Capability-Aware Object Management based on Skip List in Large-Scale Heterogeneous P2P Networks

Transcription

1 Takash Tommoto, Takuj Tachbana, Kenj Sugmoto Capablty-ware Object Management based on Skp Lst n Large-Scale Heterogeneous PP Networks TKSHI TOMIMOTO, TKUJI TCHIBN, KENJI SUGIMOTO Graduate School of Informaton Scence Nara Insttute of Scence and Technology 86-5 Takayama, Ikoma, Nara 63- JPN E-mal: {takuj-t,kenj@s.nast.jp bstract: - In ths paper, we propose an object management method n large-scale heterogeneous PP networks. In the proposed method, objects can be stored n and searched from nodes by consderng node's capabltes. The proposed system s based on skp lst, and two dentfcatons are utlzed; and HashID. The s used to specfy each node's capabltes, on the other hand, HashID s used for provdng load balancng among nodes wth smlar capabltes. ccordng to the two dentfcatons, message routng for storng and searchng objects s performed. We evaluate the performance of the proposed method by smulaton, and we nvestgate the effectveness of the method. Numercal examples show that the proposed method can manage objects based on node's capabltes and can provde the scalablty when the number of nodes s large and the dfference n node's capabltes s large. Key-Words: - Peer-to-Peer networks, Skp lst, DHT, Heterogeneous networks, Object management Introducton Peer-to-peer (PP) networks have been wdely used over the Internet for varous applcatons such as Internet telephony [], dstrbuted data storages [], data streamng [3], and onlne games [4].The numbers of PP users and objects ncrease year by year, and n order to manage a large number of users and objects, structured PP networks based on dstrbuted hash table (DHT) have emerged, ncludng Chord [5] and Pastry [6]. In the DHT-based PP networks, a key s assgned to each node and each object. n object s stored n a node whose key s closest to the object's key. In addton, an objectve s searched from a node whose key s closest to the object's key. By usng a hash functon for the key assgnment, the load balancng can be provded among partcpatng nodes. Moreover, the dfference n node's capabltes becomes large year by year [7,8]. For example, multmeda fle sharng, traffc/weather predcton, and stress level montorng of buldngs are just begnnng to be utlzed by moble hand-held devces or sensor nodes [-]. In the future, t s ndspensable to buld large-scale heterogeneous PP networks. Fg. shows a large-scale heterogeneous PP network where several types of objects are utlzed. Map Photo Musc Fg. : Heterogeneous PP network for multple applcatons. In such PP network, t s expected that each object s stored n a node whose capabltes satsfy the requrement of the object. In addton, t s expected that each object s searched from a node whose capabltes satsfy the requrement of the object. For example, musc fles should be stored n hghperformance computers and temperature data should be searched from sensor nodes. Hence, objects should be managed by consderng node's capabltes. In ths paper, we propose an object management method that s used n large-scale heterogeneous PP networks. In the proposed method, objects can be stored n and searched from nodes by consderng node's capabltes. Ths proposed method s based on skp lst []. In the capablty-aware object management, two dentfcatons are utlzed; and HashID. ISSN: Issue 5, Volume, May

2 Takash Tommoto, Takuj Tachbana, Kenj Sugmoto s assgned to each node accordng to ts own capabltes such as forwardng speed, data storage, moblty, and avalablty. It s also assgned to each object accordng to capabltes of node whch the object should be stored n or searched from. On the other hand, HashID s assgned to each node and each object wth a hash functon. When an object s stored n or searched from a node accordng to pre-specfed and HashID, message routng s performed based on the two dentfcatons. Frst, accordng to, a message s routed to one of the nodes whose s are the same as the pre-specfed. Then, the message s routed accordng to HashID, and fnally the message s receved by a node whose HashID s closest to the pre-specfed HashID. By usng the proposed method, t s expected that each object can be managed accordng to node's capabltes based on and that the load balancng can be provded based on HashID. In addton, ths method has hgh scalablty for heterogeneous envronments. We evaluate the performance of the proposed method by smulaton, and we nvestgate the effectveness of ths method. Moreover, we evaluate ts performance over physcal networks. Note that our proposed routng algorthms and smulaton results have been updated from those n our prevous works [3]. The rest of the paper s organzed as follows. Secton descrbes PP system based on skp lst, and Secton 3 explans the proposed capabltyaware object management. Numercal examples are shown n Secton 4 and fnally, conclusons are presented n Secton 5. PP System based on Skp Lst Skp lst has been proposed as a randomzed balanced tree data structure []. In ths data structure, each data has a specfc key and every data s sorted by key. Fg. shows an example of skp lst wth three levels. s shown n ths fgure, every data s doubly lnked n ncreasng order by key at level, and at level ( ), each data n level appears n level wth probablty p ( p ). The lsts at hgher level allow the sequence of data to be traversed quckly. Therefore, when data wth a partcular key s searched, the searchng process s performed at hgher level. Recently, data structures smlar to the skp lst have been utlzed n large-scale PP networks; skp graph [4] and SkpNet [5]. PP nodes n the two networks correspond to data n the skp lst. s H E D 4 7 shown n Fg. 3, n a skp graph wth h ( h ) levels, each PP node has a specfc key and all nodes are doubly lnked n ncreasng order by key at level, as s the case wth the skp lst. t level ( h), there are one or more doubly lnked lsts and each node belongs to one of the lsts. Each node has an dentfcaton called membershp vector n addton to ts own key, and a lst where a node belongs at level s determned by ts own membershp vector. On the other hand, n the SkpNet, rng data structure s used nstead of lst data structure. Fg. 4 shows an example of SkpNet wth four levels. s shown n ths fgure, there are one or more rngs at each level, and rngs at level are obtaned by splttng a rng at level nto two dsjont sets. Each node has two dentfcatons called name ID and numerc ID, and a node belongs to one of the rngs at each level accordng to ts own numerc ID. In every rng, nodes are sorted by name ID. In both the skp graph and SkpNet, message routng s performed accordng to the two dentfcatons. message can be transmtted to a node wth pre-specfed dentfcatons. By usng the dentfcatons effectvely, the skp graph and SkpNet can provde several functons such as object storage, path localty, and constraned load balancng for large-scale PP networks. 3 Proposed Method In ths paper, we propose a capablty-aware object management n large-scale heterogeneous PP networks. The proposed method s based on the skp 6 Fg. : Skp lst wth three levels Lnk 3 38 Lnk 5 4 Lnk 45 Lnk Lnk Lnk Lnk Lnk Lnk Route lnk Membershp vector Fg. 3: Routng mechansms for a skp graph. T I L Level 3 Level Level Level ISSN: Issue 5, Volume, May

3 Takash Tommoto, Takuj Tachbana, Kenj Sugmoto Rng Rng Rng Rng Rng Rng Rng Rng Numerc ID Rng Rng 38 Rng Rng 5 3 Lv CW CCW Routng table Rng Rng Name ID Root rng 45 Level 3 Level Level Level lst n order to consder node's capabltes, and rng data structure s used as s the case wth SkpNet for the effcent message routng. In the followng, we explan our proposed method n terms of ID assgnment, node structure, message routng, and an example of use. 3. ID ssgnment The proposed method utlzes two dentfcatons called and HashID. and HashID correspond to name ID and numerc ID n SkpNet, respectvely. The s assgned to each node for specfyng ts capabltes such as forwardng capablty, data-storage capablty, moblty, and avalablty. In ths paper, for the smplcty, we assume that four-dgt ( w, x, y, z) s used as follows. Frst dgt (w) : Forwardng capablty Second dgt (x) : Data-storage capablty Thrd dgt (y) : Moblty Fourth dgt (z) : valablty Here, the number of dgts of can be changed dependng on how many capabltes should be consdered, and a capablty can be denoted wth more than one dgt n order to represent the capablty n more detal. Table shows an example about how each dgt number s determned. When the forwardng capablty C (w) s. Gbps, data-storage capablty C (x) s 5 Gbytes, moblty C (y) s low, and avalablty C(z) s hgh for a hgh-performance computer, s set to wxyz (see Fg. 5(a)). In the case of a cell-phone whose C (w) s.4 Mbps, C (x) s 5 Mbytes, C (y) s hgh, and C (z) s Name ID routng Numerc ID routng Fg. 4: Data structure and message routng n SkpNet. Table : ssgnment of four-dgt. w C ( w) Gbps C ( w) Gbps x C ( x) 5 Gbytes C ( x) 5 Gbytes y C( y) low C( y) hgh z C(z) hgh C(z) low C(w):. Gbps C(x): 5 Gbytes C(y): low C(z): hgh (a) Hgh-performance computer. C(w):.4 Mbps C(x): 5 Mbytes C(y): hgh C(z): hgh (b) Cell-phone. Fg. 5: assgnment. : : IP address.68.. HashID: Hash functon Fg. 6: HashID assgnment. hgh, of ths node s set to wxyz (see Fg. 5(b)). On the other hand, HashID s assgned to each node by applyng a collson-resstant hash functon to ts IP address or others. For example, HashID of the hgh-performance computer n Fg. 5(a) s set to based on ts IP address (see Fg. 6). Moreover, a character : s used for each node n order to dscrmnate ts own and HashID as :HashID. In the case of Fg. 5(a) and Fg. 6, ths hgh-performance computer s denoted as :. 3. Node Structure Fg. 7 shows a node structure for the capabltyaware object management n a case of four-dgt. In ths structure, there are one or more rngs at each level, and rngs at level are obtaned by splttng a rng at level nto multple dsjont sets. The number of levels s H when the number of dgts of HashID s H. Each node belongs to a rng at every level so that -dgts prefx of HashID s shared by other nodes. ISSN: Issue 5, Volume, May

4 Takash Tommoto, Takuj Tachbana, Kenj Sugmoto Rng Rng Rng Rng Level routng Rng Rng Level Lv CW CCW Routng table Root rng HashID routng Level Fg. 7: Node structure and message routng based on and HashID. For example, a node whose the frst dgt of HashID s belongs to Rng at level. Because nodes are sorted by at each rng, nodes wth the same,.e., smlar capabltes, are located n a rng sequentally. Here, the number of nodes n a top-level rng s one f all nodes have dfferent HashID. s the level becomes low, the number of nodes n a rng becomes large. Therefore, by usng hgher-level rngs, messages are traversed to a node quckly. Under ths node structure, each node has a routng table whch ncludes neghbor nodes at each level (see node n Fg. 7). Ths routng table s used for message routng, whch s explaned n the next subsecton. 3.3 Message Routng In the proposed method, message routng for storng and searchng an object s performed based on and HashID. Moreover, message routng for node jon and departure procedures s also performed based on the two dentfcatons. Fgs. 8 and show two message routng algorthms. In the followng, we explan the routng algorthms n a case where a source node sends a message to a destnaton node. t frst, the source node starts message routng based on as shown n Fg. 8 (sold lnes n Fg. 7). If the of the source node shares some common prefxes wth that of the destnaton node, the source node determnes a drecton of message routng from both ts own and destnaton node's (see () of Fg. 8). In Fg. 8, clockwse drecton s denoted as true and counter clockwse drecton s denoted as false. On the other hand, f the source and the destnaton SendMsg(, HashID, msg) { f(longestprefx(, localnode.) == ) msg.dr = RandomDrecton(); else f( < localnode.) msg.dr = false; // CounterClockwse else msg.dr = true; // Clockwse msg. = ; msg.hashid = HashID; RouteBy(msg); RouteBy(msg) { h = localnode.maxroutngtableheght; (B) whle(h >= ) { f(lesbetween(msg.dr, localnode., localnode.routngtable.[h][msg.dr]., msg. ) == false ) { h--; contnue; () (C) f(lesbetween(msg.dr, localnode., localnode.routngtable.[h][msg.dr]., msg. ) == true ) (D) { NextCanddateNode = localnode.routngtable.[h][msg.dr]; f(checkiflreadyvsted(msg, NextCanddateNode)) (E) { h--; contnue; msg.lreadyvsted(localnode); SendtoNode(NextCanddateNode, msg); (F) return; f( localnode.!= msg. ) (G) { Negatveck(msg); return; msg.dr = true; RouteByHashID(msg); (H) Fg. 8: Routng algorthm based on. ISSN: Issue 5, Volume, May

5 Takash Tommoto, Takuj Tachbana, Kenj Sugmoto RouteByHashID(msg) { f( msg.hashid == localnode.hashid msg.fnaldestnaton == true ) { () DelverMessage(msg); return; f(msg.startnode!= null && localnode == msg.startnode ) { (B) msg.fnaldestnaton = true; SendtoNode(msg.bestNode); return; h = CommonPrefxLen(msg.HashID, localnode.hashid); (C) f( h > msg.rnglvl) { msg.rnglvl = h; msg.startnode = msg.bestnode = localnode; f( abs(localnode.hashid - msg.hashid) < abs(msg.bestnode.hashid - msg.hashid) ){ (D) msg.bestnode = localnode; f( localnode.routngtable[h][msg.dr]. == msg. ){ (E) SendtoNode(msg, localnode.routngtable[h][msg.dr]); else f( msg.dr == true ){ (F) msg.fnaldestnaton = false; msg.startnode = null; msg.dr = false; SendtoNode(msg, localnode); else f( msg.dr == false ) { (G) msg.fnaldestnaton(true); SendtoNode(msg, msg.bestnode); Fg. : Routng algorthm based on HashID. have no common prefx, a routng drecton s selected at random. Then, along the selected drecton, the source node tres to fnd a canddate of the next node from the top-level ponter n ts own routng table (see (B)), and f such a node cannot be found, the level of ponter s decreased (see (C)). By usng hgher level ponter preferentally, as s the case wth the skp lst, the message reaches the destnaton node quckly. When a canddate of the next node s found, the current node checks whether the canddate node has already receved the message (see (D) and (E)). If the node has receved the message prevously, the source node tres to fnd a new canddate of the next node from lower level ponter agan. Otherwse, the source node sends the message to the canddate node as the next node (see (F)). Ths process contnues untl the message s receved by a node whose s the same as the destnaton. When the message arrves at a node wth destnaton, the message routng based on termnates (see (H)). If there s no node wth destnaton, ths message routng fals and negatve acknowledgment s sent back to the source node (see (G)). Note that n the actual system, a message wll reach a node wth the closest. () () (a) Lst structure. () () (b) Rng structure. However, ths s out of scope n ths paper because t depends on the mplementaton. Just after the termnaton of the message routng based on, message routng based on HashID starts as shown n Fg. (dotted lnes n Fg. 7). Ths message routng s performed only among nodes wth the destnaton. Note that the message tends to be routed n a lst structure at most of rngs (see Fg. (a)) but the message may be routed n a rng structure at hgher-level rng (see Fg. (b)). In the message routng based on HashID, at frst, the node checks the number of dgts whch are shared between ts own HashID and the destnaton HashID (see (C)). When the number of shared dgts s h, the message routng starts at level h. The ntal routng drecton s set to true n Fg. 8. The node checks whether of the neghbor node s the same as ts own. If the neghbor node has the same, the node forwards the message to the neghbor node (see (E) of Fg. and () of Fg. ). t ths tme, when HashID of the neghbor node s closer to the destnaton HashID than ts own HashID, the nformaton about the neghbor node s stored n the message as the best node (see (D)). When the neghbor node has a dfferent, the current node reverses the routng drecton and contnues the message routng (see (F) of Fg. and () of Fg. (a)). The message routng based on HashID termnates when the node wth the destnaton HashID s found (see ()), when the message routng for both drectons fnshes (see (G) of Fg. and (3) of Fg. (a)), or when a node receves the message agan n the ntal drecton (see (B) of Fg. and () of Fg. (b)). In order to decrease the number of hops for message routng over physcal networks, we have to consder the network proxmty n our message (3) Fg. : Node structure for message routng based on HashID. ISSN: Issue 5, Volume, May

6 Takash Tommoto, Takuj Tachbana, Kenj Sugmoto Rng Rng Rng Rng Rng Rng Root rng Fg. : Example of object management where car node downloads an object from server node B. routng algorthm. However, ths s out of scope because some solutons have been proposed n [5] and those are avalable n our method. 3.4 Example of Use Fg. shows a PP network where several types of nodes have partcpated by usng a PP software wth our proposed method. In ths network, we assume that musc fles have been stored n hghperformance fle servers. That s, of musc fle s the same as that of hgh-performance fle server. For example, when a drver (car node ) tres to lsten to a favorte song, the drver nputs ts ttle and fle type nto the software. In ths software, of the musc fle s determned from ts fle type, and HashID s determned wth a hash functon from ts ttle. Then, message routng starts n the PP network to fnd a destnaton node wth the musc fle based on the determned and HashID. If the message reaches the destnaton node (server node B), the drver can download the object from ths node. If a DHT-based PP network s bult for each node type,.e., for each n our method, a smlar object management may be mplemented. However, ths mplementaton requres that each node has at least a ponter to each PP network. Therefore, the number of ponters becomes large when the number of node types becomes large. On the other hand, n our proposed method, each node has only nformaton about neghbor node's s. Therefore, n the future, t s expected that the proposed method s effectve n large-scale heterogeneous PP networks. B B B 4 Numercal Examples In ths secton, we evaluate the performance of the proposed method by smulaton. We assume that the number of PP nodes s N and the number of objects s M. We consder how M objects are stored n N nodes by usng the proposed method. In ths PP network, a four-dgt s assgned to each node and each object. For the smplcty, n the followng, we denote four-dgt wth decmal number format, for example, s denoted as 5. We assume that ( 5) s assgned to a node (an object) wth probablty ( ). On the other hand, HashID s denoted as 8 bts bnary strng, and t s assgned to a node (an object) wth a hash functon. Under ths stuaton, we evaluate by smulaton the performance of the proposed capablty-aware object management. In order to perform the performance comparson, we also evaluate the performance of a conventonal DHT-based method where node's capabltes are not consdered. 4. Impact of ssgnment for Object Frst, we nvestgate the mpact of assgnment for each object. Here, the number of nodes s N 6384, and ( 5) s assgned to a node wth probablty / 6. In addton, we assume that the number of objects s M 5. Here, we consder two cases for probablty wth whch ( 5) s assgned to each object. In case,.5,., 3 4.5, 5 4., and 5.. That s, about half of objects should be stored n nodes wth but no object should be stored n nodes wth 5. On the other hand, n case,.,.,.5, 3 4., and 5.5. In ths case, about half of objects should be stored n nodes wth 5 but no object should be stored n nodes wth. Fg. (a) shows the total number of objects whch are stored n nodes wth n the case. From Fg. (a), we fnd that by usng the conventonal method, objects are stored n all nodes randomly regardless of those capabltes. s a result, n the conventonal method, capabltes of each node are never consdered, as expected. On the other hand, we fnd that each object can be stored n N nodes accordng to by usng the proposed method. For example, nodes wth stores about half of objects and nodes wth ISSN: Issue 5, Volume, May

7 Takash Tommoto, Takuj Tachbana, Kenj Sugmoto Number of objects 3 Conventonal method 5 Capablty-aware management Number of objects Conventonal method Capablty-aware management (a) Total number of objects stored n nodes wth each n case. Number of objects 3 Conventonal method Capablty-aware 5 management (a) Total number of objects stored n nodes wth each. Number of objects.5 Conventonal method. Capablty-aware management (b) Total number of objects stored n nodes wth each n case. Fg. : Impact of assgnment for each object. 5 stores no object. Therefore, the proposed method can manage objects by consderng node's capabltes. Note that n all the results for the proposed method, an object wth has been stored n a node wth necessarly. Fg. (b) also shows the total number of objects n the case. From ths fgure, we fnd that objects can be stored n nodes wth pre-specfed capabltes by usng the proposed method. However, n the conventonal method, objects are stored n all nodes randomly regardless of. Therefore, the proposed method s effectve n heterogeneous envronments. 4. Impact of ssgnment for Node Next, we nvestgate the mpact of assgnment for each node. In ths subsecton, the (b) verage number of objects stored n nodes wth each. Fg. 3: Impact of assgnment for each node. number of nodes s N 6384 and the number of objects s M 5. Fg. 3(a) shows the total number of objects that are stored n nodes wth, and Fg. 3(b) shows the average number of objects. Here,.,.5,., 3., 4.8, 5.7, 6.6, 7.5, 8.4,.3, 3., and That s, the number of nodes wth decreases as becomes large. On the other hand, s assgned to each object wth probablty /6. Hence, more or less the same number of objects should be stored evenly for each. From Fg. 3(a), we fnd that by usng the conventonal method, the total number of objects for each becomes small as ncreases. ISSN: Issue 5, Volume, May

8 Takash Tommoto, Takuj Tachbana, Kenj Sugmoto Number of objects <x4> 6 4 <xd> 5 <xc> xf <x> <xb> x HashID space <xe> <x3> 7 8 <x3e> 4 6 Ths s because the number of nodes becomes small as becomes large. s shown n Fg. 3(b), the average number of objects whch are stored n each node s almost the same regardless of. However, by usng the proposed method, each object can be stored at nodes evenly accordng to regardless of the number of nodes wth each (see Fg. 3(a)). From Fg. 3(b), a node wth larger (smaller) stores a large (small) number of objects. Therefore, n the proposed method, a large number of objects can be stored n a small number of hgh-performance computers and a small number of objects can be stored n a large number of low-performance computers. 4.3 Effect for Load Balancng In ths subsecton, we nvestgate how the proposed method can provde the load balancng among nodes wth the same. We assume that the number of nodes s N 56 and the number of objects s M 5,. We assume that the number of nodes wth s 6 for every, and s equal to /6 for every. Fg. 4 shows the number of objects whch are stored n each node wth. In the horzontal axs of ths fgure, 6 nodes are represented as both nteger number and the frst three-dgts of HashID. It s also shown how 6 nodes are spread n HashID space. From ths fgure, we fnd that the numbers of objects for 6 nodes are much dfferent. Therefore, by usng the proposed method, objects cannot be stored randomly n nodes wth the same. However, from the node dstrbuton n HashID space, we fnd that a large (small) number of objects <x43> <x4f> <x5> <x76> <x76> <x3f> Fg. 4: Total number of objects stored n each node wth. <xb> <xc6> Number of hops 4 3 Case Case Case 3 Case 3 Case Case Number of nodes Fg. 5: verage number of hops vs. number of nodes. are stored n sparsely-dstrbuted (denselydstrbuted) nodes. Ths s because objects are stored n nodes accordng to HashID. Therefore, the proposed method can provde the load balancng f nodes are unformly-dstrbuted n HashID space, for example, when the number of nodes s large. 4.4 Impact of Number of PP Nodes In ths subsecton, we nvestgate the mpact of the number of nodes on the performance of our proposed method. In the followng, the number of nodes s N and the number of objects s gven by M N /. In terms of assgnment for each node, we set / 6 for every. On the other hand, we consder three cases n terms of assgnment for each object. In case, / 6 for every. On the other hand, n case, / 8 when s from to 8 and when s from to 5. Moreover, n case 3,.5,., 3 4.5, 5 4., and 5.. Fg. 5 shows the average number of hops for the proposed method n the three cases. From ths fgure, we fnd that the average number of hops for every case s almost the same. Ths denotes that the average number of hops for the proposed method s not affected by the assgnment for each object. Therefore, the proposed method s effectve even f varous types of nodes have partcpated n a PP network. In addton, we fnd that the average number of hops ncreases as the number of nodes becomes large as expected. Nevertheless, the average number of hops s O (log N) for all the cases. Ths result shows that our proposed method can provde scalablty n terms of the number of nodes. ISSN: Issue 5, Volume, May

9 Takash Tommoto, Takuj Tachbana, Kenj Sugmoto Number of hops Case Case3 Case Case Case Case Number of nodes Fg. 6: verage number of hops vs. number of PP nodes over physcal network. 4.5 Performance over Physcal Networks Fnally, we evaluate the performance of our proposed method over physcal networks. We generate a vrtual physcal network topology based on Barabás-lbert (B) model by usng Boston unversty Representatve Internet Topology generator (BRITE) [6]. The generated network conssts of 5 nodes n a square. In ths network, the number of PP nodes s N, and the nodes are randomly selected among 5 nodes. By usng the proposed method, message routng s performed among N PP nodes based on and HashID. In parallel, over the physcal network, messages are forwarded from a PP node to another PP node va non-pp nodes accordng to Djkstra's algorthm. Fg. 6 shows the average number of hops over the physcal network aganst the number of PP nodes. In ths fgure, the number of objects s gven by M N / for the number of nodes N. ( 5) s assgned to each node wth probablty / 6. On the other hand, n terms of probablty, we consder the same three cases as the subsecton 4.4. From ths fgure, we fnd that the average number of hops s O (log N) for all the cases, as s the case wth the prevous subsecton. Therefore, the capablty-aware object management has scalablty n terms of the number of nodes over physcal networks. 5 Conclusons In ths paper, we proposed the capablty-aware object management based on skp lst, whch s used n large-scale heterogeneous PP networks. In ths method, two dentfcatons are utlzed to consder node's capabltes and provde the load balancng. When objects are stored n and searched from a node, message routng s performed accordng to the two dentfcatons. We evaluated the performance of the proposed method by smulaton. From smulaton results, we found that each object can be stored n nodes by consderng node's capabltes n heterogeneous envronments. In addton, we also found that when the number of nodes s large, the load balancng can be provded easly. The average number of hops for the proposed method s O (log N), and hence the proposed method has scalablty n terms of the number of nodes. From these results, our proposed method s one of the most promsng object management methods n large-scale heterogeneous PP networks. References: [] [] P. Druschel and. Rowstron, PST: Large- Scale, Persstent Peer-to-Peer Storage Utlty, Proc. HotOS VIII, Schoss Elmau, Germany,. [3] [4] S. Yamamoto, Y. Murata, K. Yasumoto, and M. Ito, Dstrbuted Event Delvery Method wth Load Balancng for MMORPG, Proc. 4th CM SIGCOMM Workshop on Network and System Support for Games, 5. [5] I. Stoca, R. Morrs, D. Karger, F. Kaashoek, and H. Balakrshnan, Chord: Scalable Peerto-Peer Lookup Servce for Internet pplcatons, Proc. CM SIGCOMM, ugust. [6]. Rowstron and P. Druschel, Pastry: Scalable, Dstrbuted Object Locaton and Routng for Large-Scale Peer-to-Peer Systems, Proc. Internatonal Conference on Dstrbuted Systems Platforms, November. [7] Z. Xu, M. Mahalngam, and M. Karlsson, Turnng Heterogenety nto an dvantage n Overlay Routng, Proc. IEEE INFOCOM'3, prl 3. [8] J. Hu, M. L, H. Yu, and W. Zheng, Tourst: Self-daptve Structured Overlay n Heterogeneous PP Networks, Techncal Report THTR-CS-HPC-6-, Tsnghua Unverstfy, 6. [] C. Chou, D. We, C. Kuo, and K. Nak, nonymous Peer-to-Peer Communcaton Protocol over Moble d-hoc Networks, Proc. IEEE Globecom 6, November/December 6. ISSN: Issue 5, Volume, May

10 Takash Tommoto, Takuj Tachbana, Kenj Sugmoto [] Y. Zhang, W. Lu, and W. Lou, nonymous Communcatons n Moble d Hoc Networks, Proc. IEEE INFOCOM'5, March 5. [] M. Demrbas and H. Ferhatosmanoglu, Peerto-Peer Spatal Queres n Sensor Networks, Proc. the 3rd Internatonal Conference on Peer-to-Peer Computng, September 3. [] W. Pugh, Skp Lsts: Probablstc lternatve to Balanced Trees, Proc. Workshop on lgorthms and Data Structures, January 3. [3] T. Tommoto, T. Tachbana, and K. Sugmoto, Capablty-ware ID ssgnment and Message Routng based on Skp Lst n Large- Scale Heterogeneous PP Networks, Proc. IEEE Globecom 7, November 7. [4] J. spnes and G. Shah, Skp Graphs, Proc. the 4th CM-SIM Symp. on Dscrete lgorthm (SOD), January 3. [5] N. Harvey, M. Jones, S. Sarou, M. Themer, and. Wolman, SkpNet: Scalable Overlay Network wth Practcal Localty Propertes, Proc. the Fourth USENIX Symposum on Internet Technologes and Systems (USITS'3), March 3. [6] ISSN: Issue 5, Volume, May