individual rejoin traffic for various p and α Figure 4. Number of rejoin traffic in various f For all proportions of disconnected normal peers performing rejoin as normal peers, the higher superpeer failure ratio (f) generates more rejoin traffic. A pattern similar to that depicted in Fig. 3 occurs, where the lower p value produces the higher traffic. Even though at p 100% the lowest bandwidth consumption value is produced, this situation may lead the superpeer being overloaded. Figure 5. Number of individual rejoin traffic for various p and f. C. The Bandwidth Consumption Efficiency In Fig. 6 and Fig. 7 the bandwidth consumption efficiencies of collective rejoin are presented. The bandwidth consumption efficiency (eff) is obtained using the formula eff rejoin rejoin rejoin i c (10) The graph in Fig. 6 shows the bandwidth consumption efficiency of collective rejoin for various ratios of superpeer (α) when various proportion of disconnected normal peers (50%, 75%, 90%, and 100%) perform rejoin as normal peers. Using the same variation of f as in all i

5 912 JOURNAL OF NETWORKS, VOL. 9, NO. 4, APRIL 2014 part of this paper [5%..30%], the bandwidth consumption efficiency for various ratios of superpeer failure are presented in Fig. 7. For all variations of p, the bandwidth consumption efficiency increases with the higher superpeer failure ratio. The graphs in Fig. 6 and Fig. 7 demonstrate that the collective rejoin approach produced less traffic than that of individual rejoin approach. The decreasing of efficiency resulting from the higher superpeer ratio as shown in Fig. 6 is a logical impact of the smaller number of normal peers when the value of is increased. As stated in (2), the higher value of means the smaller size of group. compared to the output of simulation. The same range of as in previous formal analysis [1%...10%] is used. Although both plots have different values, but they have similar pattern. The difference between analysis result and simulation result occurs, since the value in analysis result is the maximum one. The same behaviour as in Fig. 8 occurs, as shown in Fig. 9, when the similar comparation perform for various ratios of superpeer failure (f). Figure 6. Bandwidth consumption efficiency for various Figure 7. Bandwidth consumption efficiency for various f D. Simulation Results To validate the results of previous formal analyses, a Java package was developed to implement the proposed collective rejoin algorithm. The package is bundled as an additional package on Peersim [22]. Separate configuration file was used for each of the simulation scenarios. The generic scenario is as follows: the overlay was created first, and then the superpeer failure event was performed for one hour simulation time (Peersim uses a simulation unit time instead of computer system unit time; we equated one second to 100 simulation units time). Figure 9. Collective rejoin traffic for various f IV. CONCLUSION Hierarchically structured P2P is an appropriate architecture for heterogeneous networks. In order to minimize the performance degradation produced by the normal peers rejoin process following a superpeer failure event, the proposed collective rejoin algorithm is an appropriate solution. The analytical results show that our proposed collective rejoin algorithm has lower bandwidth consumption than the individual rejoin algorithm in all scenarios. The efficiency is even more significant, in the case of the high dynamic overlay which is represented by the high superpeer failure ratios. As it is showed in Fig. 4, for the same incremental value of the superpeer s failure ratio, the increase of traffic produced by individual rejoin is higher than that of collective rejoin. Although the graphs in Fig. 6 shows that for the higher superpeer ratio, the efficiency is decreased, but the value is still more than 30%. From these two experimental schemes we conclude that the collective rejoin algorithm is an efficient approach for a P2P overlay network that highly dinamics as well as for that with a big gopup size. These results are validated by the simulations ouput that produced similar patterns for our proposed collective rejoin algorithm. The similar pattern also occurs in the comparation of formal analytical result and simulation result for individual rejoin algorithm. ACKNOWLEDGMENT Figure 8. Collective rejoin traffic for various In Fig. 8, the formal analytical result of the collective rejoin algorithm for various ratios of superpeer (α) is The main author of this paper thanks the Department of Computer Science, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University for facilitating her study. Part of this study is also supported by The Directorate General of Higher Education, Ministry of Education and Culture, The Republic of Indonesia under Sandwich-Like 2012 Programme (No /E4.4/2012).

6 JOURNAL OF NETWORKS, VOL. 9, NO. 4, APRIL REFERENCES [1] J. Kangasharju, "Peer-to-Peer System," in Handbook of Research on Ubiquitous Computing Technology for Real Time Enterprises, Ney York: IGI Global, 2008, pp [2] [3] [4] [5] S. Ratnasamy, P. Francis, M. Handley, R. Karp, and S. Shenker, A Scalable Content-Addressable Network. Proceedings of the 2001 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (SIGCOMM 01) pp , [6] I. Stoica, R. Morris, D. Liben-Nowell, D. R. Karger, M. F. Kaashoek, F. Dabek, and H. Balakrishnan, Chord: a scalable peer-to-peer lookup protocol for Internet applications, IEEE/ACM Transactions on Networking, vol. 11, no. 1, pp , Feb [7] A. Rowstron and P. Druschel, Pastry: Scalable, decentralized object location, and routing for large-scale peer-to-peer systems, Lecture Notes in Computer Science, 2218: , 2001 [8] B. Y. Zhao, L. Huang, J. Stribling, S. Rhea, A. D. Joseph, and J. D. Kubiatowicz, Tapestry: A Resilient Global-scale Overlay for Service Deployment, IEEE Journal on Selected Areas in Communications, vol. 22, no. 1, pp , [9] L. Garces-Erice, E. W. Biersack, P. A. Felber, K. W. Ross, and G. Urvoy-Keller, Hierarchical Peer-to-Peer Systems, Proceeding of the ACM/IFIP International Conference on Parallel and Distributed Computing (Euro-Par), Klagenfurt, Austria, 2003 [10] A. Montresor, A Robust Protocol for Building Superpeer Overlay Topologies, Technical Report UBLCS , [11] S. Zoels, Z. Despotovic, and W. Kellere, On hierarchical DHT systems An analytical approach for optimal designs, ScienceDirect, Computer Communications 31 pp , [12] Z. Peng, Z. Duan, J. Qi, Y. Cao and E. Lv, HP2P: A Hybrid Hierarchical P2P Network, Proceedings of the 1st International Conference on the Digital Society (ICDS'07), [13] M. Pandey, S. M. Ahmed, and B. D. Chaudhary, 2T-DHT: A Two Tier DHT for Implementing Publish/Subscribe, Proceeding of The International Conference on Computational Science and Engineering (CSE '09) pp , Vancouver, Aug [14] S. Zoels, Q. Hofstatter, Z. Despotovic, and W. Kellerer, Achieving and Maintaining Cost-Optimal Operation of a Hierarchical DHT System. Proceeding of The IEEE International Conference on Communications ICC '09, [15] J. Li, C. Li, Z. Fang, and H. Wang, Layer Optimization for DHT-based Peer-to-Peer Network, Proceeding of The 10 th Annual Conference on Networks (ICN) 2011 pp [16] S. Rhea, D. Geels, T. Roscoe, J. Kubiatowcz, Handling Churn in a DHT, The USENIX 2004 Annual Technical Conference, Boston, MA, USA, [17] S. Wahjuni and K. Ramli, Considering The Nodes Join/Leave Behavior in The Analysis of Chord Stabilization in The Heterogeneous Network, International Journal of Computer Science and Network Security, Vol. 11 No. 12 pp , [18] S. Zoels, Z. Despotovic, and W. Kellerer, Cost-Based Analysis of Hierarchical DHT Design, in Sixth IEEE International Conference on Peer-to-Peer Computing, P2P 2006, 2006, pp [19] S. Wahjuni, A. A. P. Ratna, and K. Ramli, Efficient normal peers group recovery in hierarchical peer-to-peer, in 2012 IEEE International Conference on Communication, Networks and Satellite (ComNetSat), 2012, pp [20] S. Zöls, R. Schollmeier, W. Kellerer, and A. Tarlano, The hybrid chord protocol: a peer-to-peer lookup service for context-aware mobile applications, in Proceedings of the 4th international conference on Networking - Volume Part II, Berlin, Heidelberg, 2005, pp [21] T. Silverston, O. Fourmaux, A. Botta, A. Dainotti, A. Pescapé, G. Ventre, and K. Salamatian, Traffic analysis of peer-to-peer IPTV communities, Computer Networks, vol. 53, no. 4, pp , Mar [22] Sri Wahjuni is currently working toward a Ph. D degree in the Department of Electrical Engineering, University of Indonesia. She is a member of the Laboratory of Net-Centric Computing and a faculty member of the Department of Computer Science, Bogor Agricultural University. Her research interests include embedded systems, mobile computing, and ubiquitous networks. A.A. Putri Ratna is senior lecturer at the Faculty of Engineering, University of Indonesia. She obtained her Master s degree at Waseda University, Japan, in 1990 and her Doctoral degree at the University of Indonesia in Her research interests include computer networks and web-based information systems. Kalamullah Ramli is Professor of Computer Engineering at the Faculty of Engineering, University of Indonesia. He finished his Master s degree in Telecommunication Engineering at the University of Wollongong, NSW, Australia, in 1997 and obtained a Doktor-Inginieur in Computer Networks in 2003 from Universitaet Duisburg-Essen, NRW, Germany. His research interests include embedded systems, computer and communications, and mobile applications.