Dynamic service function chain placement with instance reuse in Fog–Cloud​ Computing

Li Xueqiang,Su Cai,Ghobaei-Arani Mostafa,Albaghdadi Mustafa Fahem 한국통신학회 2023 ICT Express Vol.9 No.5

The advent of Network Function Virtualization (NFV) technology has brought flexible provisioning to Fog–Cloud Computing-based Networks (FCCNs) for enterprises to outsource their network functions to data center networks. Service Function Chaining (SFC) is a networking concept in NFV by which traffic is steered through an ordered set of Virtual Network Functions (VNFs) composing an end-to-end service. When hundreds of users outsource their network functions to FCCN, the optimal placement of VNFs in the network becomes important for assembling SFCs with the aim of resource utilization efficiency. Motivated by the scalability shortcomings of existing schemes, we propose Deep Reinforcement Learning (DRL)-based approaches by simultaneously considering parallelized SFC and reuse of VNFs to solve this problem, i.e., Asynchronous Advantage Actor–Critic (A3C). A parallelized SFC consists of several sub-SFCs, which can reduce delay and guarantee availability. Also, reuse of preliminary VNFs in SFC placement can improve computation acceleration. The proposed scheme pursues the maximization of the long-term cumulative reward for the trade-off between Quality of Service (QoS) and service cost. The results of the experiments show that the proposed scheme performs better than the state-of-the-art methods.

Identifying natural genotypes of grain number per panicle in rice (Oryza sativa L.) by association mapping

Jianyin Xie,Fengmei Li,Najeeb Ullah Khan,Xiaoyang Zhu,Xueqiang Wang,Zhifang Zhang,Xiaoqian Ma,Yan Zhao,Quan Zhang,Shuyang Zhang,Zhanying Zhang,Jinjie Li,Zichao Li,Hongliang Zhang 한국유전학회 2019 Genes & Genomics Vol.41 No.3

Introduction As one of the main yield components, grain number per panicle (GNP) played critical role in the rice yield improvement. The identification of natural advantageous variations under different situations will promote the sustainable genetic improvement in rice yield. Objectives This study was designed to identify natural genotypes in a rice mini-core collection, to examine the genotypic effects across the indica and japonica genetic background in different environments, and excavating the superior genotypes that had drove the modern genetic improvement. Methods The association mapping of GNP was carried out using a mini-core collection including 154 indica and 119 japonica accessions in seven different environments. Genotypic effects of each genotype for each QTL were calculated and genotype frequency distortion between the commercial rice cultivars and landraces was screened by χ2-test. Results In total, 74 QTLs containing stable and sensitive QTLs in various environments were detected. Within them, 20 positive and 24 negative genotypes in indica, and 24 positive and 16 negative genotypes in japonica were identified. When checking the accumulation of positive genotypes identified in indica across cultivars in each of the two subspecies, it indicated that increased number of positive genotypes identified in indica results in the substantially increased GNP in both indica and japonica across all of the environments, while this trend was not obvious for the positive genotypes identified in japonica especially in short day environments. Moreover, the positive and negative genotype frequency distortion between the landraces and commercial rice cultivars indicated that both positive selection of positive genotypes and negative selection of negative genotypes had driven the genetic improvement on GNP. Conclusion Our findings suggested that the accumulation of positive genotypes and purifying negative genotypes played equivalently important roles in the improvement of rice yield, but the efficient use for some QTLs or genotypes depends on the comprehensive evaluation of their effect under diverse genetic backgrounds and environments.

The Role of Macrophage Migration Inhibitory Factor (MIF) in Asthmatic Airway Remodeling

Li Ruyi,Wang Feiyun,Wei Jianghong,Lin Yun,Tang Guofang,Rao Lizong,Ma Libing,Xu Qing,Wu Jingjie,Lv Qian,Zhou Rui,Lei Huiren,Zhao Xueqiang,Yao Dong,Xiao Bo,Huang Haiming,Zhang Jiange,Mo Biwen 대한천식알레르기학회 2021 Allergy, Asthma & Immunology Research Vol.13 No.1

Purpose: Recent studies have demonstrated that macrophage migration inhibitory factor (MIF) is of importance in asthmatic inflammation. The role of MIF in modulating airway remodeling has not yet been thoroughly elucidated to date. In the present study, we hypothesized that MIF promoted airway remodeling by intensifying airway smooth muscle cell (ASMC) autophagy and explored the specific mechanisms. Methods: MIF knockdown in the lung tissues of C57BL/6 mice was conducted by instilling intratracheally adeno-associated virus (AAV) vectors (MIF-mutant AAV9) into mouse lung tissues. Mice genetically deficient in the autophagy marker ATG5 (ATG5+/−) was used to detect the role of autophagy in ovalbumin (OVA)-asthmatic murine models. Moreover, to block the expression of MIF and CD74 in vitro models, inhibitors, antibodies and lentivirus transfection techniques were employed. Results: First, MIF knockdown in the lung tissues of mice showed markedly reduced airway remodeling in OVA murine mice models. Secondly, ASMC autophagy was increased in the OVA-challenged models. Mice genetically deficient in the autophagy marker ATG5 (ATG5+/−) that were primed and challenged with OVA showed lower airway remodeling than genetically wild-type asthmatic mice. Thirdly, MIF can induce ASMC autophagy in vitro. Moreover, the cellular source of MIF which promoted ASMC autophagy was macrophages. Finally, MIF promoted ASMC autophagy in a CD74-dependent manner. Conclusions: MIF can increase asthmatic airway remodeling by enhancing ASMC autophagy. Macrophage-derived MIF can promote ASMC autophagy by targeting CD74.

Removal and fate of carbamazepine in the microbial fuel cell coupled constructed wetland system

Jingyi Xie,Haixiao Li,Shiyu Wang,Hao Chen,Wei Jiang,Lin Zhang,Lianjie Wang,Yufeng Wu,Lirong Li,Xueqiang Lu 대한환경공학회 2022 Environmental Engineering Research Vol.27 No.3

Carbamazepine (CBZ), which is difficult to remove in the wastewater treatment system and easily forms toxic transformation products during the treatment process, is one of the priority pollutants of pharmaceuticals and personal care products (PPCPs). Increasing attention has been paid to explore their treatment technology without side effects from the treatment products. This study aims to reveal the removal and transformation of CBZ in the microbial fuel cell coupled constructed wetland (CW-MFC) system. The CW-MFC system was operated continuously at room temperature for nearly 80 days. The results show that CW-MFC system can effectively remove CBZ with an average removal rate of 97%. Three transformation products were identified by liquid chromatography−high-resolution mass spectrometry: 2-(2-oxoquinazolin-1(2H)-yl) benzoic acid (TP267), methyl 2-(2-oxoquinazolin-1(2H)-yl) benzoate (TP281), 2-(2,4-dioxo-3,4-dihydroquinazolin-1(2H)-yl) benzoic acid (TP283). Except TP281 in the influent, the other transformation products were formed in the system, which indicated that TP267 and TP283 were the main transformation products of CBZ. The formation pathway of transformation products could be explained by reactions including oxidation, hydrolysis, bond rupture and intramolecular reaction. The results also indicate that the CW-MFC system might be a promising technology for PPCPs treatment.

Outage Analysis of CRNs with SC Diversity Over Nakagami-m Fading Environment

( Zongsheng Zhang ),( Qihui Wu ),( Xueqiang Zheng ),( Jinlong Wang ),( Lianbao Li ) 한국인터넷정보학회 2013 KSII Transactions on Internet and Information Syst Vol.7 No.12

In this paper, we investigate the outage performance of a cognitive relay network. We consider mutual interference in an independent, non-identically distributed Nakagmai-m fading environment. We first derive the close-form outage probability expression, which provides an efficient means to evaluate the effects of several parameters. This allows us to study the impact of several parameters on the network`s performance. We then derive the asymptotic expression and reveal that the diversity order is strictly determined by the fading severity of the cognitive system. It is not affected by the primary network. Moreover, the primary network only affects the coding gain of the cognitive system. Finally, Monte Carlo simulations are provided, which corroborate the analytical results.