Network Traffic Management using Dynamic ...

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Networks (CDNs) and mobile Internet usage. With the ... P. G. Department of Computer Science, Utkal University, Vani Vihar, Bhunaneswar, Odisha, India.
International Journal of Computer Science and Information Security (IJCSIS), Vol. 15, No. 6, June 2017

Network Traffic Management using Dynamic Bandwidth on Demand P. C. Sethi, P. K. Behera P. G. Department of Computer Science, Utkal University, Vani Vihar, Bhunaneswar, Odisha, India 

the network. Remote device configuration, network performance monitoring, network resource usage verification and network fault detection are the major responsibilities of SNMP. A third-party SNMP management software or a user defined SNMP management software can be used for network management.

Abstract— Traffic Analysis and measurement in large networks is very challenging task for network managers. Bandwidth plays a vital role during network traffic analysis and management. Bandwidth allocation becomes a critical issue for effective network management. Bandwidth on demand concept gradually evolved while addressing the need of network managers for monitoring on-demand traffic. Use of efficient bandwidth allocation algorithm significantly improves network performance by assuring availability of network to all users. In this paper, we propose an optimized algorithm using the concept “rating of web pages”, which is based on users’ past accessibility. This algorithm assigns a minimum guaranteed bandwidth to each connected user, instead of equally dividing the total available bandwidth among the users. Finally, based on rating of web pages, any excess bandwidth is distributed dynamically among existing users. This significantly improves the average utilization of available bandwidth.

This research paper deals with a diverse research interests that focuses on Traffic Engineering (TE) based Software Defined Network (SDN) for network traffic monitoring, network traffic measurement and management for efficient processing as compared to traditional processing. SDN is a way to deal with network organization that permits the network managers to manage the system based on abstract lower-level functionality. Since the static design of traditional network doesn't bolster the dynamic, versatile figuring and capacity needs of more advanced processing situations, SDN idea is utilized by the various data center. This is accomplished by decoupling or disassociating the framework that settles on choices about where network traffic is available. SDN is commonly associated with the OpenFlow protocols.

Index Terms— Network Traffic classification, Software Defined Network, BoD, SeLeCT, Load balancing, Incremental clustering.

I. INTRODUCTION In the current scenario, almost all business applications are being carried out over Internet. Online businesses increasingly rely on Internet for its basic operations. Along with increase in the complexity of Internet services, there is drastic increase in Content Delivery Networks (CDNs) and mobile Internet usage. With the growth of technology along with increase in users, complexity will continue to increase in the future. According to survey done by CISCO in 2016, nearly 40% of the world population has Internet connection which was less than 1% in 1995. Hence there is a high demand for Internet traffic management.

To begin with, we propose a reference system for TE in SDN based on page rating. It comprises of two sections, such as, network traffic estimation and network traffic administration. Network traffic was estimated by monitoring the real network and breaking down the system into different activities. Network traffic estimation is the prerequisite for traffic administration. Network traffic measurement and forecasting is the fundamental requirement in the network traffic management. Traffic load balancing, guaranteed scheduling of network information are the related fields of network traffic management. Here, network traffic management using web page rating was proposed for improving the quality of service.

Traffic Engineering (TE) deals with the measurement and management of network traffic to designs optimized network traffic for routing and improving network resources utilization. When number of user increases, it causes bottleneck problem in accessing the network. Passive network is an effective solution to the bottleneck problem in accessing

The rest of this paper is organized as follows: Section 2 presents the literature overview related to network traffic management, section 3 describes the proposed work, section 4 contains the proposed algorithm implemented using page rating, section 5 deals with the experimental result and section 6 provides performance of the algorithm and section 7 provides the conclusion. Section 8 contains the future scope of the research work.

P. C. Sethi is Ph.D scholar and working as Senior Research Fellow (SRF) in Department of Computer Science and Appications, Utkal University, Vani Vihar, Bhubaneswar, Odisha, India. The author can be reached over e-mail: [email protected]. P. K. Behera is working as Reader (Associate Professor) in Department of Computer Science and Appications, Utkal University, Vani Vihar, Bhubaneswar, Odisha, India. The author can be reached over e-mail: [email protected]. This work is supported under UGC grant RGNF-2013-14-ORI-49267. 369

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oriented centralized traffic management instead of a distributed approach. Network Organization is done for efficient system accessibility and performance enhancement. We had proposed a Sensible network traffic management approach for dynamic data processing, traffic management and load balancing to enhance QoS of network.

II. LITERATURE OVERVIEW Network traffic may occur due to the exponential growth of Internet user and limited availability of various Internet resources. According to Cisco, the global smartphone traffic will increase by ten folds by 2019 [figure-1].

SDN enables network traffic engineers to select appropriate path out of various available paths between pair of nodes participating in network. The SDN controller keeps up worldwide perspective of present utilization of every way in system utilizing different network traffic parameters. We had proposed a network traffic management algorithm for dynamic streaming of data, traffic management, load balancing, and efficient QoS. Various dynamic bandwidth allocation techniques are widely studies in literatures [1-5]. In [1], the authors proposed a probabilistic sampling approach for efficient traffic flow control. Flow-based analysis was applied that reduced the high volume of network traffic by dividing it into flows generators. Flow-based analysis detection and monitoring of traffic was applied for distribution of traffic flow uniformly. Since flowbased analysis provides poor performance, so monitoring technique was applied for traffic analysis and concluded that flow-based monitoring technique provides efficient traffic management than traditional approach.

[Fig-1: Increase in Traffic Chart by Smart Phone users] Millions of users relay on various broadband connections. The ratio of bandwidth supply by different broadband service providers are represented in figure-2.

In [2], the author proposed a method using link dimension for traffic monitoring. Link dimension is used to calculate packet level measurement and deploy packet sampling technique for traffic monitoring. Three packet sampling techniques such as Bernoulli sampling, n-in-N sampling and sFlow sampling was done and concluded that packet sampling have no negative impact based on sampling rate and packet sampling. The accuracy of system remains unaffected even for too short timescales such as 10 ms using large dataset around the world.

[Fig-2: Ration of supply bandwidth by different Broadband Service Providers]

In [3], the author proposed a novel technique for precise and competent stream oriented latency calculation using load balancing for traffic management. The latency was measured based on packet size according to the capacity of network without involving any time stamping or inquiry packets. A new approach called COLATE (Counter based Perflow Latency Estimation scheme) was applied that adds noise for storage space minimization. Using a statistical approach, packets are denoise to get that actual latency. For secured implementation, single hashing along with single memory update was applied in COLATE for each packet. COLATE utilized less than 0.1 bits per packet. So, the connection can accommodate nearly million packets per second. Accordingly, a single 1 TB drive can be used to store timestamp for more than 6 years COLATE timestamp data connections. Three types of network traffic traces were considered such as backbone, enterprise, and server traffic for efficient analysis of the proposed system.

Traffic engineering (TE) deals with the study of network traffic analysis and measurement. Efficient routing mechanisms are proposed by network traffic engineers to reduce network resource utilization, control network traffic and enhance network quality of service (QoS). A Software Defined Network (SDN) is a new technique of traffic engineering which works in two layers such as forwarding and controlling layer of network system. The administrator of system can perform forwarding to enhance the ability of network system by appropriate task specification. In comparison with traditional network traffic management, SDN has many points of interest to bolster TE for globalized, fast programmability of network system processing. Data layer traffic and control layer traffic are two major categories of network traffic which affects performance of network system. The data layer traffic uses load balancing concept for network traffic management. Contrasted with the customary system, the primary favorable advantage of load balancing in SDN is that it allows a centralized; stream

In [4], the authors proposed an automated network protocol identification approach for traffic classification. A 370

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secured semantic trace based information system was applied for traffic classification. It does not need any prior knowledge of protocol specification rather frequency rank distribution concept was applied for management of network traffic. It supports both connectionless as well as connection oriented protocols for both short and long flow of data. The average accuracy of recall is nearly 97.4% with precision nearly 98.4%.

In general, each user is assigned with equal bandwidth irrespective of application. This leads to wastage or insufficiency of bandwidth. Due to the above reason, a frequency distribution mechanism is applied which divides the available bandwidth according to the rating of web page i.e. the web page which have more rating will be assigned with higher bandwidth.

In [5], the authors had proposed an efficient algorithm to improve network quality of service using dynamic bandwidth allocation. In a network, each node is assigned with equal bandwidth which was not utilized properly. Taking the traffic conditions into consideration, the algorithm was proposed to provide a guaranteed access and utilize the bandwidth properly. The resources are allocated following load balancing condition. In [6], the authors proposed an approach for a dynamic environment based on clustering. The dynamic environment was defined as a zero-configuration system i.e. any type of device can participate in the networking system using plug-and-play concept for improving the quality of service. [7, 8] involves a rank based clustering. [7] used click stream approach for ranking of the pages, accordingly the clusters are created. The authors guaranteed 100 percent data transmission, but the time of processing is not considered. [8,9] provided secured and faster searching approach based using GFGS (Generalized Frequent Common Gram) technique by SeLeCT (Self Learning Classifier) following self-seeding approach on that involves less on-chip memory for processing. In [10], the authors provided a brief comparison of various security algorithms. In [11], the authors provided a more secured approach using RSA algorithm that involves same processing time but with increased security of data and [12] contains the description of SeLeCT algorithm.

Bandwidth for a network can be calculated in two basic steps: 1. Total available bandwidth calculation. 2. Calculation of required bandwidth for specific application based on parameter.

3. 1. Bandwidth Computation for Network

If the network is Giga bit Ethernet, then it will support 125,000,000 Bps (considering, 1000 Mbps for a Gigabit network). Based on number of user and their type of application, bandwidth needed for each application is to be determined. According to numbers of bytes transferred per second, network analyzer detects the bit rate for network. Cumulative Bytes needed are calculated by the network analyzer, and then traffic is captured from a test workstation. According to network traffic generated by each user, bandwidth is assigned to each user dynamically. Number of users and type of application will affect the aggregated efficiency of the system. The following research work for classification of traffic is based on three basic fields defined as Classification of traffic according to user rating for the movieId, SeLeCT algorithm [12] and bandwidth allocation according to rating. The Internet traffic clustering is done following rating of movies of movielens dataset. A Class-Based Weighted Fair Queuing (CBWFQ) model is considered for clustering of dataset items as well as bandwidth allocation dynamically. Required bandwidth differs from network to network and application to application. A minimum bandwidth called Minimum Guaranteed Bandwidth (MGB) is initially assigned to each user. Based on probability distribution of web page ratings, available bandwidth will be distributed among rest of users. A queuing system provides a load balancing during congestion conditions.

III. PROPOSED WORK The data rate reinforced by a network is called bandwidth. Bandwidth is calculated as difference between highest and the lowest frequency supported by a network. Generally, bandwidth is expressed in terms of bits per second (bps) OR bytes per second (Bps). The theoretical bandwidth distribution and real-world bandwidth always differs. For example, theoretically Gigabit Ethernet network supports 1,000 Mbps bandwidth, but in practical this can’t be achieved due to the overhead of hardware and system software. Hence bandwidth calculation becomes a challenging task for network traffic managers. Allocation of bandwidth depends on many parameters such as type of application running, service level agreement, hardware performance used for implementation. Most of the time, network managers only consider number of user involvement as the major parameter for traffic management, but instead of number of user involved, the actual work done by user will affect network performance during traffic analysis. For example, in a group of 100 users in network, each user doesn’t utilize network equally; few user leads to bottleneck problem to the network. So, the traditional client server distribution of bandwidth will lead to performance degradation.

The implementation will be done using MovieLens (http://movielens.org) dataset. MovieLens is an outcome of a movie reference facility. It contains ratings of movies by random users between 1-5 according to their preference. The whole dataset consists of 20000263 ratings with 465564 tag values for 27278 number of movies. The dataset was created by collecting ratings of 138493 users between 9th January 1995 and 31st March 2015 and was modified on 17th October 2016. Clients were chosen randomly for consideration. Every client had evaluated not less than 20 films. No statistical data is incorporated. Every client is identified by a unique userId, and no other data was considered. SeLect algorithm was applied to cluster dataset. SeLeCT stands for Self-Learning Classifier. It is one of the efficient algorithms used for Internet Traffic examination. SeLeCT is 371

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an unsupervised algorithm based on self-seeding approach for automatic traffic classification. It doesn’t involve any prior knowledge of environment or grouping of data. It provides nearly 98% perfectness of traffic classification during network administration. The data automatically switches between clusters due to adoptive seeding approach. Based on type of clusters, prediction for data was done. Development of information and growth of group size is given in figure–3.

the time, actual bandwidth allocated is less than theoretical bandwidth. Considering the minimum guaranteed bandwidth as constant, the whole research was implemented. The minimum guaranteed bandwidth (Bmin) can be calculated as: ()

(

)



Where, αi - the weight factor (rating) for each web page Tcycle - maximum transmission cycle needed for each web page N - total number of users accessing the Internet Tguard - guard time between two consecutive access R - transmission rate (both upstream and downstream). The following parameters are variable for individual user. The algorithm for dynamic bandwidth on demand service according to rating of web pages is: Step-1: Calculate available bandwidth and total number of user (N). Step-2: Clusters are made according to the weights (ratings) by CBWFQ following an incremental approach dynamically. Step-3: Initialized the queue depth for storing various packets which are to be stored in a cluster earlier to the drops out of packet (By default, queue depth is set as 64 which is maximum length of queue). Step-4: If any traffic doesn’t match with any cluster, then it is assigned to one of default cluster. When more parameters (restrictions) are assigned, data will switch to appropriate cluster. It is maintained using a normal Weighted Fair Queuing. Step-5: Find total bandwidth available. Apply Minimum Guaranteed Bandwidth (MGB) to each user. Step-6: Calculate excess bandwidth available. Excess bandwidth = Total available bandwidth – N × MGB Step-7: Excess Bandwidth to be assign = (Excess bandwidth/ N) × (Current rating/Maximum rating) Step-8: IF (End of Useri) Then Release the allocated resources Goto Step-6 ELSE IF (New User) Then Allocate Bmin to New User Goto Step-6 EndIF Step-9: Stop

[Figure-3: Movements & enlargements of a window] The whole process for dynamic assignment of bandwidth on demand for network traffic management is represented using following flow chart [figure-4].

V. EXPERIMENTAL RESULT The algorithm was implemented using MatLab-13 in Intel core i3 2.20GHz speed processor, 8 GB RAM. Movielens dataset was used for implementation of proposed algorithm. The pareto

distribution chart for a standard Movielens dataset was represented in figure-5. It provides standard distribution of movies based on UserId and Movie rating. Implement of proposed algorithm was done by considering first 100 users of data set. 2230 movies information (tuples) are considered for such implementation.

[Figure-4: Flowchart DBoD for network traffic management] IV.

PROPOSED ALGORITHM Though actual implementation was done using 2230 information but for simplicity, first tuple for each user is represented in the table and graph showing comparison among required bandwidth and proposed bandwidth in figure-6.

Calculation of minimum guaranteed bandwidth is too difficult task for traffic engineers because the theoretical bandwidth and the actual bandwidth assigned differ. Most of 372

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Figure-6 [Comparison Graph of Required bandwidth and Proposed bandwidth]

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The table showing the comparison among the old and new bandwidth is assigned for each user represented (considering the first element of result) is given in table-1.

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expert users. With the use of proposed algorithm, the total available bandwidth is distributed dynamically among different types of Internet users based on their needs. Thus, the optimized utilization of bandwidth was carried out efficiently among different users.

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The above research work is implemented using rating of web pages as parameter. Higher rating web pages are assigned higher bandwidth. We have not taken into consideration the priority of user or the vitality of information which can also play an important role in decision making process about the dynamic allocation of bandwidth for the rated pages. These parameters can also be considered for more effective assignment of bandwidth in a dynamic environment.

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REFERENCES

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Zahra Jadidi, Vallipuram Muthukkumarasamy, Elankayer Sithirasenan, and Kalvinder Singh, ―A Probabilistic Sampling Method for Efficient Flow-based Analysis‖, JOURNAL OF COMMUNICATIONS AND NETWORKS, VOL. 18, NO. 5, OCTOBER 2016, P-818-825. [2] Ricardo de Oliveira Schmidt, Ramin Sadre, Anna Sperotto, Hans van den Berg, and Aiko Pras, ―Impact of Packet Sampling on Link Dimensioning‖, IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, VOL. 12, NO. 3, SEPTEMBER 2015, p392-405 [3] Muhammad Shahzad and Alex X. Liu, ―Accurate and Efficient Per-Flow Latency Measurement Without Probing and Time Stamping‖, IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 24, NO. 6, DECEMBER 2016, P3477-3492 [4] Xiaochun Yun, Member, IEEE, Yipeng Wang, Member, IEEE, Yongzheng Zhang, Member, IEEE, and Yu Zhou, Member, IEEE, “A Semantics-Aware Approach to the Automated Network Protocol Identification”, IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 24, NO. 1, FEBRUARY 2016, P583-595 [5] P.C. Sethi, P.K. Behera, ―An efficient dynamic bandwidth allocation algorithm for improving the quality of service of networks‖, European Journal of Academic Essays, Special Issue (1), 2014, P31-35. [6] P. C. Sethi, ―UPnP and Secure Group Communication Technique for Zero–configuration Environment construction using Incremental Clustering‖, International Journal of Engineering Research & Technology (IJERT), Vol. 2 Issue 12, December – 2013, ISSN: 2278– 0181, pp. 2095–2101 [7] P. C. Sethi, C. Dash: ―High Impact Event Processing using Incremental Clustering in Unsupervised Feature Space through Genetic algorithm by Selective Repeat ARQ protocol‖, ICCCT– 2nd IEEE Conference – 2011, pp. 310–315. [8] P. C. Sethi, P.K. Behera, ―Secure Packet Inspection using Hierarchical Pattern matching implemented Using Incremental Clustering Algorithm‖, December–22–24, ICHPCA–2014 (IEEE International Conference) [9] P. C. Sethi, P. K. Behera, ―Internet Traffic Classification for Faster and Secured Network Service‖, International Journal of Computer Applications (IJCA), Volume 131 – No.4, December2015, pp. 15–20 [10] P. C. Sethi, P. K. Behera, ―Methods of Network Security and Improving the Quality of Service – A Survey‖, International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE) Volume 5, Issue 7, July 2015, pp. 1098–1106 [11] P. C. Sethi, P. K. Behera, ―RSA Cryptography Algorithm Using linear Congruence Class‖, International Journal of Advanced Research (2016), Volume 4, Issue 5, 1335-1347 [12] Luigi Grimaudo, Marco Mellia, Elena Baralis and Ram Keralapura, ―SeLeCT: Self-Learning Classifier for Internet Traffic‖, IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, VOL. 11, NO. 2, JUNE 2014 (P144 – P157)

100 480 3 0.448833 0.380061 Table-1 [Comparison table of old and new bandwidth] The graph presented in firure-7 depicts comparison between the earlier method and the proposed method showing utilization of bandwidth. This graph contains 2230 tuples of the dataset. It is evident from the graph that the proposed method results in optimized utilization of bandwidth. VI. PERFORMANCE OF ALGORITHM The minimum guaranteed bandwidth is assigned to each user who participated in the network, which is essential to satisfy the basic bandwidth requirement. The calculated excess bandwidth is distributed among the users having higher rating. Considering the higher rating items as high demanded web pages, a frequency distribution technique was applied for bandwidth on demand (using SeLeCT). By application of this algorithm, any wastage or insufficiency of bandwidth was managed effectively. Hence, the efficiency of the overall system increases significantly as compared to earlier system. VII. CONCLUSION Due to the exponential growth of Internet users, the limited bandwidth has to be utilized efficiently. Growth of Internet users highly increases traffic over the network, which puts more responsibility on traffic engineers for controlled network management. Since each user doesn’t need equal bandwidth for their application, most of the time there is either wastage of bandwidth or insufficient bandwidth for demanding users. Allocating bandwidth on demand based on rating of web pages provides a solution for controlling wastage of bandwidth for naive and insufficient bandwidth for 374

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Er. P. C. Sethi received the B. Tech and M.Tech degrees in Information Technology Engineering and Computer Science Engineering from College of Engineering & Technology, Bhubaneswar. He has qualified UGC-NET three times in Computer Science and Applications. He is currently pursuing PhD in P.G. Department of Computer Science at Utkal University, Odisha, India. His current research area of interest is Network Security and QoS. He has published five research papers in refered international journals and two IEEE conference paper. He is a life time member of CSI, ISTE, IAENG, CSTA.

Dr. P. K. Behera is currently working as Reader at Department of Computer Science, Utkal University, Bhubaneswar, Odisha, India. He has more than two decades of teaching experience. His area of interest is MANET, Wireless Network, Distributed Systems, Mobile Computing, Network and Information Security, Software Engineering. He has published number of research papers in reputed International Conferences and Journals. He is a reviewer of many national and International referred Journals. He is the Secretary of CSI Bhubaneswar Chapter.

Fig – 5: Pareto Distribution Chart for Movielens dataset

[Fig-7: Comparison Graph of Old and New bandwidth needed]

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