Online Service Provisioning in NFV-enabled Networks Using Deep Reinforcement Learning

In this paper, we study a Deep Reinforcement Learning (DRL) based framework for an online end-user service provisioning in a Network Function Virtualization (NFV)-enabled network. We formulate an optimization problem aiming to minimize the cost of network resource utilization. The main challenge is provisioning the online service requests by fulfilling their Quality of Service (QoS) under limited resource availability. Moreover, fulfilling the stochastic service requests in a large network is another challenge that is evaluated in this paper. To solve the formulated optimization problem in an efficient and intelligent manner, we propose a Deep Q-Network for Adaptive Resource allocation (DQN-AR) in NFV-enable network for function placement and dynamic routing which considers the available network resources as DQN states. Moreover, the service's characteristics, including the service life time and number of the arrival requests, are modeled by the Uniform and Exponential distribution, respectively. In addition, we evaluate the computational complexity of the proposed method. Numerical results carried out for different ranges of parameters reveal the effectiveness of our framework. In specific, the obtained results show that the average number of admitted requests of the network increases by 7 up to 14% and the network utilization cost decreases by 5 and 20 %

Phylogenetic and Genetic Analysis of D-loop and Cyt-b Region of mtDNA Sequence in Iranian Sistani, Sarabi and Brown Swiss Cows

Cattle have an important role in primary human civilization, so molecular studies for more accurate recognition of their origin are effective to identify unknown historical aspects. Cattle can be divided in to 2 main groups including Bos Tuarus and Bos Indicus. Both types of cattle can be found in Iran; therefore study of their origin has particular importance. The aim of this study was to investigate the nucleotide sequences of Cytochrome-b (Cyt-b) and HVR1&2 loci of D-loop gene region in mitochondrial DNA of Sistani, Sarabi and Brown Swiss breeds of cattle. Twenty blood samples of each breed, from non-relative individuals were obtained from blood bank of animal science department of Faculty of Agriculture, Ferdowsi University of Mashhad. The DNA content of sample was extracted based on the guanidinium thiocianate-silicagel method. Polymerase Chain Reaction with specific designed primers was performed to amplify Cyt-b and HVR 1&2 loci with 751 and 701 bp lengths, respectively. Sequencing of amplified Cyt-b and HVR 1&2 loci were done based on Sanger method by automatic sequencer machine (ABI 3130). Nucleotide diversity in Brown Swiss, Sarabi and Sistani breeds were estimated 0.0037, 0.0024 and 0.0029, respectively. Sequences of Cyt-b and HVR 1&2 were register in National Center for Biotechnology Institute due to nucleotide differences. Results of phylogenetic test using UPGMA for both loci showed that Sarabi and Sistani breeds are belonging to first group and Brown Swiss breed to other group

Profit Maximization in 5G+ Networks with Heterogeneous Aerial and Ground Base Stations

In this paper, we propose a novel framework for 5G and beyond (5G+) heterogeneous wireless networks consisting of macro aerial base stations (MABSs), small aerial base stations (SABSs), and ground base stations (GBSs) with two types of access technologies: power domain non-orthogonal multiple access (PD-NOMA) and orthogonal frequency-division multiple access (OFDMA). We aim to maximize the total network profit under some practical network constraints, e.g., NOMA and OFDMA limitations, transmit power (TP) maximum limits, and isolation of the virtualized wireless network. We formulate the resource allocation problem encompassing joint TP allocation, ABS altitude determination, user association, and sub-carrier allocation parameters. Our optimization problem is mixed integer non-linear programming (MINLP) with high computational complexity. To propose a practical approach with reduced computational complexity, we use an alternate method where the main optimization is broken down into three sub-problems with lower computational complexity. We do this b