Service Classes Research Articles

Лексико-семантическое поле вербализаторов концепта КОНЬ/ЛОШАДЬ: общекультурная и региональная специфика

The aim of study is to reveal the common linguocultural character and regional peculiarities of the concept “horse” by analyzing the structure and content of the semantic field of this concept`s verbalizers. The novelty of the research is due to the fact that we have used the dictionaries of local dialects of the Kuban region as a source of lexical items — verbalizers of the semantic field of the concept “horse”. The results obtained show that the general cultural concept “horse” is one of the significant concepts in a retrospective picture of the world of the Russian people. For native speakers, horse was mainly a domestic animal — used for field work or transportation — and, therefore, an emotionally and materially valuable object. At the language level, we could reveal the regional specific character of the concept “horse”. For the Kuban Cossacks a horse was a charger. The charger was a symbol of the military character of Cossacks as the service class whose main duty was to stand guard over the state borders of Russia. The value constituent of the concept was even more significant than in the all-Russian language picture of the world — a horse was of great importance in a Cossack’s life and indicated a high social status.

Multi-Connectivity in 5G and Beyond Non-Terrestrial Networks

The Fifth Generation (5G) communications systems aim to serve such service classes as Ultra-Reliable Low Latency Communications (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine-Type Communications (mMTC). To meet the growing requirements posed to mobile networks, satellites can be used to complement the Terrestrial Networks (TNs). To increase the efficiency of the satellite communications involved, bandwidth-efficient techniques should be used. Multi- Connectivity (MC) is one such technique. In MC, a User Equipment (UE), for example, a smartphone, can be connected to multiple Next Generation Node Bs (gNBs) simultaneously. In this paper, an adaptive MC activation scheme for throughput enhancement in 5G and beyond Non-Terrestrial Networks (NTNs) is presented. The algorithm is evaluated by system simulations using different traffic split algorithms, namely, even split, data request per connection and per gNB algorithms. In the considered simulation scenario, the maximum throughput enhancement of 9.1%, compared to when MC is turned off, is experienced when using the adaptive Secondary Node (SN) addition algorithm with the combination of the data request algorithms.

Клиентоориентированный подход в оценке качества сайта грузоперевозочной компании при обслуживании частных клиентов

The article proposes a methodology for assessing the quality of the site of a transport and logistics company from the standpoint of customer focus on the basis of a survey of private clients-shippers. The first part of the article contains the rationale for the methodology. The second part of the article contains its approbation on the example of the company JSC RZD-Logistics. The methodology is based on expert judgment. It is subject to general and special criteria proposed by the author as indicators of the quality of the carrier’s website. The results of the survey of respondents outlined the areas of work to improve the section of the website of the Russian Railways Logistics company in terms of website content, transparency of the algorithm for calculating the cost of services, the possibility of choosing a class of service and getting answers to typical questions of interest to the client. Thanks to the analysis of the comments of the interviewed experts regarding the ratings set, it became possible to study the reasons for their deviation from the maximum values and develop responses to improve the company’s website from the standpoint of customer focus.

Providing QoS to High Performance Distributed Applications in Software-Defined Networks (SDNs)

Nowadays, there is an ever-increasing number of applications that have strict quality of service (QoS) requirements. The ability to define QoS requirements in traditional networks is limited due to the complex control of these environments. Modification of the existing infrastructure has high administrative costs, since the subsystem of each service component must have its resources managed by a centralized organization to guarantee users the level of QoS that each of them requires. The specification of quality of service (QoS) requirements in traditional networks is limited by the high administrative cost of these environments. Nevertheless, newer network paradigms, as Software-Defined Networks (SDNs), simplify and relaxes the management of networks. In this sense, SDN can provide a simple/effective way to develop QoS provisioning. This approach allows the specification of classes of service and also negotiates the QoS requirements between applications and the SDN network controller. The SDN controller, in turn, monitors the network and adjusts its performance through resource reservation and traffic prioritization. Have developed a QoS provision architecture for high performance distributed applications exploiting the capabilities of SDN. In this architecture, simple software components enable a process of message exchanging that includes reservation request and reservation confirmation. This procedure allows a network controller to configure QoS queues to individual packet flows. The developed a proof-of-concept of proposal and experimental results show that the additional routines present low overhead, whereas – for a given test application — observes a reduction of up to 47% in transfer times. Despite the specific scenario evaluated, results indicate our solution feasibility. Future works include the development of renegotiation and adaptation functions.

Fog-RAN Enabled Multi-Connectivity and Multi-Cell Scheduling Framework for Ultra-Reliable Low Latency Communication

Ultra-Reliable Low Latency Communication (URLLC) is a newly introduced service class targeting emerging Internet-of-Things (IoT) application scenarios. This paper assumes an interference-limited Fog Radio Access Network (F-RAN) setup composed of multiple Remote Radio Heads (RRHs) equipped with multiple antennas serving single-antenna users. F-RAN facilitates collaborative solutions while reducing delay by pushing the network capabilities beyond the edge. By leveraging diversity, RRHs may cooperate through silencing, reducing interference, or joint transmission strategies such as maximal ratio transmission. We derive closed-form outage probability expressions and attain their diversity gain. We validate the derived analytical results through extensive numerical simulations. Furthermore, we propose a mini-slots-based scheduling framework to serve URLLC users within their fixed latency budget. In an interference-limited regime with the proposed scheduling framework, we show that a performance gain is superior when RRHs cooperate compared to when they do not. We briefly discuss the cost of reliability, i.e., the impact on the system’s average sum throughput under cooperation. Moreover, numerical results verify that cooperating transmission schemes boost transmission reliability with a significantly improved latency performance at the cost of reduced system’s average sum throughput.

6G-Enabled Internet of Things: Vision, Techniques, and Open Issues

There are changes in the development of wireless technology systems every decade. 6G (sixth generation) wireless networks improve on previous generations by increasing dependability, accelerating networks, increasing available bandwidth, decreasing latency, and increasing data transmission speed to standardize communication signals. The purpose of this article is to comprehend the current directions in 6G studies and their relationship to the Internet of Things (IoT). Also, this paper discusses the impacts of 6G on IoT, critical requirements and trends for 6G-enabled IoT, new service classes of 6G and IoT technologies, and current 6G-enabled IoT studies selected by the systematic literature review (SLR) method published from 2018 to 2021. In addition, we present a technical taxonomy for the classification of 6G-enabled IoT, which includes self-organization systems, energy efficiency, channel assessment, and security. Also, according to the articles reviewed, we consider the evaluation factors in this domain, including data transmission, delay, energy consumption, and bandwidth. Finally, we focus on open issues and future research challenges in 6G-enabled IoT. To mention important future challenges and directions, we can point to migration, data storage, data resource, data security, data sharing, data offloading, availability, scalability, portability, user experience, reliability, authentication, and authorization.

SOFTWARE IMPLEMENTATION FOR MONITORING ENTERPRISE IT INFRASTRUCTURE USING MICROSOFT VISUAL STUDIO

Enterprise information security specialists need separate functions and services (monitoring, management, copying) that interact with any class of server and client applications and have a common interface. There is a general trend towards its appearance. It is the consideration of this problem and its programmatic implementation that is considered in the framework of the presented study.

The Making of a Teenage Service Class: Poverty and Mobility in an American City. RanitaRay, Oakland, California: University of California Press, 2018, 286 pp.

Anthropology & Education QuarterlyEarly View Book Review The Making of a Teenage Service Class: Poverty and Mobility in an American City. Ray, Ranita, Oakland, California: University of California Press, 2018, 286 pp. Tarsha I. Herelle, Corresponding Author Tarsha I. Herelle therell1@jh.edu orcid.org/0000-0003-0497-3830 Johns Hopkins UniversitySearch for more papers by this author Tarsha I. Herelle, Corresponding Author Tarsha I. Herelle therell1@jh.edu orcid.org/0000-0003-0497-3830 Johns Hopkins UniversitySearch for more papers by this author First published: 16 December 2021 https://doi.org/10.1111/aeq.12415Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation

Energy Efficient Optimal Resource Allocation in Multi-RAT Heterogeneous Network

ABSTRACT In this paper, we address the frequent problem associated with user association and resource allocation along with optimal deployment of base station (BS) in multiple radio access technology (Multi-RAT)-assisted heterogeneous network (Het-Net). Considering real time user scenarios, optimal resource allocation in Het-Net while ensuring each user’s minimum required data rate is a challenging task to be performed. Here, we propose a novel algorithm with a well-known and efficient meta-heuristic optimization technique to resolve the aforementioned problem. We use hybrid memory-based dragonfly algorithm with differential evolution (DADE) for its excellent convergence characteristics. Extensive simulations are performed to determine the optimal network utility under the consideration of nonuniform user distribution and fine-tuning their respective service class and contract of association parameters. Simulation results depict that the proposed algorithm improves the overall network utility in terms of radio resource utilization and energy consumption while satisfying the user demands. Comparative analysis of the proposed technique with the other state-of-the-art algorithm depicts the superiority of the proposed algorithm in terms of accuracy and consistency. We also perform optimal multi-RAT cell planning under the above constraints including a network blackout scenario. The algorithm ensures each user coverage by optimally allocating the available resources.

Latency-aware service provisioning in survivable multilayer IP-over-elastic optical networks to support multi-class of service transmission

To meet the requirements of the emerging applications, future networks must transmit multiple class of services traffic with diverse quality of service (QoS) requirements. In this paper, we formulate an integer linear programming (ILP) to design multilayer internet protocol (IP) over elastic optical networks (EONs), IP-over-EONs to support multi-class services with different latency and availability requirements. The objective of the ILP is to minimize capital expenditure (CAPEX) and spectrum usage while satisfying all QoS constraints in terms of latency and survivability mechanism. Furthermore, a heuristic algorithm is proposed to solve this problem in large-scale networks. We compare the results of the ILP formulation and the proposed heuristic algorithm in a 5-node network, where our results reveal that the performance gaps between the ILP and heuristic algorithm are on average 2.4% and 9.8% in terms of CAPEX and occupied spectrum, respectively, while the heuristic algorithm has a lower run time. Moreover, we quantitatively analyze the effects of end-to-end latency on the CAPEX of multilayer IP-over-EONs. Our results reveal that serving latency-sensitive demands increases the CAPEX of the network. This is because all-optical connections (i.e., single-hop IP connections) must be established to meet the delay constraints, which in turn leads to a higher CAPEX and occupied spectrum. Furthermore, we compare the effects of using different types of transponders in three practical topologies and evaluate the impacts of the length of the topology links on CAPEX and spectrum usage. Finally, we propose the best transponder types that could be used for each topology based on the gathered results.

Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator

The problem of transforming Real World Data into Real World Evidence is becoming increasingly important in the frameworks of Digital Health and Personalized Medicine, especially with the availability of modern algorithms of Artificial Intelligence high computing power, and large storage facilities.Even where Real World Data are well maintained in a hospital data warehouse and are made available for research purposes, many aspects need to be addressed to build an effective architecture enabling researchers to extract knowledge from data.We describe the first year of activity at Gemelli Generator RWD, the challenges we faced and the solutions we put in place to build a Real World Data laboratory at the service of patients and health researchers. Three classes of services are available today: retrospective analysis of existing patient data for descriptive and clustering purposes; automation of knowledge extraction, ranging from text mining, patient selection for trials, to generation of new research hypotheses; and finally the creation of Decision Support Systems, with the integration of data from the hospital data warehouse, apps, and Internet of Things.

Augmenting DiffServ operations with dynamically learned classes of services

In this work, we provide a Machine Learning framework for augmenting the Differentiated Services (DiffServ) protocol with fine-grained dynamic traffic classification. The framework is called L-DiffServ. It is composed of two classification algorithms able to detect the QoS classes of incoming packets only looking at three packet header fields; the first algorithm, referred to as Inter-L-DiffServ, is a semi-supervised classification procedure able to replicate DiffServ classification; the second one, referred to as Intra-L-DiffServ, is an unsupervised algorithm for intra-class classification, useful for classes taking large portions of the overall traffic. We apply the latter to the low priority best-effort class. The performance evaluation shows that our solution is able to dynamically classify packets and to detect new QoS sub-classes hence adapting to traffic aggregate characteristics. We also show that network resource management can be improved exploiting the new generated QoS sub-classes: two active queue management algorithms based on WRED and CHOKe show a reduction of the number of sessions affected by packet losses up to 40% with respect to the legacy DiffServ procedure.

A price-aware congestion control protocol for cloud services

In current infrastructure-as-a service (IaaS) cloud services, customers are charged for the usage of computing/storage resources only, but not the network resource. The difficulty lies in the fact that it is nontrivial to allocate network resource to individual customers effectively, especially for short-lived flows, in terms of both performance and cost, due to highly dynamic environments by flows generated by all customers. To tackle this challenge, in this paper, we propose an end-to-end Price-Aware Congestion Control Protocol (PACCP) for cloud services. PACCP is a network utility maximization (NUM) based optimal congestion control protocol. It supports three different classes of services (CoSes), i.e., best effort service (BE), differentiated service (DS), and minimum rate guaranteed (MRG) service. In PACCP, the desired CoS or rate allocation for a given flow is enabled by properly setting a pair of control parameters, i.e., a minimum guaranteed rate and a utility weight, which in turn, determines the price paid by the user of the flow. Two pricing models, i.e., a coarse-grained VM-Based Pricing model (VBP) and a fine-grained Flow-Based Pricing model (FBP), are proposed. The optimality of PACCP is verified by both large scale simulation and small testbed implementation. The price-performance consistency of PACCP are evaluated using real datacenter workloads. The results demonstrate that PACCP provides minimum rate guarantee, high bandwidth utilization and fair rate allocation, commensurate with the pricing models.

A Network Analysis on Cloud Gaming: Stadia, GeForce Now and PSNow

Cloud gaming is a class of services that promises to revolutionize the videogame market. It allows the user to play a videogame with essential equipment while using a remote server for the actual execution. The multimedia content is streamed through the network from the server to the user. Hence, this service requires low latency and a large bandwidth to work properly with low response time and high-definition video. Three of the leading tech companies (Google, Sony, and NVIDIA) entered this market with their products, and others, like Microsoft and Amazon, are also launching their platforms. However, these companies have released little information about their cloud gaming operation and how they utilize the network. In this work, we study cloud gaming services from the network point of view. We collect more than 200 packet traces under different application settings and network conditions from a broadband network to poor mobile network conditions, for 3 cloud gaming services, namely Stadia from Google, GeForce Now from NVIDIA and PS Now from Sony. We analyze the employed protocols and the workload that they impose on the network. We find that GeForce Now and Stadia use the RTP protocol to stream the multimedia content, with the latter relying on the standard WebRTC APIs. Depending on the network and video quality, they result in bandwidth-hungry services consuming up to 45 Mbit/s. PS Now instead uses only undocumented protocols and never exceeds 13 Mbit/s. 4G mobile networks can often sustain these loads, while traditional 3G connections struggle. The systems quickly react to deteriorated network conditions, and packet losses up to 5% do not cause a reduction in resolution.

The economic impacts of COVID-19 on the city tourism-related services; the case of Tehran

PurposeThe COVID-19 pandemic has had an extremely destructive impact on the tourism and hospitality industry. However, there is a gap in the literature on measuring the economic impact of the pandemic on employees of urban tourism industries by class. Accordingly, this paper aims to investigate the perceived economic impact of the COVID-19 by workers of urban tourism-related services in Tehran.Design/methodology/approachBased on stratified sampling, a Web-based survey was carried out among 383 employees of tourism industries. To measuring the perceived impact and evaluate statistically significant differences, one samples t-test, one-way ANOVA and post hoc test were used.FindingsThe study findings revealed that employees of tourism-related services perceived that COVID-19 outbreak has a huge negative impact on the tourism industries in terms of earnings reduction. However, the perceived impact on job losses and reduction of hours of work was non-monotonic.Originality/valueIdentifying the negative effects of the COVID-19 outbreak on city tourism by industries and their subclasses can contribute to more effective interventions to support and recover the tourism industries. In other words, it is essential to prioritize support for different class of services, to fair allocation of bailout monies and to improve their resilience against crises and shocks.

Optimizing package express operations in China

We explore a novel optimization model to support the planning and operation of an air service network at a package express carrier in China. The model simultaneously considers ground and air transportation, company-owned aircraft capacity and belly capacity purchased on commercial aircraft, multiple service classes for packages, and packages becoming available throughout the day. A column generation based algorithm is devised to solve real-life problems with more than 650,000 timed origin-destination demands (packages), a service network with 150 hubs at airports, and an air cargo fleet of 60 aircraft of varying capacities. An extensive computational study shows the efficacy of the model and algorithm, and provides managerial insight. In particular, we see that considering ground transportation when designing an air service network is critical to improving operational efficiency and company profits as well as to extending service coverage. Furthermore, we find that in an environment with ample and reasonably-priced capacity on commercial aircraft, an air service network design based on single-flight shipments can be more effective than the commonly used star network design with multi-flight shipments.

Online learning for hierarchical scheduling to support network slicing in cellular networks

We study a learning-based hierarchical scheduling framework in support of network slicing for cellular networks. This addresses settings where users and/or service classes are grouped into slices, and resources are allocated hierarchically. The hierarchy is implemented by combining a slice-level scheduler which allocates resources to slices, and flow-level schedulers within slices which opportunistically allocate resources to users/services. Optimizing the slice-level scheduler to maximize system utility is typically hard due to underlying heterogeneity and uncertainty in user channels and performance requirements. We address this by reformulating the problem as an online black-box optimization where slice-level schedulers (parameterized by a weight vector) combined with flow-level schedulers result in user/service level stochastic rewards representing performance fitness; the goal is to learn the best weight vector. We develop a bandit algorithm based on queueing cycles by building on Hierarchical Optimistic Optimization (HOO). The algorithm guides the system to improve the choice of the weight vector based on observed rewards. Theoretical analysis of our algorithm shows a sub-linear regret with respect to an omniscient genie. Finally through simulations, we show that the algorithm adaptively learns the optimal weight vectors when combined with opportunistic and/or utility-maximizing flow-level schedulers.

CPU Overclocking: A Performance Assessment of Air, Cold Plates, and Two-Phase Immersion Cooling

Computing capacity has always been on the upward climb due to the constant technological improvements in semiconductor manufacturing and packaging industry. This growth in computing capability is usually accompanied by a steep rise in heat flux density associated with the electronic component (CPUs or GPUs, for example). High-performance computing (HPC) data centers often employ several of these high-performance devices for crunching their enormous artificial intelligence (AI) or scientific computing workloads. State-of-the-art air cooling technologies throttle after a certain heat flux level and would require bigger heat sinks driving enormous airflow through it, which may not be desirable from a data center operation standpoint. Hence, a lookout for advanced thermal management techniques is quite imperative. In this article, a commercially available intel overclockable CPU i9-9900k was exercised with high performance workloads while employing and evaluating three different cooling technologies namely air-cooled, cold plates, and two-phase (2P) immersion. The high heat carrying capacity associated with cold plates and 2P immersion techniques outperformed the air-cooled solution by constantly yielding higher CPU clock rates up to 41% utilizing cold plates and 51% employing 2P immersion. The performance of cold plates and 2P immersion was evaluated at different functional points (different coolant operating temperatures and 2P coolant types for example) to understand how the technologies would respond in a legacy or modern data center operation. It was again observed that both 2P immersion technology and cold plates provided the least thermal resistance (as low as 0.247 °C/W) path to heat transfer and therefore provided higher computational performance and efficiency compared to air cooling. Further improvement in performance is not limited by cooling but by packaging constraints. Advanced packaging techniques coupled with minimizing the interfacial resistance can reap enhanced performance and energy efficiency gains using these modes of cooling. This article demonstrates the potential increase in virtual machine (VM) performance that can be attained using cold plates or 2P immersion. Authors recommend adoption of liquid cooling in early chip design and architecture phases. Finally, the article provides a generic overclocking methodology that the industry can use to qualify postturbo server class chip performance.

코로나19 팬데믹 전후의 항공 서비스품질 인식 비교: 토픽모델링 및 의미연결망분석을 중심으로

This study investigates service quality perceptions of airlines before and after COVID-19 pandemic using topic modeling and semantic association analysis. To this end, this study was conducted by collecting customer’s reviews of 4 airlines selected by TripAdvisor, and total 15,716 reviews were collected. The data was followed by text preprocessing, LDA (latent dirichlet allocation) using Python, then semantic association analysis by Python and Gephi. Through LDA, one of the topic modeling methodologies was confirmed that ground service, in-flight service, service consistency, frequent-flyer, business class, connection, entertainment, reservation, food and beverage, legroom, human service, and economy class were the topics before the COVID-19 pandemic. After the COVID-19 pandemic, the topics were selected as reservation service, human service, quarantine, ground service, seat class, refunds, and changes. In addition, we found that words related to in-flight service such as flight, class, service, food, and seating were strongly linked before the COVID-19 using semantic association analysis. But after the COVID-19 pandemic, the quality of ground service related to ticket reservation, refund, cancellation was important.

End-of-Life Healthcare Service Needs Among Children With Neurological Conditions: A Latent Class Analysis.

BACKGROUND: At the end of life, children with neurological conditions have complex healthcare needs that can be met by providing care of their life-limiting conditions concurrently with hospice care (ie, concurrent care). Given the limited literature on concurrent care for children with neurologic conditions, this investigation aimed to identify patterns of nonhospice, healthcare service needs and to assess characteristics of children within each group. METHODS: A nationally representative sample children with neurological conditions enrolled in concurrent hospice care was used. Latent class analysis and descriptive statistics were calculated to identify patterns of healthcare needs and characteristics of children within the groups. A subgroup analysis of infants was conducted. RESULTS: Among the 1601 children, the most common types of services were inpatient hospitals, durable medical equipment, and home health. Two classes of service needs were identified: moderate intensity (58%) and high intensity (42%). Children in the moderate-intensity group were predominantly between 1 and 5 years old, male, White, and non-Hispanic. The most common neurological condition was central nervous system degeneration. They also had significant comorbidities, mental/behavioral health conditions, and technology dependence. They commonly resided in urban areas in the South. Children in the high-intensity group had a wide range of neurological conditions and high acuity. The subgroup analysis of infants indicated a different neurological profile. CONCLUSIONS: Two distinct classes of nonhospice, healthcare service needs emerged among children with neurological conditions at the end of life. The groups had unique demographic profiles.