Software-defined networking (SDN) features the decoupling of the control plane and data plane, a programmable network and virtualization, which enables network infrastructure sharing and the “softwarization” of the network functions. Recently, many research works have tried to redesign the traditional mobile network using two of these concepts in order to deal with the challenges faced by mobile operators, such as the rapid growth of mobile traffic and new services. In this paper, we first provide an overview of SDN, network virtualization, and network function virtualization, and then describe the current LTE mobile network architecture as well as its challenges and issues. By analyzing and categorizing a wide range of the latest research works on SDN and virtualization in LTE mobile networks, we present a general architecture for SDN and virtualization in mobile networks (called SDVMN) and then propose a hierarchical taxonomy based on the different levels of the carrier network. We also present an in-depth analysis about changes related to protocol operation and architecture when adopting SDN and virtualization in mobile networks. In addition, we list specific use cases and applications that benefit from SDVMN. Last but not least, we discuss the open issues and future research directions of SDVMN.

One of the main challenges in deploying container service is providing the scalability to satisfy the service performance and avoid resource wastage. To deal with this challenge, Kubernetes provides two kinds of scaling mode: vertical and horizontal. Several existing autoscaling methods make efforts to improve the default autoscalers in Kubernetes; however, most of these works only focus on one scaling mode at the same time, which results in some limitations. Only horizontal scaling may lead to low utilization of containers due to the fixed amount of resources for each instance, especially in the low-request period. In contrast, only vertical scaling may not ensure the quality of service (QoS) requirements in case of bursty workload due to reaching the upper limit. Besides, it is also necessary to provide burst identification for auto-scalers to guarantee service performance. This paper proposes a hybrid autoscaling method with burst awareness for containerized applications. This new approach considers a combination of both vertical and horizontal abilities to satisfy the QoS requirement while optimizing the utilization of containers. Our proposal uses a predictive method based on the machine learning technique to predict the future demand of the application and combines it with a burst identification module, which makes scaling decisions more effective. Experimental results show an enhancement in maintaining the response time below the QoS constraint whereas remaining high utilization of the deployment compared with existing baseline methods in single scaling mode.

This work presents a surface-enhanced Raman scattering (SERS) and density functional theory (DFT) study of a fipronil adsorbed on colloidal silver nanoparticles (AgNPs). A standard curve was established to quantify fipronil within a range of 0.0001–0.1 ppm (r² ≥ 0.985), relying on the unique fipronil Raman shift at ~2236 cm⁻¹ adsorbed on AgNPs. DFT calculations suggest that the nitrile moiety (C≡N) binding should be slightly more favorable, by 1.92 kcal/mol, than those of the nitrogen atom of the pyrazole in fipronil and Ag₆ atom clusters. The characteristic peaks of the SERS spectrum were identified, and both the calculated vibrational wavenumbers and the Raman intensity pattern were considered. The vibrational spectra of fipronil were obtained from the potential energy distribution (PED) analysis and selective Raman band enhancement.

Recently, several researchers have proposed adopting software‐defined networking (SDN) concepts for mobile network architectures, particularly for LTE‐evolved packet core (LTE/EPC). Although several new designs have been introduced for architectures based on SDN or its concrete realization, Openflow, few studies have presented in‐depth discussions of real procedures that are relevant to such architectures. To this end, this paper first surveys the current approaches and solutions for adopting SDN/Openflow in LTE/EPC architectures and then introduces a new Openflow‐enabled EPC (OEPC) architecture. This work provides detailed analyses of five procedures that commonly occur in LTE/EPC architectures, and the analyses are further elaborated with the separation of the control and data planes and the support of extended Openflow protocol. The analysis shows that the data management of these procedures is simpler relative to traditional LTE/EPC. In addition, in order to prove that efficient data management takes place for these procedures relative to a traditional LTE/EPC architecture, the numbers of signalling messages that are processed by control entities (i.e., the MME and the controller) are taken into account as a metric to evaluate the OEPC architecture. Moreover, the results of a numerical evaluation also show the benefits of this proposal relative to another Openflow‐based LTE/EPC architecture.

Recently, the development of Kubernetes (K8s) containerization platform has enabled cloud-based, lightweight, highly scalable, and agile services in both general and telco use-cases. Ensuring high availability, reliable and continuous containerized services is a major requirement of service providers to provide fault-tolerance, transparent service experiences to end-users. To satisfy this requirement, fault prediction and proactive stateful service recovery features must be applied in cloud systems. Prior proactive failure recovery approaches mostly focused on either improving fault prediction performance based on different machine learning time series forecasting techniques or optimizing recovery service placement after fault prediction. However, a mechanism that enables stateful containerized service migration from the predicted faulty node to the healthy destination node has not been studied. Service migration in previous proactive works is only simulated or performed by virtual machine (VM) migration techniques. In this paper, we propose a proactive stateful fault-tolerant system for K8s containerized services that pipelines a Bidirectional Long Short-Term Memory (Bi-LSTM) fault prediction framework and a novel K8s stateful service migration mechanism for service recovery. Experimental results show how the Bi-LSTM model improved prediction performance against other time-series forecasting models in prior proactive works. We then combined the Bi-LSTM fault prediction framework with both the default K8s and our stateful migration mechanisms. The comparison between these two proactive systems proves our system efficiency in terms of avoiding Quality of Service (QoS) violation.

Nowadays, in big cities, parking management is a critical issue from both the driver’s side and the city government’s side. From the driver’s side, how to find an available parking lot in a city is a considerable concern. As a result, smart parking systems recently have received great interest, both in academia and industry. From the city government’s side, how to manage and distribute such a limited public parking resource efficiently to give every visitor a fair chance of finding an on-street parking lot is also a considerable concern. However, existing studies of smart parking management focus only on assisting the driver’s side to find available parking spaces. This study aims to raise a new perspective on such smart parking management and to propose a novel location-centric IoT-cloud-based parking violation management system. The system is designed to assist authoritative officers in finding parking violations easily and recommends the least cost path for officers so that officers can achieve their highest productivity in finding parking violations and issuing parking tickets. Experimental results show that the system not only improves the productivity of officers in finding parking violations and issuing tickets, but also helps reduce the traveling cost of officers and to reduce the average violation period of violating cars considerably.