Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   consider the optimal design and provisioning of WDM networks for the grooming of multicast subwavelength traffic.  develop a unified framework for the optimal provisioning of different practical scenarios of multicast traffic grooming.  also introduce heuristic solutions. Optimal solutions are designed by exploiting the specifies of the problems to formulate Mixed Integer Linear Programs (MILPs). Specifically,  solve the generic multicast problem in which, given a set of multicast sessions and all destination nodes of a multicast session requiring the same amount of traffic, all demands need to be accommodated. The objective is to minimize the network cost by minimizing the number of higher layer electronic equipment and, simultaneously, minimizing the total number of wavelengths used.  also solve two interesting and practical variants of the traditional multicast problem, namely, multicasting with partial destination set reachability and multicasting with traffic thinning. For both variants,  also provide optimal as ll as heuristic solutions. Also, the paper presents a number of examples based on the exact and heuristic approaches
['Raza Ul-Mustafa', 'Ahmed E. Kamal']
I am writing a research paper on a idea Design and provisioning of WDM networks with multicast traffic grooming
[{"from": "human", "value": "I am writing a research paper on a idea Design and provisioning of WDM networks with multicast traffic grooming"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   consider the optimal design and provisioning of WDM networks for the grooming of multicast subwavelength traffic.  develop a unified framework for the optimal provisioning of different practical scenarios of multicast traffic grooming.  also introduce heuristic solutions. Optimal solutions are designed by exploiting the specifies of the problems to formulate Mixed Integer Linear Programs (MILPs). Specifically,  solve the generic multicast problem in which, given a set of multicast sessions and all destination nodes of a multicast session requiring the same amount of traffic, all demands need to be accommodated. The objective is to minimize the network cost by minimizing the number of higher layer electronic equipment and, simultaneously, minimizing the total number of wavelengths used.  also solve two interesting and practical variants of the traditional multicast problem, namely, multicasting with partial destination set reachability and multicasting with traffic thinning. For both variants,  also provide optimal as ll as heuristic solutions. Also, the paper presents a number of examples based on the exact and heuristic approaches"}]