Intelligent Content-Based Routing for Enhanced Internet Services

Transcription

1 Sylvanus A. Ehikioya, Suresh Jayaraman, Jose A. Rueda Intelligent Content-Based Routing for Enhanced Internet Services Sylvanus A. Ehikioya *, Suresh Jayaraman * and Jose A. Rueda * Dept. of Computer Science University of Manitoba Winnipeg, MB, Canada R3T 2N2 TRLabs, Winnipeg Scurfield Boulevard, Winnipeg, Manitoba, Canada R3Y 1P6 { arueda, ehikioya, suresh Abstract This paper presents an intelligent content-based router that examines the data in a packet, and then routes the packet to a destination where it can be most quickly, cheaply, and efficiently processed. Before forwarding packets to their respective destinations, the router examines the data in each packet and based on the data, as well as the network state, determines a suitable destination address that can optimize processing of the packet. Current content routers examine only the HTTP based URL requests and routes the request to the best server for processing. These routers fail to examine different type of TCP-based user requests. The content router we propose examines all types of TCP-based requests. The content router is a core router that simply forwards packets to the edge routers for delivery after performing its contentbased processing. Thus, a packet may be redirected to a different destination address than was originally specified. This can be used to improve network bandwidth utilization by replicating network services (e.g. web servers) and doing in-network selection of the optimal replica to use for a particular packet / request. 1. Introduction As the number of Internet users and sites continues to increase rapidly, demands on network transmission bandwidths keep growing and the networks connected to the Internet often become heavily loaded. As a result, locating and accessing relevant information in large distributed systems is sometimes difficult and slow. This limits the practical applicability of wide area distributed systems. To address this problem, efforts must be made to use the available bandwidth more effectively. Transmission links alone do not make a network. Other components such as switches, routers, etc. (and the software that run them) are also parts of a network. Of particular interest in this paper is the router. A router is a device that is used for forwarding packets from one network to another. Every packet must pass through, typically, many routers. The increase in demand for network bandwidth also places a huge demand on network routers [10] and router saturation has an impact on the performance of many distributed computing applications, including electronic commerce. One way to overcome this problem is to develop innovative new router architectures 8

2 Intelligent Content-Based Routing for Enhanced Internet Services that do routing based on packet content in an effort to minimize wasted bandwidth. The design and prototyping of such router architecture is the focus of this paper. Current routers do not examine packet data; rather they blindly forward packets based solely on their destination address (which is contained in each packet header). While this minimizes router processing and thereby increases potential router throughput, it also limits routing flexibility. With content-based routing, it is possible to optimize routing based on application characteristics. Such optimizations can be applied to increase the efficiency of bandwidth use of the Internet. This paper presents an intelligent content-based router that examines the data in a packet, and then routes the packet to a destination where it can be most quickly, cheaply, and efficiently processed. The routing mechanism proposed in this paper uses a set of metrics (including network state information such as the cost, speed, and traffic over various links as well as server proximity and workload) in making decisions about where to forward packets. The job size is not considered as a metric in this paper. The transmission cost of each packet depends upon various factors like network bandwidth, general health of the network (i.e., status of participating servers), and size of each packet. This routing mechanism, which we refer to as Intelligent Content-based Routing, is also useful for any distributed system, which can offer required data at different network locations. It is also extensible to other optimizations based on packet content. This paper proposes a new design for an intelligent content-based router. This design addresses various problems, such as network traffic, load on different servers, replicated data on different servers, and implements a new solution to overcome these problems. Our proposed architecture provides a content-based routing technology that can be used to build application-specific intelligent software routing environments. Such environments can be exploited to create more efficient, geographically distributed databases and other similar applications [6]. The areas of applications are vast, ranging from e-commerce to intelligent network switches and call-center processing. Intelligent content-based routing can provide the following key services: (i) content-based routing, (ii) traffic optimization, (iii) economically scalable services that provide appropriate response to varying processing loads, and (iv) the ability to track content requests and respond with appropriate content. Another area of particular current interest is that content-based routing can be used to deliver optimized Web response time, which is critical to the success of e- commerce applications. That is, content routing enables the transparent selection of the best site and server for processing / delivering the requested content, thereby, providing an enabling technology for more efficient distributed Web site processing. We expect that our design will also lead to other application-level content routing applications and, potentially, to the development of a hardware intelligent content-based router. The architecture we propose in this paper consists of three major components embedded within the content router. They are the Packet Inspector, the Resource Inspector and the Scheduler Unit. We discuss the details of each component in Section 3. We utilize application information from the participating servers and their status. The designed router is capable of finding the load and resource information on each sever dynamically and provides the information to other components of the router in order to process the user s request. The organization of the rest of the paper is as follows: Section 2 reviews the existing mechanisms and products. Section 3 presents our system design while in Section 4 we International Journal of The Computer, the Internet and Management Vol. 14.No.1 (January-April, 2006) pp

3 Sylvanus A. Ehikioya, Suresh Jayaraman, Jose A. Rueda describe different business models for the deployment of our router. Section 5 shows the implementation results. Finally, we provide concluding remarks and suggestions for extension of our present work in Section Background Literature This section introduces the reader to the necessary background and related research work in content-based routing. We briefly examine some of the mechanisms used to improve the speed of information retrieval and message delivery and to enhance the efficiency of network applications. We begin by examining the issues in conventional router design and then discuss several mechanisms to remedy these issues. Some of the approaches incorporate router technology while others do not. Sheldon [5] discusses content routing using content tags / labels for documents in a Wide Area Information Service (WAIS) server using a semantic file system, and a source and a catalog file. In this approach, a query posed as a predicate, is used to identify keywords in a document. The source file contains the details of host name, host address, database name, port number, and a short description of the database. The catalog file contains a list of short headlines for each file in the database. The architecture described in [5] is similar to the one in [17]. The content routing system has a collection of documents and each document has a content label associated with it. Each content label contains a brief abstract of the documents related to that particular collection. Each query predicate contains a field name and the value to be searched. This architecture uses the brute-force searching technique. However, it is inefficient and has a high implementation cost because it requires maintaining a large number of files. Keshav and Sharma [14] discuss general design issues for routers including speed, scalability, consistency, cost, configuration, and bandwidth. The primary design issues are speed and reliability. Reliability is attained by using techniques such as hot spares, dual power supplies and duplicate data paths through the routers [14]. The time taken to do lookups in the routing table typically has a great effect on the performance of a router. Decreasing the time required to lookup the destination address can increase the speed of the router. As the packet size decreases the number and hence cost of route lookup increases. Gupta, et al. [10], Srinivasan, et al. [23], and Waldvogel, et al. [24] address efficient routing table lookup. To increase the speed of packet forwarding (including route lookup), architecture with multiple parallel forwarding engines can also be used. Ghosal et al. [9] provide a detailed scheme for the load balancing of parallel forwarding and processing of packets. Another consideration in designing a router is the scheduling of incoming packets. A simple method is First Come First Serve (FCFS). This method, however, is not an efficient one because the chances of losing packets are high. Design of a fair queuing method by Demers et al. [4], however, resolves these problems at a somewhat higher implementation cost. Another method used in increasing router performance is differentiated processing based on packet type. The increase in performance is achieved by using different schemes for the buffering and forwarding, filtering and classifying, and queuing and scheduling of different packets. These mechanisms can be applied at various levels. The mechanisms we propose in this paper differentiate between packets based on their data content, and allocate resources and customize processing accordingly. Partridge, et al. [15], Asthana, et al. [1], and Konstantinidou [12] discuss hardware design issues related to high performance (multi-gigabit) routers. Hunt, et al. [11] 10

4 Intelligent Content-Based Routing for Enhanced Internet Services discuss a TCP router, called a Network Dispatcher, which supports load sharing over several TCP servers. The dispatcher is placed between the front-end clients and the back-end server and forwards requests from the clients to the server nodes. Responses from servers are returned directly, bypassing the network dispatcher. Though the performance of the router is good, it does not analyze the packet data but merely forwards packets to the most lightly loaded server node. Cardellini, et al. [3] discuss a similar system for geographic load balancing for scalable distributed web systems. Pai, et al. [16] discuss a simple strategy, Locality-Aware Request Distribution (LARD), which is a content-based request distribution system. LARD focuses on static content. The architecture of LARD consists of back-end nodes and a front-end. The front-end is responsible for forwarding requests to the back-end nodes, which constitute the server. In routing a request, this strategy focuses on the content requested and the load on the back-end nodes. LARD uses hashing techniques to locate the requested data. Based on the load on each node, the front-end decides which node should process the given request. When a request arrives, it sends the request to a lightly loaded node, which caches the needed data. If the requested node is fully loaded it will send the request to a new node, which is not heavily loaded. One of the advantages of LARD over normal cluster-based network servers is that it offers enhanced performance due to its high cache hit rates. However, replication of data is required for LARD to be useful. Song, et al. [8] describe an architecture for a scalable and highly available web server accelerator based on caching data from frequently visited sites. These caches are also known as HTTP (Hypertext Transfer Protocol) accelerators. The web server accelerators use multiple processors to provide more cache memory and higher throughput. The system works as follows: The client sends a request into the network and a TCP router receives the request and passes it on to a nearby caching site. If the first site is not the owner of the requested object, it determines the owner and sends the request to the owner along with the TCP connection details. The owner fetches the object from its cache or from the back-end server if it is not in the cache. Finally, the primary owner returns the requested object either directly or indirectly (through caching sites) to the client. Song, et al. [21] also provide an alternative design to [22] that includes a load balancer as a separate node, which may also choose to route the requests using content-based information. The load balancer has information about the availability and load details of each caching site. When the load balancer acts as a content router, it analyzes the content and directly routes the requests to the owner site, which fetches the requested object either from its cache or from the back-end server. Genova and Christensen [8] describe a layer 5 switch for implementing distributed web sites. A distributed web site consists of multiple local sites and the switch acts as a front-end for each local site. Each local site has one or more servers and caches information about the load on, and content available from, the server nodes. When a client makes a request, the switch consults the cache to see if the requested object is available in that local site and what the load information is for the server node. If the node is fully loaded and the request data is not available, the request is passed to the next closest switch. After processing, the requested object is sent back to the client. The routing depends mainly on the data stored in the cache. In a globally distributed site, one can have any number of local sites. Each local site can have any number of server nodes. So, every time a new local site is created or a new server node is added, a new cache should be created or the cache International Journal of The Computer, the Internet and Management Vol. 14.No.1 (January-April, 2006) pp

5 Sylvanus A. Ehikioya, Suresh Jayaraman, Jose A. Rueda size should be increased. Some commercial systems for improving web access times are now available. Cisco [2], for example, discusses a suite of protocols that provides statistical information for load balancing and provides redirection to process a client request. IntelliDNS [23] provides Internet traffic and content redirection; with support both DNS and HTTP based traffic redirection. However, IntelliDNS supports only DNS and HTTP based request requiring large database to store the client s geographical location and the server location. Web Network Services (WebNS) [22], a name-based switching, provides a solution for URL and cookie based intelligent switching. The Web switch parses the URL to identify the client s request and based on the request the switch finds a suitable server or site. The Packet Inspector, which consists of the Packet Capture and Packet Analyzer modules, enables the unit to capture and extract the data in each packet of a user s request. This data is the content that is routed to the appropriate server at that moment based on a set of metrics. The Packet Capture component handles capturing of the packet, extracts the data in the packet, and sends the data to the Packet Analyzer. The Packet Analyzer converts the extracted data from the machine code to readable string format. The converted data is tokenized and a keyword or set of keywords is selected, which is sent to the next component of the system, the Resource Inspector. The packet inspector intercepts the user s request data stream in the form of packets and then extracts the data content (i.e., the payload) which is used for routing. 3. System Architecture Packet Inspector Resource Inspector The existing content routers can fail to deliver correct information to the right people in appropriate time. So a need for an intelligent-content based router arises. A content router analyzes the data present in a packet before forwarding the packet to the appropriate server. The motivation for developing a new intelligent content-based router is to reduce network traffic and to optimize routing cost, which in turn could potentially increase the performance and decrease the latency of the content router. Although some existing content routers possess these features, they fail to examine different types of TCP-based request. Our implementation examines all types of TCPbased user requests. These new features make this design unique when compared with current content-based routers. Figure 1 shows the high-level system architecture of our design [6, 18]. The router consists of the Packet Inspector, Resource Inspector, the Scheduler Unit, and the Switching Unit. We briefly describe each component below. Users Application Information Destination 1 Packet Capture Packet Analyzer Switching Unit ( Router )... Destination n Resource Locator Resource Manager Schedule Plans Cache Manager Load Inspector Network Nodes Scheduler Unit Application Resources Cost Manager Figure 1. Intelligent Content-Based Routing Architecture A core component of the system is the Resource Inspector. The main job of the resource inspector is to assemble vital information about the resources available in the system for ease of access and fast decision-making. The Resource Inspector consists of two components, the Resource Locator and Resource Manager. The Resource Locator collects the resources information. When the switching unit is 12

6 Intelligent Content-Based Routing for Enhanced Internet Services started, the Resource Locator creates resource agents. These agents are capable of moving from one location to another. Because of their mobile property, these agents are called mobile agents. Mobile agents are suitable because they enable us to seamlessly and transparently assess servers (at remote locations) and retrieve appropriate data of interest. The resource agents are sent to different machines to discover / find resources. The resources for e-commerce applications are often stored in databases at participating servers. The resources are heterogeneous because they are built using different database systems like Microsoft Access, Oracle, SQL Server, DB2, Sybase, etc. The resource agents access the appropriate designated server and retrieve the data of interest. The agents extract the metadata of each database, such as the name of the schemas, the description of the schemas, table definitions, etc. These information are passed to the Resource Manager to make informed judgements on where to find the available resources for the application. Based on the metadata information and the server addresses, the Resource Manager collects resources information about the number of databases available in the system, the addresses of these databases, and permissions on the databases and stores the collected data in a resource table. Figure 2 shows the structure of the resource table. Figure 2 Resource Table The Resource Manager scans the resource table for tokenized data obtained from the packet inspector to find the appropriate server or servers for processing the user s request. The obtained server address or addresses are stored in a Data Location Table (DLT), see Figure 3. Each entry in the DLT has a server address. The DLT is sent to the Scheduler Unit for further processing. Figure 3. Data Location Table The Scheduler Unit, a major part of the system, uses the information assembled by the Resource Manager to facilitate contentbased routing. It is responsible for scheduling and allocating transactions to the various servers for execution based on the current processing / work load information of each server. This unit answers questions such as how busy is each server and which server can process the request in the shortest time? The Scheduler selects a best and efficient destination address based on a set of metrics. The set of metrics consists of the load on the server and the distance between the client and the server. The Scheduler Unit consists of the following components: Load Inspector, Cost Manager, and Cache Manager. We discuss in details the functionality of each component below. The Scheduler receives the DLT from the Resource Inspector. For each entry in the DLT, the Load Inspector creates Load Detector Agents. The Load Detector Agent reads the server address and accesses the appropriate server to retrieve its load information. Before accessing the server, the Load Detector Agent checks the status of the server from the System Status Table (SST). The SST contains the status information of International Journal of The Computer, the Internet and Management Vol. 14.No.1 (January-April, 2006) pp

7 Sylvanus A. Ehikioya, Suresh Jayaraman, Jose A. Rueda all the participating servers. Figure 4 shows an SST. Figure 4 System Status Table If the system is active the agent checks the percentage of CPU available for the next process, free memory available and the length of the processor queue to determine the total number of jobs waiting to get processed by the server. If the server is down or inactive the Load Detector Agent ignores the server and looks for the next server address in the DLT. The Load Detector Agent collects the load information and sends it to the Scheduler Unit for further processing. The Cost Manager finds the distance between a client and a server. The Cost Manager uses a simple traceroute procedure to determine the number of hops, or nodes between the client and the given server address, and forms a Proximity Table. Figure 5 shows the structure of the Proximity Table. The Cost Manager scans each row in the DLT to retrieve the server address. For each scanned server address, the distance information is obtained by looking into the Proximity Table. The distance information is retained for further processing. The Cache Manager is a separate component of the Scheduler Unit. The main functionality of the Cache Manager is to get the best and efficient destination address from the Scheduler and puts it into the cache with the corresponding data of interest for that server. When a request comes from a client, the router checks the cache for the requested data and its corresponding server address. If the data is cached, the router picks the server address and sends it to the Scheduler Unit for further processing. If the data is not available in the cache, the router sends the tokenized data to the Resource Inspector to obtain an appropriate server address. The cache is maintained in two different ways; the surrogate server or a file can be maintained as a cache. A surrogate server is similar to a cache where the most frequently requested data is stored. The storage capacity of this server is huge when compared to a file. In our work, we use a file as a cache. An important component of the Scheduler Unit is the Schedule Plan. The Schedule Plan selects the best and efficient server address for routing the users request. The Schedule Plan collects the information from the Load Inspector and Cost Manager and based on the collected information forms a schedule table. Figure 6 shows the structure of schedule table. Figure 6 Schedule Table Figure 5 Proximity Table An efficient server address is selected from the schedule table by comparing all the given metrics. The runtime for our selection algorithm is O(n 2 ) where n is the number of nodes in the schedule table. The selected server address is sent to the switching unit 14

8 Intelligent Content-Based Routing for Enhanced Internet Services for routing the user request. Finally, the Switching Unit is responsible for the actual redirection of the user payload based on the contents of the packets. Using the assembled data of the Resource Manager and the recommended scheduling plans of the Scheduler Unit (network nodes, application resources, etc), the Switching Unit selects the specific destination to route the user request. 4. Business Models The content router we propose in this paper can be used in various network design models. Each design has its benefits. Although several models are possible, we propose only two network designs in this paper: (i) Intelligent content routing for metropolitan type of networks and (ii) Intelligent content routing for wide area networks. The details of these network design models follow. Other network design models we propose are available in [20, 19, 18, 7]. Figure 7 A Sample Design for Metropolitan Network Figure 7 shows one design for metropolitan networks. This network design model has different clients connected to a switch. The Internet Service Provider (ISP) network has a content router connected to an ISP server. The Layer 3 switch, which is outside the ISP network, is connected to the content router. A bypass router is connected to the content router. The ISP server may have many differentiated servers connected to it, which offers different services. Each server has different databases on it. The content router is also connected to the Internet. This model is specifically designed for registered services with the ISP. The registered services can be a single company with different branches or it can be different companies with a single major server. The clients send requests into the network. The Layer 3 switch collects the user request in a packet format and forwards the packets to the content router in the ISP network. The main function of a Layer 3 switch is to collect all users requests on a queue basis. The content router reads the header and tokenizes the data. If the request is a URL based request the content router sends the request to the Internet and continues to process the next request. If it is a registered service request, the content router finds a suitable server for processing the request based on the information given by the ISP server. The client s request is forwarded to the best appropriate server through the bypass router connected to the content router. The ISP server sends the processed request back to the client via the content router. The content router bypasses the re-processing of the processed results since they are from its ISP server. The response is sent back using different queuing strategies: (i) High Priority Queuing (HPQ), (ii) Low Priority Queuing (LPQ), and (iii) Unprocessed Queuing (UQ). The requests for registered services and their responses are sent through the HPQ. The ISP server sends the response to the content router, which sends it back to the Layer 3 switch that in turn forwards the response to the client. The URL response from the Internet to the content router is stored in the LPQ. The LPQ is processed only when the HPQ is empty. The remaining requests and responses are sent to the UQ (unprocessed queue). The UQ is processed when both the HPQ and LPQ are empty. However, we employ other mechanisms to avoid livelock / International Journal of The Computer, the Internet and Management Vol. 14.No.1 (January-April, 2006) pp

9 Sylvanus A. Ehikioya, Suresh Jayaraman, Jose A. Rueda starvation in LPQ and UQ. This network design model is efficient because the content router resides within the ISP network. In addition, the routing is cheap, quicker and efficient for the registered servers within an ISP network. Figure 8 shows a sample design model for wide area networks. The design consists of several clients, a client side content router, a server side content router, and servers with different databases on them. The client side content router is connected to the Internet. A server side content router has different servers connected to it. Each server has different databases on it. In addition to the two routers, there is a Gigabit Network connected to the server side content router and the client side content router. This design is well suited for a large company with many branches worldwide. When clients submit their requests, the content router usually captures the requests in the form of packets. The data in the packet is analyzed and tokenized. The content router forwards the packet with the tokenized data to the server-side router. The tokenized data sent by the client-side content router is read by the server-side router and finds an efficient server to process the user request. After processing the request, the server sends the response back to the server-side content router. While responding to the client-side content router, the server-side router labels each processed packet and forwards them to the Gigabit network for a quicker response from the server. The Gigabit network transmits the labeled packet to the client-side content router. The content router examines each packet for a label. If the packet is labeled the content router forwards the packet to the client without processing the packet. Otherwise the content router starts the processing of the packet. Figure 8 Sample Design for a Wide Area Network To avoid multiple processing, the processed packets are labeled. We used the Multiprotocol Label Switching (MPLS) mechanism to label each packet. The main advantage of using this method is to avoid heavy traffic on the Internet and process requests in an efficient and fast approach. The content router starts processing the packets without knowing the status of the packet whether it is processed or unprocessed. So when the content router captures a packet it looks for the label and forwards the packet to the client, thereby enhancing processing time. This design is efficient and fast because the response from the server is sent through a different path instead of the same forwarding path. Traffic is reduced and the time taken for processing each packet is minimized. 5. Implementation Results Many factors affect the performance of a router. Examples of such factors are network traffic, load on network nodes, and system configuration. A small change in any of these factors can drastically affect the performance of a router. To measure the performance of our content-based router, we performed test on various algorithms we implemented and recorded the time taken to complete execution in milliseconds. For each 16

10 Intelligent Content-Based Routing for Enhanced Internet Services test, we experimented with 20 different test cases and we took the average of the 20 cases to determine the time taken. The network used for the test has 12 different cluster nodes. We converted the cluster nodes as 12 different servers and we measured the time taken by each algorithm to complete its functionality. The algorithms were implemented in Java. The first test measured the Load Inspector algorithm. We start by finding the time taken to find load information for a node and we increase the number of server nodes incrementally, until all the 12 nodes are included. Figure 9 shows the graph for time taken to find the load on nodes versus number of different nodes. The load information of interest are: (i) percentage of free cpu, (ii) percentage of free memory available, and (iii) length of processor queue. In the graph, most of the processing time is spent on parsing the results of load details. Next, we measured the performance of the Proximity algorithm and System Status algorithm. These results we report here are for the traceroute and ping implementations used in our algorithm. Similar to the previous test, we start the test by finding the distance between the client and server for two nodes and we extend the test to 12 nodes with an interval of two. Figures 10 and 11 shows the performance for the proximity and system status algorithms, respectively. Time Taken to find Load Information (ms) Cluster of Server Nodes Figure 9 Results for Load Inspector Algorithm Time Taken to find Distance between client and server (ms) No. of Server Nodes Figure 10 Results for Proximity Algorithm The execution of the algorithm takes place in parallel, which reduces the execution time. The elapse time is spent on finding the size of the result vector that stores the distance between a client and server and the status of participating server. Time Taken to find server status (ms) No. of Server nodes Figure 11 Results for System Status Algorithm The initial overhead noticeable in the above graphs might be due to the time taken by Java to load the Java Virtual Machine, time it takes to load different libraries included in the program, as well as the time taken to handshake with other network nodes. 6. Conclusion In this paper, we presented the design and implementation of an intelligent contentbased router that finds a suitable server for processing a client s request quickly and efficiently. The increase in network traffic and load on different servers due to large user population demands the need for a new International Journal of The Computer, the Internet and Management Vol. 14.No.1 (January-April, 2006) pp

11 Sylvanus A. Ehikioya, Suresh Jayaraman, Jose A. Rueda efficient processing of requests. Our content router could potentially reduce network traffic and optimizes routing cost. By reducing the network traffic and optimizing the routing cost, the performance of the router increases. Also a key feature of the architecture is that the execution of operations in each of the components is done in parallel. The functionality of these components was explained in detail in Section 3. Based on the information obtained from these components users requests are forwarded to the appropriate server. This design is robust and fail-safe (see [18, 20] for the details). We conclude this paper by identifying some of the important issues to be addressed in extending this work. Our suggestions are based on our current design and the need to address the general problem of content-based routing with respect to Internet applications like e-commerce. We need to simulate the different network designs proposed in this paper to study their performance in order to obtain a most suitable design for the different applications. Using some standard optimization techniques, the different algorithms, such as load inspector, proximity and system status algorithms that were implemented could potentially be optimized to obtain a better performance. Another interesting direction for future work would be in the area of wireless access to resource data from remote locations. We need to assess the performance of the content-router in a wireless environment. In this paper, we did not consider security issues. If this factor were to be considered, the encryption and decryption of data that is passed in the network would have to be done. This is potentially one of the future works that is suggested because all the transactions done in an e-commerce application should be secured. The content router design proposed in this paper lays a solid foundation for future work on which differentiated e-commerce applications can be built. References [1] A. Asthana, C. Delph, H. V. Jagadish, and P. Krzyzanowski, Toward a Gigabit IP Router, Journal of High Speed Networks, Vol. 1, No. 4, 1992 pp [2] Cisco Systems Inc., "Content Routing Protocols, White Paper, Cisco Systems Inc, October 31, t/tech/ccrp_wp.htm. [3] V. Cardellini, M. Colajanni, and P. S. Yu, Geographic Load Balancing for Scalable Distributed Web Systems. Proc. IEEE Mascots 2000, San Francisco, CA, Aug./Sept [4] A. Demers, S. Keshav, and S. Shenker, Design and Analysis of a Fair Queuing Algorithm, Proceedings of ACM SIGCOMM 89, Austin, TX, USA, September [5] A. Duda and M. A. Sheldon, Content Routing in a Network of WAIS Servers, 14 th International Conference on Distributed Systems, Poznan, Poland, June [6] S. A. Ehikioya, Formal Specification of Intelligent Routing Infrastructure for Electronic Commerce Systems, Technical Report # TR-CS , Dept of Computer Science, U of M, Winnipeg, Canada, June [7] S. A. Ehikioya, S. Jayaraman and J. A. Rueda, Intelligent Content Routing Architectures for Distributed Applications, The 6 th World Multiconference on Systemics, Cybernitics and Informatics, Orlando, Florida, USA, July 14-18, [8] Z. Genova and K. Christensen, Challenges in URL Switching for Implementing Globally Distributed Web Sites. Proceedings of the Workshop on Scalable 18

12 Intelligent Content-Based Routing for Enhanced Internet Services Web Services, August 2000, pp [9] D. Ghosal, T. V. Lakshman, and Y. Huang, Parallel Architectures for Processing High Speed Network Signaling Protocols, IEEE / ACM Transactions on Networking, December 1995, pages [10] P. Gupta, S. Lin, and N. McKeown, Routing Lookups in Hardware at Memory Access Speeds, IEEE INFOCOM, April [11] G. Hunt, G. Goldszmidt, R. King, and R. Mukherjee, Network Dispatcher: A Connection Router for Scalable Internet Services, Proceedings of the 7th International World Wide Web Conference, Brisbane, Australia, April [12] S. Konstantindou, Segment Router A Novel Router Design for Parallel Computers, Proceedings of ACM SPAA-94, Cape May, N.J., USA, [13] V. P. Kumar, T. V. Lakshman, and D. Stiliadis, Beyond Best Effort: Router Architecture for the Differentiated Services of Tomorrow s Internet, IEEE Communications Magazine, 36(5), May 1998, [14] S. Keshav and R. Sharma, Issues and Trends in Router Design, IEEE COMMUNICATONS Magazine, 35(6), May 1998, [15] Craig Partridge et al., A 50-Gb/s IP Router, IEEE / ACM Transactions on Networking, Vol. 6 No. 3, June [16] V. Pai, M. Aron, G. Banga, M. Svendsen, P. Druschel, W. Zwaenepoel, and E. Nahum, Locality-Aware Request Distribution in Cluster-based Network Servers, Proceedings of the Eighth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS-VIII), San Jose, California, October [17] M. A. Sheldon, Andrzej Duda, Ron Weiss, James W. O Toole, Jr., and David K. Gifford, A Content Routing System for Distributed Information Servers, Proceedings Fourth International Conference on Extending Database Technology, March [18] S. Jayaraman, Intelligent Content-Based Routing for Enhanced Internet Services, M.Sc Thesis, Dept. of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada, October 30, [19] S. Jayaraman, S. A. Ehikioya, and J. A. Rueda, Network Architectures for Content- Based Routing, IEEE Canadian Conference on Electrical and Computer Engineering, Hotel Fort Garry, Winnipeg, Manitoba, Canada, May 12-15, [20] S. Jayaraman, S. A. Ehikioya, and J. A. Rueda, Content Router, - USA Patent Application - (File no: ADB) Oct 26, [21] J. Song, E. Levy-Abegnoli, A. Iyengar, and D. Dias, Design Alternatives for Scalable Web Server Accelerators, Proceedings of IEEE International Symposium on Performance Analysis of Systems and Software, Austin, TX, USA, April [22] J. Song, E. Levy-Abegnoli, A. Iyengar, and D. Dias, A Scalable and Highly Available Web Server Accelerator, IBM Research Report RC 21377, Shorter version appeared in Poster Proceedings of the 8th International World Wide Web Conference (WWW8), Toronto, Canada, May [23] V. Srinivasan and G. Varghese, Efficient Best Matching Prefix Using Tries, Pre Publication Manuscript, January [24] Marcel Waldvogel, George Varghese, Jon Turner, Bernhard Plattner, Scalable High Speed IP Routing Lookups, In Proceedings of SIGCOMM 97, September [25] /Understanding/ [26] utions/whitepapers/webns.html International Journal of The Computer, the Internet and Management Vol. 14.No.1 (January-April, 2006) pp