Abstract-Telemedicine traffic carries critical information with regard to a patients' condition; hence, it requires the highest transmission priority compared with all other types of traffic in the cellular network. The need for expedited errorless transmission of multimedia telemedicine traffic calls for a guaranteed bandwidth to telemedicine users. This condition, however, creates a tradeoff between the satisfaction of the very strict quality-of-service (QoS) requirements of telemedicine traffic and the loss of this guaranteed bandwidth in the numerous cases when it is left unused due to the infrequent nature of telemedicine traffic. To resolve this complex problem, in this paper, we propose and combine the following two techniques: 1) an adaptive bandwidth reservation scheme based on road map information and on user mobility and 2) a fair scheduling scheme for telemedicine traffic transmission over wireless cellular networks.

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Network virtualization (NV) has ubiquitously emerged as an indispensable attribute to enable the success of the forthcoming virtualized networks (eg, 5G network and smart Internet of Things [IoT]). Virtual network embedding (VNE) is the major challenge in NV that allows multiple heterogeneous virtual networks (VNs) to simultaneously coexist on a shared substrate infrastructure. A great number of VNE algorithms have been proposed, but over the past decades, most of them are only targeting for VNE node mapping. In this paper, we propose two distributed parallel genetic algorithms, which are based on two versions of crossover and mutation schemes, for online VN link embedding problems with low latency and high efficiency. Furthermore, we conduct a time analysis on the executing time of independently distributed parallel computing machines in details. This comprehensive analysis validates the parallel computing scalability on an identical number of predefined parallel machines. Extensive simulations have shown that our proposed algorithms can achieve better performance than integer linear programming (ILP)-based solutions while meeting the stringent time requirements for online VN embedding applications. Our proposed algorithms yield superior performance in running time with 32.78% up to 1727.8% faster than existing popular VNE algorithms. Additionally, the theoretical analysis indicates that the execution time can be reduced to logarithmic times by applying proposed distributed parallel algorithms.

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