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1 Multipath Cross-Layer Service Discovery (MCSD) for Mobile Ad Hoc Networks Xu Shao Lek Heng Ngoh Teck Kiong Lee Teck Yoong Chai Luying Zhou Joseph Chee Ming Teo Institute for Infocomm Research A*STAR (Agency for Science Technology and Research) 1 Fusionopolis Way #21-01 Connexis Singapore {shaoxu lhn tklee tychai lzhou cmteo}@i2r.a-star.edu.sg Abstract In this paper we propose and study how to provide multipath cross-layer service discovery (MCSD) for mobile ad hoc networks (MANETs). Cross-layer service discovery integrates service discovery into route discovery by taking advantage of network-layer topology information and routing message exchange. Multipath service discovery differs from multipath routing in that multipath service discovery can be either multipath from a client to a server or multipath from a client to multiple servers depending on the policy used in service selection and invocation. Compared with the traditional unipath cross-layer service discovery (UCSD) MCSD performs better in terms of improving throughput enhancing service availability and optimizing network-layer resources. However as a new service discovery scheme MCSD posts a lot of challenges particularly in the process of service discovery and how to find the optimal multipath. Due to the complexity of the problem we focus on double-path cross-layer service discovery (DCSD) a special and most important case of MCSD. We present a heuristic called which can intelligently find the optimal routes out of the candidate paths from a client to a server and from a client to two servers by minimizing hop count in network layer. Extensive simulation results show that compared with UCSD i) MCSD delivers better service availability; and ii) the proposed leads to a better network-layer performance with reasonable computational complexity. Index Terms Multipath cross-layer service discovery; multi-path routing; double-path cross-layer service discovery; service discovery; MANET I. INTRODUCTION Service discovery is a process allowing networked entities to advertise their services query about services provided by other entities select the most appropriately matched services and invoke the services [1] [2]. Service discovery has been mainly addressed in the context of wired networks in the past where clients and especially servers are typically powerful and resource-rich machines connected to the wired network. There are several standards on service discovery protocols (SDP) e.g. Service Location Protocol (SLP) of IETF Sun s Jini Microsoft s UPnP IBM s Salution Bluetooth s SDP and DEAPSpace. Mobile Hoc Networks (MANETs) are networks that comprise wireless mobile nodes that cooperatively form a network without infrastructure. Service discovery in MANETs that represents the extreme of the pervasive computing spectrum [1] is much more challenging mainly because i) frequent node mobility will affect the service availability; ii) nodes tend to have limited power supply computational resources and memories; and iii) servers and clients join and leave networks more frequently. Studies in the field of service discovery are mainly on three types of networks i.e. wired networks single-hop wireless networks and multihop MANETs. The service discovery architecture for MANETs mainly depends on having a directory or not []. Directory stores information on various services available in the network. Service discovery architectures for MANETs can be directory-based directory-less or hybrid of the two. The service discovery architectures for MANETs can also be categorized by the presence of overlay network support []. In a typical medium-sized MANET (10 to 100 nodes) with medium mobility the consensus is that the architecture that is directory-less and without overlay network support is considered superior to others [] []. For this reason the study of this paper will use this kind of service discovery architecture. Similar to service discovery in other fixed networks service discovery in MANETs that is directory-less and without overlay network support includes several procedures. Services in MANETs can be tangible or intangible [9]. For example a service could be a software service like a ftp server on a mobile device so that when others need this service they can contact this mobile device and use it. A service can also be a hardware service like a printer that can be used by a mobile device to print a file. A service provider is a server and a service consumer is a client. The server will advertise its service first. When a client wants a service it starts a query for the address of the service provider. Servers listen to such messages at a predetermined network interface and port. If the requested service is supported then a reply message is sent back to the client. The client may receive replies from multiple servers. The client will choose a service from a server according to predefined criteria and invoke this service. Service discovery is conducted on the service layer [] that is above the network layer. Service discovery and selection can be integrated into the underlying routing protocol and take network-layer information into account in service discovery especially in service selection decisions which is called network-layer-aware discovery [] [7] or cross-layer service discovery [8]. Studies have demonstrated the overall network performance improvements using the network-layer information. In traditional service selection when multiple entries in the service table match the request the client selects one with the lowest hop count [8]. Since only one server and one path will be chosen in the service selection and invocation process we call these schemes unipath cross-layer service discovery (UCSD). In this paper we argue that sometimes UCSD is not sufficient to provide good performance from both service and network layers point of view. As a result the study of multipath cross-layer service /09/$2.00 c 2009 IEEE 08

2 (a). Multipath from a client to a server (Client to Server 1). (b). Multipath from a client to multiple servers (Client to Server 1 & 2). (c). Multipath from a client to multiple servers (Client to Server 1 & ). Fig. 1. Illustrative example of multipath cross-layer service discovery and network layer resource optimization. discovery (MCSD) for mobile ad hoc networks is mainly motivated by the potential of MCSD to: i) improve throughput; ii) enhance service availability; and iii) optimize network resources. Instead of finding one server and one path in service selection and invocation process in MCSD multiple servers and multiple paths will be considered. The rest of this paper is organized as follows. Section II presents the architecture of MCSD challenges and heuristics for double-path cross-layer service discovery (DCSD). Section III presents extensive simulation results under different MANET topologies and parameters. Section IV concludes this paper. II. MULTIPATH CROSS-LAYER SERVICE DISCOVERY (MCSD) The proposed MCSD comprises two enabling technologies: the availability of network topology information by using cross-layer service discovery and search over multiple candidate paths between client and server(s) for enhanced availability and network performance optimization. A. Cross-Layer Service Discovery The integration of route discovery and service discovery stems from the fact that any service discovery protocol implemented above the routing layer always requires the existence of routing protocols. Two message-producing processes will coexist i.e. service discovery messages and routing messages. As a result a node is forced to perform multiple times the battery-draining operations of receiving and transmitting packets. Cross-layer service discovery exploits the capability of acquiring service information along with routing information by piggybacking service information onto routing messages [7] [8]. For proactively routed MANETs a service reply extension added to topology updating routing messages is enough for providing both service discovery and route discovery concurrently. This way redundant transmissions of service discovery packets at the service layer are avoided and energy is saved. In addition to cross-layer service discovery and selection benefits both service and network layers. First the availability of routing information can greatly reduce the cost and increase the accuracy of service selection. Second the availability of network topology information enables clients to efficiently detect changes in network topology and switch to more reliable routes. B. Multipath Service Discovery There exist mainly two different categories of routing schemes for MANETs: proactive routing and reactive (on-demand) routing. Different routing schemes provide optimal performance under different operating conditions. In some cases using hybrid proactive and reactive routing or using them intelligently can further improve performance [9]. Multipath routing allows the establishment of multiple paths between a single source and single destination nodes. All of these routing schemes can be extended to support multipath routing in MANETs. For survivability reasons the multiple paths need to be link-disjoint or node-disjoint to survive single-link failures or single-node failures [10] [11]. While much previous work has concentrated on multipath routing to the best of our knowledge no effort has gone into multipath service discovery. Multipath routing consists of finding multiple routes between a source and a destination node. These multiple paths between source and destination node pairs can be used to compensate for the dynamic and unpredictable nature of MANETs. Meanwhile multiple paths can provide load balancing fault-tolerance and higher aggregate bandwidth. However we argue that multipath service discovery is quite different from multipath routing. Multipath service discovery includes two scenarios: 1) from a client to a server; and 2) from a client to multiple servers. As an illustrative example in Fig. 1 there are three servers providing the same service and one client. When the client requests a service we can establish two paths from the client to server1 as shown in Fig 1(a). Alternatively we can establish a path from the client to server1 and a path from the client to server2 as shown in Fig. 1(b). Another possibility is to establish a path from the client to server 1 and a path to server as shown in Fig. 1(c). In contrast to multipath service discovery multipath routing studies the first scenario only. In this regard multipath routing is only a special case of multipath service discovery. In this regard the problem of multipath service discovery and selection is more complex than the problem of multipath routing IEEE Asia-Pacific Services Computing Conference (IEEE APSCC) 09

3 Multipath service discovery between a client and multiple servers will bring tremendous benefits. For instance there are two file servers on MANETs. Traditional UCSD works as follows. The file servers advertise their services. The client sends service discovery requests to the network. The file servers reply to the client. According to certain criteria the client will select one server only and invoke the service. In contrast we argue that multipath service discovery may further improve the performance. For example if the client can download different parts of a file from different file servers the speed of downloading will be increased. Furthermore once there is a failure of a server another server can provide a backup so multipath service discovery between a client and multiple servers can not only provide survivability against network failures but also server failures to some extents. C. Challenges of MCSD over MANETs As discussed earlier the proposed MCSD is not a simple combination of two technologies of cross-layer service discovery and multipath routing. The challenges of MCSD over MANETs are twofold: First how to extend the existing multipath route discovery schemes to MCSD? A lot of multipath route discovery protocols have been proposed. Similarly MCSD needs to integrate service discovery into route discovery process. Basically the service advertisement and discovery process do not need many amendments. We need to define and develop new service selection and invocation protocols for the new multipath requirements. Second how to select the optimal multipath from many candidates so as to optimize both application and network layer performance? As discussed earlier MCSD has more candidates compared with multipath routing. In a network-topology-aware service discovery hop count is one of the major metrics. For example in Fig. 1 (a) the total hop count is 1+= in Fig. 1(b) the total hop count is 1+2= and in Fig. 1(c) the total hop count is 1+=. Therefore in terms of hop count in Fig 1 the second choice as shown in Fig. 1(b) has the best network performance. Due to the randomly distributed servers and clients from any places of MANETs and dynamics of MANET topology how to select and adjust multipath using cross-layer information so as to optimize both application and network layers remains a challenge. D. Heuristics for Double-Path Cross-Layer Service Discovery (DCSD) Though there may be many objectives in MCSD the study focuses on the objective to select the multipath with the lowest total hop count of multiple paths. Therefore the problem of MCSD in MANETs can be stated as follows: Given the current MANET network topology traffic distributions and the location of servers and clients find M paths with the shortest total hop count from a client to one or multiple servers. When M is different the heuristic for the optimal result can be quite different. As a special case and the most common case of MCSD double-path cross-layer service discovery (DCSD) has the lowest computational complexity. Furthermore double-path service discovery often achieves results sufficiently close to that of multiple paths. Therefore we focus on DCSD. As shown in Fig. 1 in DCSD no matter how many servers in the network we always use two paths to connect the client and one or two servers. Therefore we will sometimes choose the scenario of one client to the same server with two paths or sometimes choose the scenario of one client to two different servers with two paths depending on whose hop count is shorter. We call the algorithm that compares and chooses from the two scenarios the intelligent double-path cross-layer service discovery () algorithm. For survivability reasons we consider the scenario of link-disjoint two paths and node-disjoint two paths. Therefore the proposed algorithm has basically three steps. Step 1): find the two shortest paths from the client to one server (). Step 2): find the two shortest paths from the client to two servers () and each path corresponds to each server. Step ): compare the results from the previous two steps and choose the shorter ones. is equivalent to traditional double path routing where there are two paths from one client to one server. Assume there are one client c and N servers S = { s s i s N }. In the scenario of 2 paths from c to one 1 server i.e. the sets of paths will be 1 2 P = p p s i S. Hence { } c s i c si c si P min Pc s 1 Pc s P i c s N ( ) =. (1) In the scenario of 2 paths from c to 2 servers i.e. the 1 2 P = p p s i S s j S sets of paths will be { } c s s c s c s i j. Hence ( ) i j i j P = min P c s i s j s i S s j S i j. (2) Therefore the result of will be P = min( P P ). () Though there are no obvious distinctions between the two paths in DCSD here we refer one as working path (or the first path) and another one as backup path (or the second path) where working path may play equal or more important role in service delivery. As a simple and efficient algorithm working path first algorithm [12] is often used for double-path heuristics where the working path is calculated first by using the shortest-path algorithm (such as Dijkstra s algorithm) on the graph followed by using the link- or node-disjoint shortest path as backup path. Below is the heuristic of based on working path first and dynamic routing. Step 1.1) Compute the shortest path to each server as working path using the shortest path algorithm. Step 1.2) Remove all the links that the respective working path passing through (if link-disjoint constraints) or the intermediate nodes that the respective working path passing through (if node-disjoint constraints). Step 1.) Compute the shortest path to the same server as the backup path. Step 1.) Compare the hop count of N pairs of double paths to different servers and choose the shortest one. Note that the hop count of double paths is the addition of hop count of working path and hop count of backup path. 2 The computational complexity of is O( K ) N where K is the number of nodes in the MANETs. For the heuristic of if all the scenarios are compared 2 2 the computational complexity will be O( K ) N. To reduce IEEE Asia-Pacific Services Computing Conference (IEEE APSCC)

4 the computational complexity we will fix the working path with one round of calculation. The heuristic of is as below: Step 2.1) Compute the shortest path to each server as working path using the shortest path algorithm and sort all the servers in an increasing order of working path hop count. We use the path to the first server as the working path. Step 2.2) Remove all the links that the working path passing through (if link-disjoint constraints) or the intermediate nodes that the working path passing through (if node-disjoint constraints). Step 2.) Compute the shortest path to the rest servers as the backup path respectively. Step 2.) Compare the hop count of various backup paths to different servers and choose the shortest one. Step ) is to compare results from and to get the final result. The computational complexity of the 2 proposed heuristic is also O( K ) N so the computational complexity of the proposed is 2 O K. ( ) N III. PERFORMANCE EVALUATION In this section we present the simulation results of MCSD under various MANET network size topologies and other parameters. We dynamically generated MANETs topology with certain network degree. Note that the network degree is defined as 2 the number of links / the number of nodes. The link between two nodes is bidirectional. For example in Fig. 1 there are 9 links and 8 node so the network degree is 2*9/8 = 2.2. Although the network topology is randomly generated for connectivity considerations there are always at least two routes from one node to another node. The simulation program was written with Matlab and the statistical results are the average of at least 0000 service discoveries. In each service discovery the location of multiple servers and one client are randomly generated. However the client and servers are never attached to the same node. Initially we do not consider the changing of link availability due to mobility which requires service rediscovery reselection and re-invocation triggered by changes of service table or route breakdown [8]. In simulation we use reactive routing scheme for dynamic service request. We do not consider traffic load and always assume the nodes can handle all the requests and the metric is the hop count of double paths. The process of simulation is as follows: First the MANET topology with predefined number of nodes and predefined network degree is randomly generated meeting connectivity requirement. Second we generate a client and several servers randomly attached to different nodes of MANETs. Third cross-layer service discovery enables them know each other. Forth service selection and invocation using our proposed and other methods. Last repeat this process and get statistical results. For simplification the number of servers is always 2. In addition to we will also consider the following schemes: 1) only (traditional double path routing); 2) only; ) Network-layer-unaware service discovery (NLUSD) where the service layer does not have the network layer topology knowledge and service selection and invocation cannot be based on the network layer information. In the simulation of NLUSD the server that the client tries to connect to is randomly selected. In terms of double paths it can be NLUSD- or NLUSD-. A. With and without Network-Layer Awareness Table 1 presents the comparison results with and without network-layer awareness. In this simulation the main difference between network-layer-aware and network-layer-unaware service discovery is whether network topology information is available for service selection and invocation or not. We use the 2-node SFNet MANET topology [1] as shown in Fig.2. We compare and with and without network-layer awareness. In network-layer-unaware service discovery the server is chosen randomly from available servers. Table 1 presents the simulation results on average hop count. As expected algorithms with network-layer awareness always outperform the counterpart without network-layer awareness which can be explained that network-layer-aware service discovery is always capable of using the shortest path to minimize the network resource consumption. Fig node SFNet MANET topology. TABLE I. SERVICE DISCOVERY WITH AND WITHOUT NETWORK-LAYER AWARENESS (AVERAGE HOP COUNT). LINK-DISJOINT NODE-DISJOINT NETWORK LAYER AWARE NETWORK LAYER UNAWARE IDCSD IDCSD N.A.02.1 N.A B. Link-Disjoint DCSD Fig. (a) Fig. (c) plot the simulation results when the two paths are link-disjoint with different MANET network size and degrees. Link disjoint constraint means the working path and backup path cannot go through the same link in this network. We can observe that 1) the proposed always performs better than and. 2) If we compare Fig. (a) Fig. (b) and Fig. (c) at the same network size we can see that the average hop count decreases with network degree. This is because in a denser network it takes fewer hops to reach to the destination. ) If we compare and usually is better than in terms of total hop count. This is due to that fact that compared with there are fewer constraints in to find the second path IEEE Asia-Pacific Services Computing Conference (IEEE APSCC) 11

5 1 10 (a) Average network degree is (b) Average network degree is. (c) Average network degree is. Fig.. Simulation results (Link-disjoint). C. Node-Disjoint DCSD Fig. (a) Fig. (c) plot the simulation results when the two paths are node-disjoint with different MANET network size and degrees. Node disjoint constraint means the working path and the backup path cannot go through the same node in this network. If we compare Fig. (a) with Fig. (a) Fig. (b) with Fig. (b) or Fig. (c) with Fig. (c) at the same network size we can see that node-disjoint constraint makes the total hop count a bit longer due to the stronger constraints from node disjoint. Similarly we observe that the proposed always works better than the and. In summary the simulation results in Fig. and Fig. have demonstrated that with equal computational complexity the proposed is capable of delivering better performance (b) Average network degree is. 7 (c) Average network degree is. Fig.. Simulation results (Node-disjoint). D. Unipath versus Double-Path Service Discovery In order to quantity the benefits obtained by using double paths in service discovery and delivery in MANETs some simulations have been carried out to compare double-path service discovery with unipath service discovery. Assume each link s availability is 0.8 where link availability is defined as the probability that the link between two nodes will be found in the operating state at a random time in the future and path availability is defined as the probability that the path will be found in the operating state at a random time in the future [1]. Path availability is the multiplication of availability of links along the path. Let A and A denote the w b working path and backup path respectively so the path availability of double-path service discovery is 1 ( 1 A ) ( 1 A ) w b. Fig. and Fig present the simulation results on average path availability when network degree is 2. and respectively. Note that in this simulation we only assume that the working path and backup path are link-disjoint. We can see that using double paths can significantly improve path availability irrespective of network size degree and topology. This is even true in a lager and sparser MANET. Average availability Unipath service discovery double-path service discovery 0. Fig.. Average availability (link availability is 0.8 and network degree is 2.). (a) Average network degree is IEEE Asia-Pacific Services Computing Conference (IEEE APSCC)

6 Average availability Unipath service discovery double-path service discovery 0.7 Fig.. Average availability (link availability is 0.8 and network degree is ). IV. CONCLUSION We have proposed and studied how to provide multipath cross-layer service discovery (MCSD) for MANETs. We have presented a heuristic for double-path cross-layer service discovery (DCSD) called which can intelligently choose double paths from a client to a server and double paths from a client to double servers by minimizing hop count in network layer with reasonable computational complexity. Extensive simulation results have shown: i) The proposed service discovery architecture with network-awareness and using multiple paths have more benefits than traditional service discovery; ii) Compared with UCSD MCSD delivers even better performance in service availability; and iii) The proposed heuristic of for DCSD leads to a better performance with reasonable computational complexity. Future research includes studying how to make use of more than topology information for example node mobility information from network layer and adaptively use multiple paths according to service availability requirements to further optimize both network and service layer performance. [10] Stephen Mueller Rose P. Tsang and Dipak Ghosal Multipath Routing in Mobile Ad Hoc Networks: Issues and Challenges Lecture Notes in Computer Science Vol. 29 April 200 pp [11] SJ Lee and M Gerla Split multipath routing with maximally disjoint paths in ad hoc networks in Proc. IEEE ICC 2001 vol.10 pp [12] X. Shao L. Zhou W. Zheng Y. Wang "Providing Differentiated Quality-of-Protection for Surviving Double-Link Failures in WDM Mesh Networks" in Proc. IEEE ICC June 2007 Glasgow UK. [1] S. Sarkar H. H. Yen S. Dixit and B. Mukherjee A novel delay-aware routing algorithm (DARA) for a hybrid wireless-optical broadband access network (WOBAN) IEEE Network Vol. 22 Issue May June 2008 pp [1] S. Jiang D. He and J. Rao A prediction-based link availability estimation for routing metrics in manets IEEE/ACM Transaction on Networking vol. 1 no. pp Dec REFERENCES [1] F. Zhu M.W. Mutka and L.M. Ni Service Discovery in Pervasive Computing Environments IEEE Pervasive Computing vol. no. 200 pp [2] C. Dipanjan J. Anupam Y. Yelena and F. Tim Toward Distributed Service Discovery in Pervasive Computing Environments IEEE Trans. Mobile Computing vol. no pp [] A. N. Mian R. Baldoni and R. Beraldi A Survey of Service Discovery Protocols in Multihop Mobile Ad Hoc Networks IEEE Pervasive Computing vol. 8 no. 1 pp. 7 Jan.-Mar [] C. N. Ververidis and G. C. Polyzos Service discovery for mobile Ad Hoc networks: a survey of issues and techniques IEEE Communications Surveys and Tutorials pp. 0 vol. 10 Issue Third Quarter [] V. Hilt I. Rimac M. Tomsu V. Gurbani and E. Marocco A Survey on Research on the Application-Layer Traffic Optimization-(ALTO) Problem draft-hilt-alto-survey-00 available: July 2008 [] U. C. Kozart and L. Tassiulas Network layer support for service discovery in mobile ad-hoc networks. In Proc. IEEE INFOCOM 200 April 200. [7] C. N. Ververidis and G. C. Polyzos A Routing Layer Based Approach for Energy Efficient Service Discovery in Mobile Ad Hoc Networks Wireless Communications and Mobile Computing Vol. 9 no. May 2009 pp. 72. [8] A. Varshavsky B. Reid and E. de Lara A Cross Layer Approach to Service Discovery and Selection in Manets in Proc. 2nd Int l Conf. Mobile Ad-Hoc and Sensor Systems (MASS 0) Nov [9] A. Malatras G. Pavlou and S. Sivavakeesar A programmable framework for the deployment of services and protocols in mobile ad hoc networks IEEE Transactions on Network and Service Management vol. Dec pp IEEE Asia-Pacific Services Computing Conference (IEEE APSCC) 1