Conference Paper

Explicit multicast routing algorithms for constrained traffic engineering

Seoul Nat. Univ.
DOI: 10.1109/ISCC.2002.1021715 Conference: Computers and Communications, 2002. Proceedings. ISCC 2002. Seventh International Symposium on
Source: IEEE Xplore

ABSTRACT This paper presents a new traffic engineering technique for dynamic constrained multicast routing, where the routing request of traffic arrives one-by-one. The objective we adopted is to minimize the maximum of link utilization. Although this traffic engineering is useful to relax the most heavily congested link in the Internet backbone, the total network resources, i.e. sum of link bandwidth consumed, could be wasted when the acquired path is larger (in terms of number of hops) than the conventional shortest path. Accordingly we find a multicast tree for routing request that satisfies the hop-count constraint. We formulate this problem as a mixed-integer programming problem and propose a new heuristic algorithm to find a multicast tree for multicast routing request. The presented heuristic algorithm uses the link-state information, i.e. link utilization, for multicast tree selection and is amenable to distributed implementation. The extensive simulation results show that the proposed traffic engineering technique and heuristic algorithm efficiently minimize the maximum of link utilization better than the shortest path.

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    • "As you can see in the Table I, none of the above proposals consider how to find the appropriate multiple trees to minimize all four features (maximum link utilization, number of hops, end-to-end delay and total bandwidth consumption) which we address in the optimization model proposed in this paper. TABLE I: Type of transported flow and constraints FLOW OBJECTIVES CONSTRAINTS PATH/TREES [9] Rao & Batsell Unicast DL BC Multi-Path [5] Chen & Chan Unicast DL BC Multi-Path [8] Wang, Wang & Zhang Unicast MLU BC FA BC One and Multiple Paths [10] Lee, Seok, Choi & Kim Unicast MLU HC BC Multi-Path [11] Seok, Lee, Choi & Kim Multicast MLU BC HC BC MSF Only One Tree [7] and this paper "
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    ABSTRACT: In this paper, we propose a multi-objective traffic engineering scheme using different distribution trees to multicast several flows. The aim is to combine into a single aggregated metric, the following weighting objectives: the maximum link utilization, the hop count, the total bandwidth consumption, and the total end-to-end delay. Moreover, our proposal solves the traffic split ratio for multiple trees. We formulate this multi-objective function as one with Non Linear programming with discontinuous derivatives (DNLP). Results obtained using SNOPT solver show that several weighting objectives are decreased and the maximum link utilization is minimized. The problem is NP-hard, therefore, a novel SPT algorithm is proposed for optimizing the different objectives. The behavior we get using this algorithm is similar to what we get with SNOPT solver. The proposed approach can be applied in MPLS networks by allowing the establishment of explicit routes in multicast events. The main contributions of this paper are the optimization model and the formulation of the multi-objective function; and that the algorithm proposed shows polynomial complexity.
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    • "However, they target undirected links and do not target directed links. The multicast routing method, which emphasizes the balanced link utilization of a multicast tree with directed links, is presented in [13], but the method does not deal with minimizing the tree cost nor with the multidomain environment. The Border Gateway Multicast Protocol (BGMP) [14] and Decentralized-Core-based Tree (DCBT) [15], which are used for multidomain multicasts, are extensions of a corebased tree (CBT) [16]. "
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    ABSTRACT: The IPTV service, in which high-capacity content is broadcast from the IPTV server to a huge number of users, is becoming very popular. To establish an effective IPTV network, we need to minimize the cost of the IPTV multicast tree. That tree consists of a node, to which an IPTV server is connected, as the root node and other nodes, to which users are connected. In addition, we have to consider users who belong to multiple network domains. In this paper, we apply hierarchically distributed path computation elements (HDPCEs) to cooperatively create appropriate IPTV trees for multidomain users. Each HDPCE shares the burden of creating a multidomain multicast tree. Thus, we can reduce the computational burdens of creating a multicast tree. In addition, we enable choosing three different algorithms to create the cheapest multicast trees in individual domains. One of them is a new multiplex-aware-route- selection (MARS) algorithm. We evaluated the applicability of the three algorithms to various types of domains depending on network conditions.
    Global Telecommunications Conference, 2007. GLOBECOM '07. IEEE; 12/2007
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    • "When load balancing techniques are translated into a mathematical formulation, a heuristic or a practical implementation, different conflicting objectives are found and hence they have been considered in the literature (more details are presented in Table 2) as minimizing: • maximum or average link utilization [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]; • maximum, average and / or total hop count [1] [11] [12] [13] [14] [15] [16]; • maximum, average and / or total delay [1] [8] [9] [11] [12] [13] [14] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28]; • bandwidth consumption [3] [4] [7] [11] [12] [13] [14] [15] [17] [19] [23] [24] [25] [26] [28] [29]; • flow assignation [4] [24]; • packet loss [17] [25] [26] [28]; • queue size [22]; "
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    ABSTRACT: This paper presents a new traffic engineering load balancing taxonomy, classifying several publications and including their objective functions, constraints and proposed heuristics. Using this classification, a novel Generalized Multiobjective Multitree model (GMM-model) is proposed. This model considers for the first time multitree-multicast load balancing with splitting in a multiobjective context, whose mathematical solution is a whole Pareto optimal set that can include several results than it has been possible to find in the publications surveyed. To solve the GMM-model, a multi-objective evolutionary algorithm (MOEA) inspired by the Strength Pareto Evolutionary Algorithm (SPEA) is proposed. Experimental results considering up to 11 different objectives are presented for the well-known NSF network, with two simultaneous data flows.
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