International Journal of Software Engineering and Its Application Vol. 2, No. 1, January, 2008
New Algorithm for Effective Utilization of Bandwidth for Sensitive Applications G.Varaprasad† Department of Computer Science and Engineering, B.M.S. College of Engineering, Bangalore-560019, India. R.S.D. Wahidabanu Department of Computer Science and Engineering, Government College of Engineering, Salem-636036, India. P.Venkataram Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore-560012, India
Abstract In this paper, we propose an algorithm, which allocates the bandwidth in an effective manner for sensitive applications. The main idea of this work is to allocate more amount of bandwidth for particular client, who has received more votes in the network. The proposed model reduces the wastage of bandwidth and increases the throughput. Nomenclature N=Number of clients B=Blocking probability CIRi,j=Committed Information Rate(CIR) for connection i on channel j Access Rate j=Client access data rate on the channel j
1. Introduction The rapid growth in demand of bandwidth has led to intensive research work towards new generation of multimedia applications[1-3]. New system must be able to provide QoS and supports wide range of services, while improving the system capacity. The effective utilization of bandwidth for the multimedia applications with QoS is certainly one of the major challenges in further generation networks[4]-[6]. The transmission of sensitive applications is required more amount of bandwidth from the server to client[7]. A large number of flows required many states, which could increase the communication overhead. In order to reduce communication overhead, the system co-ordinates various states and makes the system in stable[8-10]. This paper proposes a voting model that allocates the resources for multiple users. Rest of the paper is made as follows. Section 2 presents proposed model and it needs. Simulation of our model is presented in section 3. Section 4 presents the results of proposed model. Conclusions are discussed in section 5. † Correspondent Author: G.Varaprasad, Dept of Computer Science and Engineering, B.M.S. College of Engineering, Bangalore-560019, Ph: 91-080-26614357, Fax: +91-080-26614357,
[email protected]
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International Journal of Software Engineering and Its Application Vol. 2, No. 1, January, 2008
2. Proposed Model Voting method provides a fair bandwidth allocation for multiple users as shown in Figure1. It is used in core network, where ten to thirty of users are presented at any given time. If the server receives more votes from different clients on a particular client, then the scheduler allocates more resources to a particular client.
Figure 1. Proposed model. Algorithm 1. Resource allocation Begin when a new request arrives at time t from the clients for i=1 to n (number of clients) if no other requests is currently being served then assign the bandwidth to current client else identify the number of clients is requested for channel if popular client has received more votes then it allocates bandwidth to popular client else it allocates bandwidth for others else reject the request endif endif End If the clients are independent, then the probability of nth client in the system is given by 10 (o.1n) (0.910-n ) n The probabilistic performance depends on admission control algorithm has known as static behavior of the client. It is very important during the period of network congestion. To provide a fair resource allocation, proposed model uses concept of CIR. All the connections of end systems attached to server shouldn’t exceed the capacity of server. In addition, the aggregate of CIR should not exceed the physical data rate across user-network interface. It has known as access rate and this limitation is imposed by ΣCIRi,j≤Access rate j and blocking probability is given by:
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International Journal of Software Engineering and Its Application Vol. 2, No. 1, January, 2008
3. Simulation The simulation model has considered 1server and 70clients. The following assumptions have taken in our model. The client requests for application are randomly distributed. The size of packet is fixed. Summary of simulation parameters is shown in Table I.
Table I. Simulation parameters. Number of clients Number of servers Channel capacity Simulation duration Packet size Packet type Simulation duration 4.
70 1 2Mbps 2,000 sec 1024 CBR 4000s
Simulation results
The average of simulations has been conducted in both control and un-control scheduling algorithms. In the un-control scheduling, the throughput is very low, due to its principle as illustrated in Figure 2. When the load increases, the network performance has come down as compared to control scheduling.
Figure 2. Network load versus throughput. Probability of n clients out of 12 are simultaneously active, if they are uncorrelated, then each client has 0.1 probability as shown in Figure 3.
Figure 3. Probability deceases exponentially with n.
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International Journal of Software Engineering and Its Application Vol. 2, No. 1, January, 2008
The bandwidth is always used irrespective of number of applications in the network. The scheduler is provided more amount of bandwidth for end-users based on user requirements. The bandwidth is never wasted in our proposed model as shown in Figure 4.
Figure 4. Bandwidth utilization. Time versus number of multimedia packets is shown in Figure 5. The proposed model transmitted more multimedia packets, due to voting.
Figure 5. Time against multimedia packets.
5. Conclusions In this paper, we focused on bandwidth allocation for sensitive applications. The proposed model worked based on the number of votes received per client to allocate bandwidth. The simulation of this algorithm has shown good throughput. But drawback of this algorithm is that we have not considered packet loss.
6. References 1. 2. 3. 4. 5. 6. 7.
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B.G.Evans, K.Baughan(2002), “Visions of 4G”, Electronics and Communication Engineering Journal, 12(6), pp.293-303. J.M.Pereira(2000),“Fourth Generation: Now, It is Personal”, In Proc.of PIMRC, pp.1009-1020. H.Shachnai,P.S.Yu (1998), “Exploring Wait Tolerance in Effective Batching for Video-on-demand Scheduling”, ACM Multimedia Systems Journal, 6: pp.382-394. S.Keshav, An Engineering Approach to Computer Networking, Pearson Education, India. A.Yamashita,R.Kawamura and H.Hadama(1995), “Dynamic VP Rearrangement in an ATM Network”, In Proc.of IEEE GLOBECOM, pp.1379-1383. Yahara,T,Kawamura,R.,Ohta, S(2000), “New Self Healing Scheme that Realizes Differentiated Bandwidth Requirements on ATM Networks”, IEEE Trans.Commun, E83-B(3), pp.672-679. E.W.M.Wong et al(1996), “Bandwidth Allocation and Routing in Virtual Path Based ATM Networks”,In Proc.of IEEEICC/SUPERCOMM, pp.647-652.
International Journal of Software Engineering and Its Application Vol. 2, No. 1, January, 2008
8.
S.S.Manvi and P.Venkataram(2002), “QoS Management by Mobile Agents in Multimedia Communication”, In Proc.DEXA, pp.407-411. 9. S.S.Manvi and P.Venkataram(2001), “Mobile Agent based Online Bandwidth Allocation for Multimedia Communication”, In Prec.of IEEE GLOBECOM, pp.2622- 2625. 10. Santosh Kulkarni(2003), “Bandwidth Efficient Video on Demand Algorithm”, In Proc.of International Conference on Telecommunications, vol.2, pp.1335-1342.
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