ATCM: A Novel Agent-based Peer-to-Peer Traffic Control Management

Journal of Computational Information Systems 7: 7 (2011) 2307-2314 Available at http://www.jofcis.com ATCM: A Novel Agent-based Peer-to-Peer Traffic Control Management He XU 1,, Suoping WANG 2, Ruchuan WANG 1, Min WU 1 1 College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China 2 College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China Abstract This paper presents a novel Agent-based peer-to-peer (P2P) Traffic Control Management mechanism (ATCM), which provides a new way for P2P traffic control and management. The realized control system s database records are transferred into XML documents forms, and the Java language and Aglet platform are used for development of mobile agent. Experimental results show that the control system can control the P2P traffic effectively, save network bandwidth, and ensure quality of service (QoS) of other critical business. Keywords: P2P; Traffic Control; Agent; Traffic Management 1. Introduction With the rapid development of Internet, P2P (Peer-to-Peer) technology has become very popular. It is used in many areas including file sharing, instant messaging, search engines, collaborative computing, and distributed computing. Comparing with the traditional centralized client/server (C/S) model, P2P has weakened the concept of the server. The P2P system no longer distinguishes the role of server and client relations of various nodes. Each node can be a server but also a client. In the P2P network, nodes can directly exchange resources and services without going through the central server. In a variety of P2P applications, P2P file sharing system has been occupied the largest proportion traffic of network bandwidth. Because P2P technology has been rapid spread and development, and it becomes the largest consumer of network resources more than the previous Web, E-mail, FTP and other data traffic, and becomes the main burden of the network, or even causes network congestion, impacting and reducing the performance of other businesses. According to the related statistics, P2P flow accounted for 70% of the total Internet traffic throughputs [1]. This paper studies the existing P2P traffic detection technology, an agent-based P2P traffic control model and method are proposed, and the experiment results show that the proposed model and method are availability and effective. Related work. In order to control P2P network traffic more reasonably, the first step is to identify the P2P traffic. Various solutions have been developed for P2P traffic classification in recent years. A popular Corresponding author. Email addresses: xuhe2046@126.com (He XU). 1553-9105/ Copyright 2011 Binary Information Press July, 2011

2308 H. Xu et al. /Journal of Computational Information Systems 7:7 (2011) 2307-2314 approach is the TCP port based analysis where tools such as Netflow[2] and cflowd[3] are configured to read the service port numbers in the TCP/UDP packet headers, and compare them with the known(default) port numbers of the P2P applications. Then the packets are classified as P2P traffic if a match occurs. Although P2P applications have default port numbers, newer P2P versions allow the user to change the port numbers, or choose a random port number within a specified range. Hence, port based analysis becomes inefficient and misleading. A method using application signatures was developed by S. Sen, O. Spatscheck, and D. Wang in [4], noticing the fact that internet applications have a unique string(signature) located in the data portion of the packet(payload). They used the available information in the proprietary P2P protocol specifications in conjunction with information extracted from packet-level trace analysis to identify the signatures, and classify the packets accordingly. In this case, the traffic that passes through the network is monitored and the data payload of the packets is inspected according to some previously defined application signatures. This approach has been shown to work very well for Internet traffic including P2P applications [5-6]. Nowadays, many researchers turn their attentions to machine learning based approaches for P2P traffic classification, in general it can be divided into two categories: Clustering Approaches, in which EM[7], AutoClass[8], K-Means[1,9] are typical ones; and Supervised Learning, in which naive Bayes[10 13], Bayesian Neural Network[14], Decision Tree[11] are most widely employed. In these researches the statistical characteristics of IP flows are concerned. Flow statistics, such as volume, duration and packet size, are extracted from the network data to establish the feature set. Traffic behavior analysis is another technique which passively checks the network packets and matches them with the pre-defined P2P traffic characteristics. Authors in [15] design a P2P traffic detection framework INFOPAD(Integrated Framework of P2P Traffic Detection) over large scale NetFlow data, which is based on traffic behavior analysis. This paper presents an efficient agent-based traffic control management scheme (ATCM) for P2P traffic to be controlled, which provides a new way for P2P traffic control and management. The system s database records are transferred into XML documents forms, and the Java language and Aglet platform are used for development of mobile agent. Experimental results show that the control system can control the P2P traffic effectively, save network bandwidth, and ensure QoS of other critical business. 2. ATCM: Agent based P2P Traffic Control Model Agent-based traffic control management model (ATCM) for P2P traffic Control is shown in Figure 1, which includes the Manager agent (MA), system information database, P2P flow monitor, information database, trace agent, traffic information collect agent, traffic control agent and agent run environment components. Agent-based computing is an effective paradigm for developing applications in complex domains as it supports the design and implementation of applications in terms of autonomous software entities, or Agents, which can achieve their goals flexibly by interacting with each another through high-level protocols and languages. Agents can also be endowed with a mobility feature which enables them to roam across the network, thus potentially saving network resources, improving performance, and enabling dynamic reconfigurability [16]. Architectures, protocols, and languages for Agents have been subjected to standardization by the Foundation for Intelligent and Physical Agents (FIPA) in response to

H. Xu et al. /Journal of Computational Information Systems 7:7 (2011) 2307-2314 2309 the interest of telecommunication companies for applying Agents for the intelligent management of networks [17]. Fig.1 Agent-based P2P Traffic Control Model (1) Manager Agent (MA): MA exchanges information with traffic information collect agent and determines whether to limit P2P traffic, if the traffic needs to be restricted then traffic control agent run P2P traffic control procedures. MA manages trace agent and system information database, and provides interfaces between system and network administrator. MA accumulates and assesses the information from the collect agent sent to collect all the information, if the P2P traffic flow information exceeds a certain preset value, MA will notify the traffic control agent to implement P2P traffic control. MA may reside on switch device in each local network. (2) P2P Flow Monitor: Monitor exists in each local network. Through monitoring each network s switch or routing device, it will find whether P2P traffic is existed in the network, and the monitoring results will be reported to MA. The method of P2P traffic identification can be used by the P2P traffic detection technology which is introduced in the section I s related work. (3) Traffic Information Collect Agent: It is mobile, and collects the target network with a variety of P2P-related information (such as IP, communicating port, the use of P2P protocols, etc.), and the results will be returned to MA. (4) System Information Database and Information Database: System information database is located in MA machine, which is used to collect and record all the information from the network, and will track each routing integrate all information of P2P connection. Information database is located in P2P traffic monitor for storing P2P control operation s log. (5) Trace Agent: After P2P traffic monitor device detects P2P traffic, P2P traffic monitor will release trace agent to track the location of each node in the network, and interact with the traffic control agent information in order to facilitate to implementation of the P2P flow control.

2310 H. Xu et al. /Journal of Computational Information Systems 7:7 (2011) 2307-2314 (6) Traffic Control Agent: Traffic control agent implements specific control strategies, such as restriction of the total P2P traffic, P2P flow rate, based on time stamp, etc, to ensure the QoS of all services in the management network. Fig. 2 P2P Traffic Control Flow Chart Figure 2 is the flow chart of mobile agent-based P2P flow control model. The P2P flow control process is as follows: Step 1. P2P Traffic Monitor opens monitor and detects each data link connection whether it is a P2P connection. Detection method is based on the previous section I which introduces the P2P traffic detection technology. Step 2. If it is the P2P connection, the P2P Traffic Monitor recognizes the connection information of the node (such as IP, communication port and other information) and puts the information into the database, and generates traffic information collection agent, and also transmits this information to MA. Step 3. Based on current network conditions, MA tracks and controls this P2P connection. If the connection does not been tracking then it will be released. If tracking then MA creates trace agent, and puts this connection information to the P2P flow control table, and notifies P2P traffic monitor to run real-time monitoring and controlling. Step 4. After P2P flow monitor receives MA notification information, P2P flow monitor real-time statistics P2P flow information and stores those information into the database, and makes a judge, when more than the limited value and it will notify the flow control agent, then closes this P2P connection, and transmits the related operation information and data to notify P2P traffic information collection agent.

H. Xu et al. /Journal of Computational Information Systems 7:7 (2011) 2307-2314 2311 3. Key Technology of ATCM Implementation ATCM uses Jpcap packet capture to analysis data, the Java language and Aglet to design Mobile Agent, and uses XML data access technology to store data. 3.1. Real-time Traffic Data Capture Technology P2P traffic monitor data capture module is the core of the implementation, and it is implemented by using Jpcap [18]. Jpcap is able to crawl and send a network packet by Java components, which can be used through a network interface to send any packets. Jpcap can capture Ethernet, IPv4, IPv6, ARP / RARP, TCP, UDP and ICMPv4 packets. Jpcap call wincap/libcap, and the Java language provides a common interface to achieve the platform independence. 3.2. Secure Mobile Agent Technology Aglet is a pure Java development with IBM for the mobile agent development technology, and provide a practical platform-- Aglet Workbench [19], allow people to develop or implement mobile agent system. It provides a simple and comprehensive programming model for mobile agent, and agent provides a dynamic and effective communication mechanism. It also provides a context to manage Aglet basic behaviors: such as create Aglet, copy or distribute Aglet to the destination machine, recall remote Aglet, suspend or wake Aglet, and remove Aglet and so on. These characteristics meet our proposed ATCM to achieve functional requirements. We use Java language and Aglet to implement mobile agent. 3.3. XML-based Database Access Technology System Information Database and Information Database need support layer for database, in order to be to effectively integrate with Java and the all solution of ATCM, we use XML-based database [20]. The advantage of using XML technology is that the data is exchangeability, and it has the following advantages of data applications: (1) XML file is a pure text file, no operating system and software platform restrictions; (2) XML is based on Schema, which is easy to describe the semantics of data, while the description can be understood and automatic processing by computer; (3) XML can describe structured, semi-structured and unstructured data. Figure 3 is the experimental network topology of the P2P traffic monitor system which is implemented according to the P2P flow control model. In the topology, Peer A and Peer B, Peer C and Peer D are belonging to a different network segment. In the experiment, Peer A and Peer C use P2P software (such as BitTorrent, emule, etc.) to download the same video compression package files, while Peer B and Peer D use FTP downloads and Web access operation simultaneously; P2P traffic monitoring server A opens monitoring, and P2P traffic monitoring server B closes monitoring. Deep Packet Inspection (DPI) technology [21] is used in the experiment on the P2P traffic information statistic. Experiment compares the download traffic of the router R1 and router R2, and the download rate of Peer A and Peer C. Performance results are shown in Figure 4 and Figure 5.

2312 H. Xu et al. /Journal of Computational Information Systems 7:7 (2011) 2307-2314 4. Experimental Results and Discussions As can be seen from Figure 4, the growth of download traffic of Router 1 which opens the P2P flow control is smaller than that of Router 2 which without open P2P flow control; As also can be seen from Figure 5, the download rate of Peer A is smaller than that of Peer C, because in the beginning 3 minutes of the start download time, while P2P clients stay in the stage of finding resources, the download rate of Peer A and Peer C are little difference, with the P2P client to find a lot of resources, after 3 minutes later, the download rate of the Peer C increases rapidly in the segment of not opening P2P flow control, and he download rate of the Peer A did not change significantly because it locates in the segment of opening P2P flow control. Fig. 3 Network Topology Fig. 4 Routers Download Traffic Fig.5 Peers Download Rate Experimental results show that the mobile agent based P2P traffic control system can achieve the purpose of controlling P2P traffic, make a lot of P2P traffic bandwidth down, ensure that other applications traffic would be enhanced, and so as to effectively guarantee quality of service (QoS) of other network applications.

H. Xu et al. /Journal of Computational Information Systems 7:7 (2011) 2307-2314 2313 5. Conclusions and Future Work This paper presents an efficient agent-based traffic control management mechanism (ATCM) for P2P traffic, which uses mobile agent s mobility, autonomy and intelligence, etc. Our scheme provides a new way for P2P traffic control and management. In the system s implementation, the database records is transferred into XML documents forms, and the Java language and Aglet platform are used for development of mobile agent. Experimental results show that the control system can control the P2P traffic effectively, improve the flow distribution in P2P network, save network bandwidth, and ensure quality of service of other critical business. As a result, ATCM eliminates the complexity of the original flow control, achieves a better control of P2P traffic, and the scheme is easy to be deployed in the Internet environment. However, because of P2P protocol s multiplicity, it is difficult to identification P2P traffic, thus how to detection P2P flow are the main work to do in the future work. Acknowledgement The subject is sponsored by the National Natural Science Foundation of P. R. China (60973139 61003039 61003236) Scientific & Technological Support Project (Industry) of Jiangsu Province (No. BE2010197,BE2010198) The Special Foundation for Development of Modern Service Industry of Jiangsu Province Project sponsored by Jiangsu provincial research scheme of natural science for higher education institutions (10KJB520013 10KJB520014) Scientific Research & Industry Promotion Project for Higher Education Institutions(JH10-14) Postdoctoral Foundation (20100480048 20100471353 1001006B) Science & Technology Innovation Fund for higher education institutions of Jiangsu Province (CX10B-196Z,CX10B-197Z,CX10B-198Z,CX10B-199Z,CX10B-200Z) the six kinds of Top Talent of Jiangsu Province (2008118) Doctoral Fund of Ministry of Education of China (20103223120007) and key Laboratory Foundation of Information Technology processing of Jiangsu Province (KJS1022). The authors would like to thank the editor and the anonymous reviewers, who provide insightful and constructive comments for improving this paper. References [1] J. Erman, A. Mahanti, M. Arlitt, et al. Identifying and discriminating between web and peer-to-peer traffic in the network core. Proceedings of the 16th international conference on World Wide Web(WWW 2007), pages 883-892, 2007. [2] S. Kamei and T. Kimura. Cisco IOS NetFlow Overview. Whitepaper, available at http://www.cisco.com. Cisco Systems Inc. 2006. [3] M. Crovella and B. Krishnamurthy. Internet measurement: infrastructure, traffic and applications. John Wiley and Sons Ltd.West Sussex, England, 2006. [4] S. Sen, O. Spatscheck, and D. Wang. Accurate, Scalable In Network Identification of P2P Traffic using Application Signatures. Proceedings of the 13th International World Wide Web Conference, pages 512-521, 2004. [5] P. Haffner, S. Sen, O. Spatscheck, and D. Wang. Acas:Automated construction of application signatures. In Proceedings of the SIGCOMM05 Workshops, 2005. [6] T. Karagiannis, A. Broido, M. Faloutsos and K. Claffy. Transport layer identification of P2P traffic. In Proceedings of the ACM SIGCOMM Internet Measurement Conference, pages 121-134, 2004. [7] A. McGregor, M. Hall, P. Lorier, et al. Flow clustering using machine learning techniques. In PAM 2004. Antibes Juan-les-Pins, France, April, 2004. [8] A. Zander, T. Nguyen, G. Armitage. Automated traffic classification and application identification using machine learning. In LCN 2005, Sydney, Australia, pages 250-257, 2005. [9] L. Bernaille, R. Teixeira, I. Akodkenou. Traffic classification on the fly. ACM SIGCOMM Comput Commun

2314 H. Xu et al. /Journal of Computational Information Systems 7:7 (2011) 2307-2314 Review, 36(2): 23-26, 2004. [10] A. W. Moore, D. Zuev. Internet traffic classification using Bayesian analysis techniques. In ACM SIGMETRICS 2005, pages 50-60, 2005. [11] J. Park, H. R. Tyan, C. Kuo. Internet traffic classification for scalable QoS provision. In 2006 IEEE International Conference on Multimedia and Expo. Toronto, Ontario, Canada, pages 1221-1224, 2006. [12] T. Nguyen, G. Armitage. Training on multiple sub-flows to optimize the use of Machine Learning classifiers in real-world IP networks. In LCN 2006, Tampa, Florida,USA, pages 369-376, 2006. [13] D. Bonfiglio, M. Mellia, M. Meo, et al. Revealing Skype traffic: when randomness plays with you. In SIGCOMM 07. New York, NY, USA, August. pages 37-38, 2007. [14] T. Auld, A. W. Moore, S. F. Gull. Bayesian neural networks for Internet traffic classification. IEEE Trans Neural Netw, 18(1): 223-239, 2007. [15] R. Zhang, J. Chang, H. Zhou, et al. P2P traffic detection on large scale netflow data. Journal of Computational Information Systems, 4(2): 443-448, 2008. [16] M. Luck, P. McBurney, C. Preist, A manifesto for agent technology: Towards next generation computing. Autonomous Agents and Multi-Agent Systems, 9 (3):203-252, 2004. [17] G. Fortino, W. Russo. Using P2P, GRID and Agent technologies for the development of content distribution networks. Future Generation Computer Systems, 24:180-190, 2008. [18] Z. H. Shen, H. Wang. Network Data Packet Capture and Protocol Analysis on Jpcap-Based. Proceedings of the 2009 International Conference on Information Management, Innovation Management and Industrial Engineering, USA: IEEE COMPUTER SOC, 3: 329-332, 2009. [19] D. B. Lange, D. T. Chang. IBM Aglets Workbench Programming Mobile Agents in Java. White Paper, Japan: IBM Corporation, 1996. [20] B. Mclaughlin. Java & XML, 2nd Edition. O'Reilly Media, Inc., 2006. [21] S. Sen, J. Wang. Analyzing Peer-to-Peer Traffic Across Large Networks. IEEE/ACM Transactions on Networking, 12(2): 219-232, 2004.