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3 significantly different from single path routing but routing on more than two paths is not very different from routing on two paths [8]. Therefore two paths are selected at the destination to simplify the algorithm Evaluation of service classes When a probing packet passes through the path, the availability of service classes at each router should be determined. In order to avoid oscillation which can be caused by bursty traffics, exponentially moving average (EMA) equations are used to average the incoming data rate and buffer length in each router for all classes [14]. All calculations of EMA equation are done in the routers and there is no need to be kept in the probing packets. τ sj (t) r sj(t) = (1 e k ) l sj (t) τsj (t) + e k r sj,old(t) (1) τ sj (t) sj : service class r sj(t) : The average arrival data rate l sj (t) : The length of the received packet at time t τ sj (t) : The period between the current time t and the reception time of the previous packet r sj,old(t) : The last updated average data rate k : a constant value (0.01) τ sj (t) τ sj (t) L sj (t) = (1 e k ) L sj (t) + e k L sj,old (t) (2) L sj (t) : The average of buffer length L sj,old (t) : The last updated average buffer length L sj (t) : Buffer length at time t Since the measurement of r sj(t) and L sj (t) are done in each packet arrival with the cooperation of routers along the path, the obtained performance of service classes are the reflection of real network state over the time. In the proposed algorithm, r sj(t) and L sj (t) are three dimensional arrays that are updated according to the arrival of three service classes. EMA equations consist of two parts: one part is related to current observation and the other part is related to past observations. If current observations are much bigger than previous ones, the equations cannot filter the transient effect of bursty traffics. On the other hand, if previous observations are much bigger than current ones, EMA equations converge slowly to the real value of traffic. k is a constant that controls the behavior of the bursty traffics. The calculation of the acceptable bounds of k in a similar algorithm is introduced in [10]. It is assumed that buffer lengths of different service classes are not the same in each router but buffer lengths of similar classes are the same in all routers. 3

4 Weighted fair queuing (WFQ) is used as the packet scheduling algorithm while other algorithms are also acceptable. WFQ can guarantee delay limitation and fairly assign resources among different service classes. The incoming traffic rate is compared to the maximum allowable bandwidth allocated to the requested service class in WFQ. If the arrival rate is lower than the allocated rate, then the traffic will be accepted otherwise the lower service classes are considered Decision function and load balancing parameter End-to-end delay or packet loss probability alone cannot provide a good estimation of real network congestion state for traffic splitting [9]. Buffer length is used in this paper to estimate the congestion state of the paths instead of both end-to-end delay and packet loss probability. Remaining buffer space for each class of service is calculated as follows: B sj = L sj L sj (t) L sj : The maximum buffer length of service class s j As the probing packet goes through the path, the remaining buffer spaces of all classes in each router are compared to the values of previous router and the bottleneck of the network is detected and inserted in the probing packets. After obtaining the information of each probing packet at the destination, remaining buffer space of the three service classes of the most crowded router in all path are added together. Sum(i) = B EF (i) + B AF (i) + B BE (i) Sum(i): Summation of the remaining buffer space of three service classes in the most crowded router of path i Sum(i) is chosen as a criterion to select the most proper paths; two paths with the most amount of Sum(i) are selected. Also it is used as a limiting factor for sending traffic through the network and the incoming traffic will be divided on the two previously selected paths according to the rates calculated in equation (5) and a KP producing uniform random numbers as a traffic splitter. Rate Path (i) = Sum(i) i Sum (i) 3. Simulation results The proposed algorithm is implemented in a network topology shown in Fig. 1 that is composed of one pair of source-destination, seven routers and 4.5 Mbps connecting link. OPNET modeler is used for the evaluation of the proposed algorithm [15]. Equation (1) and (2) are updated each time a probing or data packet is received. These two equations are written in all routers with state variables arrays indicating r sj(t) and L sj (t) for three service classes. After each update, the current values of r sj(t) and L sj (t), are stored as old variables for the next round of calculations. Simulation time is calculated by op_sim_time KP. By reception of each data packet, if the value of L sj (t) for corresponding service class is more than the allocated buffer length for that service class, then L sj (t) will not be updated and the packet is destroyed. Equation (3) is updated by the arrival of each probing packet for three service classes and the values of B sj are written in the probing packets. As the probing packet passes each router, the new values of B sj, are compared to the stored values of previous router and finally the minimum values of B sj are found. Then, the summations of B sj values for three service classes are written in two acknowledgment packets according to equation (4). In order to perform load balancing, 4

6 Figure 1. Applied topology Figure 2. Average throughput of the two scenarios 6

7 Figure 3. Average end-to-end delay of the two scenarios Figure 4. Average throughput of the two scenarios with decreased network capacity 7

8 Figure 5. Average end-to-end delay of the two scenarios with decreased network capacity Figure 6. Average throughput of the two scenarios with decreased network capacity and self similar source 8

9 Figure 7. Average end-to-end delay of the two scenarios with decreased network capacity and self similar source 4. Conclusion In this paper an EEAC algorithm was used to implement a new adaptive load balancing algorithm. The probing phase information represented a good estimation of network congestion. A heuristic equation is used for load balancing algorithm according to the obtained congestion information which is simple and scalable. Additionally, buffer length parameter played a key role in splitting traffic efficiently on multiple paths. Finding the most appropriate paths and splitting traffic among them, resulted in higher network throughput, better network utilization and lower end-to-end delay. References [1] S. Upadhyaya, G. Devi, Exploring Issues Related to Multipath Routing, Proceedings of the 5th National Conference, INDIACom, [2] D. Awduche, A. Chiu, A. Elwalid, I. Widjaja, X. Xiao, Overview and principles of internet traffic engineering, RFC 3272, [3] Z. Cao, Z. Wang, E. Zegura, Performance of hashing-based schemes for internet load balancing, IEEE INFOCOM, pp , [4] C. Villamizar, OSPF optimized multipath (OSPF-OMP), Work in Progress, [5] D. Thaler, C. Hopps, Multipath issues in unicast and multicast next-hop selection, RFC 2991, [6] H.S. Palakurthi, Study of multipath routing for QoS provisioning, EECS, [7] G. Yuan, Y. Chen, Y. Wei, S. Nie, A distributable traffic-based MPLS dynamic load balancing scheme, Asia-Pacific Conference on Communications, pp , [8] D. Gao, Y. Shu, S. Liu, O.W.W. Yang, Delay-based adaptive load balancing in MPLS networks, IEEE International Conference on Communications, vol. 2, pp ,

10 [9] X. He, H. Tang, M. Zhu, Q. Chu, Flow-level based adaptive load balancing in MPLS networks, ChinaCOM, pp. 1-6, [10] N. Moghim, S.M. Safavi, M.R. Hashemi, Evaluation of a new end-to-end quality of service algorithm in differentiated service networks, IET Communications, vol. 4, no.14, pp , [11] Z. Vali, M. Hashemi, N. Moghim, Proposing a load balancing algorithm with the help of an endpoint admission control algorithm to improve traffic engineering, JSCSE Journal, Advance Academic Publication, vol. 2, no. 6, pp.42-55, [12] E. Oki, R. Rojas-Cessa, M. Tatipamula, C. Vogt, IP Quality Of Service, Advanced Internet Protocols, Services, and Applications, John Wiley & Sons, USA, [13] P. Narvaez, K.Y. Siu, H.Y. Tzeng, Efficient algorithms for multi-path link state routing, ISCOM 99, [14] J. Yang, J. Ye, S. Papavassiliou, N. Ansari, A flexible and distributed architecture for adaptive end-to-end QoS provisioning in next-generation networks, IEEE Journal On Selected Areas In Communications, vol. 23, no. 2, [15] OPNET Modeler, Version 14.5.A 10

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