Network Server Research Articles

Calorie Detection of Food Image based on SVM Algorithm

Nowadays, standard intake of healthy food is vital for keeping a diet to avoid obesity within the human body . In this paper, we present a totally unique system supported machine learning that automatically performs accurate classification of food images and estimates food attributes. This paper proposes a machine learning model consisting of a support vector machine that classifies food into specific categories within the training a part of the prototype system. The most purpose of the proposed method is to reinforce the accuracy of the pre-training model. The paper designs a prototype system supported the client server network model. The client sends an image detection request and processes it on the server side. The prototype system is meant with three main software components, including a pre-trained support vector machine training module for classification purposes, a text data training module for attribute estimation models, and a server-side module. We experimented with a selection of food categories, each containing thousands of images, and therefore the machine learning training to understand higher classification accuracy.

Off-campus Access to Licensed Online Resources through Shibboleth

Institutions of advanced education and research, through their libraries, invest substantially in licensed online resources. Only authorized users of an institution are entitled to access licensed online resources. Seamless on-campus access to licensed resources happens mostly through Internet Protocol (IP) address authentication. Increasingly, licensed online resources are accessed by authorized users from off-campus locations as well. Libraries will, therefore, need to ensure seamless off-campus access to authorized users. Libraries have been using various technologies, including proxy server or virtual private network (VPN) server or single sign-on, to facilitate seamless off-campus access to licensed resources. In this paper, authors share their experience in setting up a Shibboleth-based single sign-on (SSO) access management system at the JRD Tata Memorial Library, Indian Institute of Science, to enable authorized users of the institute to seamlessly access licensed online resources from off-campus locations.

Metaheuristic based auto-scaling for microservices in cloud environment: a new container-aware application scheduling

PurposeThe cloud is a network of servers to share computing resources to run applications and data storage that offers services in various flavours, namely, infrastructure as a service, platform as a service and software as a service. The containers in the cloud are defined as “standalone and self-contained units that package software and its dependencies together”. Similar to virtual machines, the virtualization method facilitates the resource on a specific server that could be used by numerous appliances.Design/methodology/approachThis study introduces a new Dragon Levy updated squirrel algorithm (DLU-SA) for container aware application scheduling. Furthermore, the solution of optimal resource allocation is attained via defining the objective function that considers certain criteria such as “total network distance (TND), system failure (SF), balanced cluster use (BC) and threshold distance (TD)”. Eventually, the supremacy of the presented model is confirmed over existing models in terms of cost and statistical analysis.FindingsOn observing the outcomes, the total cost of an adopted model for Experimentation 1 has attained a lesser cost value, and it was 0.97%, 10.45% and 10.37% superior to traditional velocity updated grey wolf (VU-GWO), squirrel search algorithm (SSA) and dragonfly algorithm (DA) models, respectively, for mean case scenario. Especially, under best case scenario, the implemented model has revealed a minimal cost value of 761.95, whereas, the compared models such as whale random update assisted lion algorithm, VU-GWO, SSA and DA has revealed higher cost value of 761.98, 779.46, 766.62 and 766.51, respectively. Thus, the enhancement of the developed model has been validated over the existing works.Originality/valueThis paper proposes a new DLU-SA for container aware application scheduling. This is the first work that uses the DLU-SA model for optimal container resource allocation by taking into consideration of certain constraints such as TND, SF, BC and TD.

Traffic-aware adaptive server load balancing for software defined networks

Servers in data center networks handle heterogenous bulk loads. Load balancing, therefore, plays an important role in optimizing network bandwidth and minimizing response time. A complete knowledge of the current network status is needed to provide a stable load in the network. The process of network status catalog in a traditional network needs additional processing which increases complexity, whereas, in software defined networking, the control plane monitors the overall working of the network continuously. Hence it is decided to propose an efficient load balancing algorithm that adapts SDN. This paper proposes an efficient algorithm TA-ASLB-traffic-aware adaptive server load balancing to balance the flows to the servers in a data center network. It works based on two parameters, residual bandwidth, and server capacity. It detects the elephant flows and forwards them towards the optimal server where it can be processed quickly. It has been tested with the Mininet simulator and gave considerably better results compared to the existing server load balancing algorithms in the floodlight controller. After experimentation and analysis, it is understood that the method provides comparatively better results than the existing load balancing algorithms.

Edge intelligence computing for mobile augmented reality with deep reinforcement learning approach

Convergence of Augmented Reality (AR) and Next Generation Internet-of-Things (NG-IoT) can create new opportunities in many emerging areas, where the real-time data can be visualized on the devices. Integrated NG-IoT network, AR can improve efficiency in many fields such as mobile computing, smart city, intelligent transportation and telemedicine. However, limited by capability of mobile device, the reliability and latency requirements of AR applications is difficult to meet by local processing. To solve this problem, we study a binary offloading scheme for AR edge computing. Based on the proposed model, the parts of AR computing can offload to edge network servers, which is extend the computing capability of mobile AR devices. Moreover, a deep reinforcement learning offloading model is considered to acquire B5G network resource allocation and optimally AR offloading decisions. First, this offloading model does not need to solve combinatorial optimization, which is greatly reduced the computational complexity. Then the wireless channel gains and binary offloading states is modeled as a Markov decision process, and solved by deep reinforcement learning. Numerical results show that our scheme can achieve better performance compared with existing optimization methods.

PELATIHAN MEMBUAT VPN MENGGUNAKAN LAYANAN CLOUD COMPUTING SMK MA’ARIF NU 1 KEMBARAN, BANYUMAS

Cloud computing is a technology that has powerful computing resources that can be applied to many organizations by using dynamic scalability as a virtual service source via the internet. SMK Ma'arif NU 1 Kembaran has a 2013 TKJ curriculum. The curriculum has topics such as Computer Assembly, Network Operating Systems and Server Administration. In the subject of Network Operating Systems, there is no subject that discusses cloud computing technology. This technology will have an impact on the average score of students before training to make VPN with cloud computing, namely 83, 12 and after training to 89.06 so that there is a descriptive increase. The hypothesis used is a two-way hypothesis so that it uses two tails with the result t table that is 2.039513 with a p value of 0.01249. Because the p value is smaller than alpha 5% or by looking at the value | t count | > t table then Ho is rejected. This means that there is a significant difference in the level of understanding of students before and after training to make VPN with cloud computing.Cloud computing is a technology that has powerful computing resources that can be applied to many organizations by using dynamic scalability as a virtual service source via the internet. SMK Ma'arif NU 1 Kembaran has a 2013 TKJ curriculum. The curriculum has topics such as Computer Assembly, Network Operating Systems and Server Administration. In the subject of Network Operating Systems, there is no subject that discusses cloud computing technology. This technology will have an impact on the average score of students before training to make VPN with cloud computing, namely 83, 12 and after training to 89.06 so that there is a descriptive increase. The hypothesis used is a two-way hypothesis so that it uses two tails with the result t table that is 2.039513 with a p value of 0.01249. Because the p value is smaller than alpha 5% or by looking at the value | t count | > t table then Ho is rejected. This means that there is a significant difference in the level of understanding of students before and after training to make VPN with cloud computing.

CoAP Acceleration on FPSoC for Resource Constrained Internet of Things Devices

Data communication stack on Internet of Things (IoT)-devices is traditionally implemented in software as a part of their operating systems. In IoT-applications for which resource constrained IoT-devices are deployed, leveraging microprocessor(s) for both computing and data communication tasks may result in poor application performance and high power consumption. In order to alleviate these performance and power issues, using System on Chip (SoC) field programmable gate arrays (FPGAs) for the hardware acceleration of the performance- or power-critical software tasks is a recent, popular trend in the literature. On the other hand, constrained application protocol (CoAP) is a pivotal network protocol for many IoT-applications. Motivated by these facts, in this article, a hardware accelerator for a CoAP server network stack is proposed and realized on a SmartFusion2 SoC FPGA. The hardware accelerated CoAP server network stack is thoroughly evaluated and compared in terms of its performance, latency, power consumption, and FPGA resource utilization against a baseline software implemented CoAP server network stack. The evaluation results obtained clearly show that the CoAP hardware accelerator provides significantly higher performance and lower response message latency, while consuming significantly less power. Consequently, the CoAP hardware accelerator proposed seems to be a strong viable solution for the resource constrained IoT-devices.

Location-sharing protocol for privacy protection in mobile online social networks

Location-based services are becoming more and more popular in mobile online social networks (mOSNs) for smart cities, but users’ privacy also has aroused widespread concern, such as locations, friend sets and other private information. At present, many protocols have been proposed, but these protocols are inefficient and ignore some security risks. In the paper, we present a new location-sharing protocol, which solves two issues by using symmetric/asymmetric encryption properly. We adopt the following methods to reduce the communication and computation costs: only setting up one location server; connecting social network server and location server directly instead of through cellular towers; avoiding broadcast encryption. We introduce dummy identities to protect users’ identity privacy, and prevent location server from inferring users’ activity tracks by updating dummy identities in time. The details of security and performance analysis with related protocols show that our protocol enjoys two advantages: (1) it’s more efficient than related protocols, which greatly reduces the computation and communication costs; (2) it satisfies all security goals; however, most previous protocols only meet some security goals.

Инструменты автоматизированного сбора и анализа социологической информации o территориальной идентичности жителей крупных городов

The paper proposes an algorithm for automated search and initial analysis of sociological information aimed at studying the territorial identity of city area residents using Internet sources. Communities of social networks, e.g. VKontakte, are considered as the main data source, and websites of topographic objects found in the territories under study are used as auxiliary information sources. It is demonstrated that, in terms of information support, public pages and groups with open or restricted access walls have the greatest potential. The developed algorithm implies selecting relevant groups, finding content concerning area issues, and determining the indices of community activity in discussing territorial problems. The required information is retrieved through the interaction with a social network server with the use of the official Application Programming Interface (API). To identify communities and posts, it is proposed to apply methods of morphological analysis of textual information. The software implementation of the algorithm is described in Python 3.8.5, including original functions for the acquisition of data on communities by their identification numbers, for the formation of a set of urbanonyms for a specified area, and some other ones. The developed program has been used to analyze territorial groups in three areas of Moscow; the results of the analysis enable us to estimate the degree of the territorial identity of their residents. The analysis of the error in the results of automated data collection and processing shows good agreement of these results with manually obtained ones, i.e. the error is 2.6% in the identification of relevant groups and about 3% in the identification of posts on area issues. Therewith, a much higher speed of response and lower labor effort required to perform routine operations allow the algorithm and the implementing computer program to be viewed as an effective tool for sociological research based on data from social networks.

LoRaWAN Link Layer

The LoRaWAN Link Layer specification [1] is a communication protocol for the Internet of Things. It targets low power, long range, low cost communication using unlicensed spectrum. Network topology is collaborative, which reduces a lot protocol signalling compared to a cellular network. The device is connected to a network server, and protocol overhead is limited to 13 bytes for any data frame. There are three classes of operation. Class A is optimized for low power operation of end-devices, while class B and class C offer reduced downlink latency. The protocol specification offers several mechanisms to adjust the link layer parameters: adaptive data rate, adaptive power control, variable repetition rate, and channel selection.

Patenting trends in biometric technology of the Big Five patent offices

We examined the overall trends in biometric technology based on patent documents. Using PATSTAT database, we extracted 37,462 patent documents applied at the Big Five patent offices between 1990 and 2016. Latent Dirichlet allocation was applied to their abstracts to observe annual trends by topic. Our results are as follows: Fingerprint-enabled car anti-theft systems have been undergoing rapid technological development since 2014. In response, biometric signal transmitting models are becoming popular owing to concerns about theft of biometric templates. While fingerprint, face, and iris authentication technologies continue to advance, finger vein, voice, and signature authentication technologies are lagging. Use of biometric technologies in financial transactions, server networks, and digital media content security are decreasing as well. A citation analysis discovered key topics and patent applicants: Surprisingly, the quantitative growth rate of topics and the effect on the knowledge network showed an inverse relationship. US firms had the most citations, but fewer backward citations of own work, unlike Japanese companies. We provide practical insights to stakeholders of biometric technology.

AMBtalk: A Cardiovascular IoT Device for Ambulance Applications.

Acute Coronary Syndrome (ACS) and other heart emergency events require immediate chest pain identification in the ambulance. Specifically, early identification and triage is required so that patients with chest pain can be quickly sent to a hospital with appropriate care facilities for treatment. In the traditional approach, ambulance personnel often use symptom checklists to examine the patient and make a quick decision for the target hospital. However, not every hospital has specialist facilities to handle such emergency cases. If the result of the subsequent cardiac enzyme test performed at the target hospital strongly suggests the occurrence of myocardial infarction, the patient may need to be sent to another hospital with specialist facilities, such as Percutaneous Coronary Intervention. The standard procedure is time consuming, which may result in delayed treatment and reduce patent survival rate. To resolve this issue, we propose AMBtalk (Ambulance Talk) for accurate, early ACS identification in an ambulance. AMBtalk provides real-time connection to hospital resources, which reduces the elapsed time for treatment, and therefore, improves the patient survival rate. The key to success for AMBtalk is the development of the AllCheck® Internet of Things (IoT) device, which can accurately and quickly provide cardiovascular parameter values for early ACS identification. The interactions between the AllCheck® IoT device, the emergency medical service center, the ambulance personnel and the hospital are achieved through the AMBtalk IoT server in the cloud network. AllCheck® outperforms the existing cardiovascular IoT device solutions for ambulance applications. The testing results of the AllCheck® device show 99% correlation with the results of the hospital reports. Due to its excellent performance in quick ACS identification, the AllCheck® device was awarded the 17th Taiwan Innovators Award in 2020.

СТАТИСТИЧЕСКИЙ АНАЛИЗ РЕАЛЬНОГО ТРАФИКА МЕЖМАШИННОГО ВЗАИМОДЕЙСТВИЯ (М2М)

The development of digital technology has spawned the concept of the Internet of Things (IoT). The concept basis is the machine-to-machine interaction (M2M) technology, which allows devices to exchange information. The most effective data transmission medium for M2M devices is mobile communications. Rapid growth of machine-to-machine М2М traffic in mobile communication network defines the actuality of the research problem, its features and characteristics. Research outcomes are indispensable at the network modeling, planning, analyzing the М2М traffic impact at quality of service (QoS) of mobile network communication. The article analyzes the real traffic in the LoraWan network. Aggregated traffic coming to the network server from all devices is considered. To model the М2М batch traffic, apart from specifying the statistic characteristics it is necessary to assess its self-similarity. In order to define the traffic self-similarity there has been computed Hurst parameter. On the basis of STATISTICA programs batch we have conducted statistical analysis and short-term forecasting of real М2М traffic by method of exponential smoothing.

Availability-aware and energy-aware dynamic SFC placement using reinforcement learning

Software-defined networking and network functions virtualisation are making networks programmable and consequently much more flexible and agile. To meet service-level agreements, achieve greater utilisation of legacy networks, faster service deployment, and reduce expenditure, telecommunications operators are deploying increasingly complex service function chains (SFCs). Notwithstanding the benefits of SFCs, increasing heterogeneity and dynamism from the cloud to the edge introduces significant SFC placement challenges, not least adding or removing network functions while maintaining availability, quality of service, and minimising cost. In this paper, an availability- and energy-aware solution based on reinforcement learning (RL) is proposed for dynamic SFC placement. Two policy-aware RL algorithms, Advantage Actor-Critic (A2C) and Proximal Policy Optimisation (PPO), are compared using simulations of a ground truth network topology based on the Rede Nacional de Ensino e Pesquisa Network, Brazil’s National Teaching and Research Network backbone. The simulation results show that PPO generally outperformed A2C and a greedy approach in terms of both acceptance rate and energy consumption. The biggest difference in the PPO when compared to the other algorithms relates to the SFC availability requirement of 99.965%; the PPO algorithm median acceptance rate is 67.34% better than the A2C algorithm. A2C outperforms PPO only in the scenario where network servers had a greater number of computing resources. In this case, the A2C is 1% better than the PPO.

Multi-party blind quantum computation protocol with mutual authentication in network

Blind quantum computation (BQC) can ensure a client with limited quantum capability safely delegates computing tasks to a remote quantum server. In order to resist attacks from ignoring identity authentication in BQC protocols, it is necessary to guarantee the legality of both clients and servers in a multi-party BQC network. So we propose a multi-party BQC protocol involving three phases to distribute shared keys and authenticate identities. Firstly, by using the advantages of measurement device independent quantum key distribution (MDI-QKD), the registered client and the assigned server could share the initial key safely in registration phase. Secondly, with the help of semi-honest certificate authority (CA), mutual identity authentication phase realizes the two-way authentication from both sides through the shared key simultaneously. Thirdly, in the blind quantum computing phase, a registered client can complete his computing task by just measuring the qubits from the assigned server rather than preparing the qubits. Moreover, combined with first-in-first-out (FIFO) principle, clients’ authentication and blind quantum computing can be processed in parallel. The protocol can also be applied in other multi-party BQC protocols with the universality of resource states. Compared with other BQC protocols, the reliability of the protocol with identity authentication is guaranteed, and the efficiency will be significantly reflected in real experiments.

Research on Innovation of Ideological and Political Teaching Mode for Chemical Engineering Courses Based on Big Data Technology

Aiming at the current application of big data technology, this paper proposes a micro-video-based ideological and political teaching and learning system for chemical engineering courses. In order to realize the system, relying on the course of “Light Chemical Equipment and Design”, part of the main content of the micro video is designed, and the ideological and political content is run through the entire course; Then, design the system network topology, client and server functions, and finally deploy the system on the Alibaba Cloud server, and design an evaluation method to explore the application effect of the system.

A Survey of Computational Offloading in Cloud/Edge-based Architectures: Strategies, Optimization Models and Challenges

In recent years, mobile devices have become an essential part of daily life. More and more applications are being supported by mobile devices thanks to edge computing, which represents an emergent architecture that provides computing, storage, and networking capabilities for mobile devices. In edge computing, heavy tasks are offloaded to edge nodes to alleviate the computations on the mobile side. However, offloading computational tasks may incur extra energy consumption and delays due to network congestion and server queues. Therefore, it is necessary to optimize offloading decisions to minimize time, energy, and payment costs. In this article, different offloading models are examined to identify the offloading parameters that need to be optimized. The paper investigates and compares several optimization techniques used to optimize offloading decisions, specifically Swarm Intelligence (SI) models, since they are best suited to the distributed aspect of edge computing. Furthermore, based on the literature review, this study concludes that a Cuckoo Search Algorithm (CSA) in an edge-based architecture is a good solution for balancing energy consumption, time, and cost.

Intelligent feature selection using particle swarm optimization algorithm with a decision tree for DDoS attack detection

The explosive development of information technology is increasingly rising cyber-attacks. Distributed denial of service (DDoS) attack is a malicious threat to the modern cyber-security world, which causes performance disruption to the network servers. It is a pernicious type of attack that can forward a large amount of traffic to damage one or all target’s resources simultaneously and prevents authenticated users from accessing network services. The paper aims to select the least number of relevant DDoS attack detection features by designing an intelligent wrapper feature selection model that utilizes a binary-particle swarm optimization algorithm with a decision tree classifier. In this paper, the Binary-particle swarm optimization algorithm is used to resolve discrete optimization problems such as feature selection and decision tree classifier as a performance evaluator to evaluate the wrapper model’s accuracy using the selected features from the network traffic flows. The model’s intelligence is indicated by selecting 19 convenient features out of 76 features of the dataset. The experiments were accomplished on a large DDoS dataset. The optimal selected features were evaluated with different machine learning algorithms by performance measurement metrics regarding the accuracy, Recall, Precision, and F1-score to detect DDoS attacks. The proposed model showed a high accuracy rate by decision tree classifier 99.52%, random forest 96.94%, and multi-layer perceptron 90.06 %. Also, the paper compares the outcome of the proposed model with previous feature selection models in terms of performance measurement metrics. This outcome will be useful for improving DDoS attack detection systems based on machine learning algorithms. It is also probably applied to other research topics such as DDoS attack detection in the cloud environment and DDoS attack mitigation systems.

A Security Log Analysis Scheme Using Deep Learning Algorithm for IDSs in Social Network

Due to the complexity of the social network server system, various system abnormalities may occur and in turn will lead to subsequent system failures and information losses. Thus, to monitor the system state and detect the system abnormalities are of great importance. As the system log contains valuable information and records the system operating status and users’ behaviors, log data in system abnormality detection and diagnosis can ensure system availability and reliability. This paper discloses a log analysis method based on deep learning for an intrusion detection system, which includes the following steps: preprocess the acquired logs of different types in the target system; perform log analysis on the preprocessed logs using a clustering-based method; then, encode the parsed log events into digital feature vectors; use LSTM-based neural network and log collect-based clustering methods to learn the encoded logs to form warning information; lastly, trace the source of the warning information to the corresponding component to determine the point of intrusion. The paper finally implements the proposed intrusion detection method in the server system, thereby improving the system’s security status.

Functional Validation of a Pathogenic Missense Variant in Human Filamin C Cardiomyopathy through Disruption of a Zebrafish Homologue Recapitulates Cardiac Disease

Purpose We recently performed whole-genome sequencing (WGS) of an East Asian proband and family members with diverse cardiac diseases, including arrhythmias, cardiomyopathies, premature heart failure requiring transplant, and identified a pathogenic missense variant (p.G2151S) in exon 39 of the filamin C gene, FLNC. In this study, we aim to demonstrate recapitulation of the pathogenic effects of the variant using a zebrafish (Danio rerio) model. Methods The proband, 3 other affected and 3 unaffected family members of a four-generation nonconsanguineous Hong Kong Chinese pedigree of 25 individuals were sequenced by WGS with 30X coverage. Single nucleotide variants and insertion-deletions that met our inclusion criteria were shortlisted, and bioinformatic analysis and variant prioritization were performed using Kaviar, gnomAD and dbNSFP databases to identify damaging variants in known cardiac-specific genes. The study was approved by the university/hospital's institutional review board. Sequence alignment of the pathogenic gene region with the zebrafish homologue was performed using the GENESTREAM network server, Institut de Genetique Humaine, Montpellier, France. Knockdown of the region was carried out using microinjection of morpholino oligonucleotides (MO), or control oligomers. Embryonic lethality, survival after birth, animal and cardiac morphology, heart rate, and histopathology were analyzed. Results Using a zebrafish model, we performed sequence alignment and identified the corresponding point mutation in flnca and flncb. Knockdown of the homologous sequences using antisense morpholino oligonucleotides resulted in stunted growth and development of the animal, cardiomegaly, pericardial edema, dose-dependent increase in embryonic lethality (survival rates of 95.8%, 46.6% and 9.7% at day 3 post-fertilization for control oligomer (n=24), 0.1 ng MO (n=88) and 0.5 ng MO (n=154), respectively), and reduced heart rate (134, 108 and 103 beats per minute, respectively; P Conclusion Damaging effects of the missense variant in human filamin C cardiomyopathy have been confirmed in the homologous region in this zebrafish model. The variable phenotypic manifestations within this East Asian family remain to be elucidated.