Utilization Of Infrastructure Research Articles

COST OPTIMIZATION METHOD FOR INFORMATIONAL INFRASTRUCTURE DEPLOYMENT IN STATIC MULTI-CLOUD ENVIRONMENT

Context. In recent years, the topic of deploying informational infrastructure in a multi-cloud environment has gained popularity. This is because a multi-cloud environment provides the ability to leverage the unique services of cloud providers without the need to deploy all infrastructure components inside them. Therefore, all available services across different cloud providers could be used to build up information infrastructure. Also, multi-cloud offers versatility in selecting different pricing policies for services across different cloud providers. However, as the number of available cloud service providers increases, the complexity of building a costoptimized deployment plan for informational infrastructure also increases. Objective. The purpose of this paper is to optimize the operating costs of information infrastructure while leveraging the service prices of multiple cloud service providers. Method. This article presents a novel cost optimization method for informational infrastructure deployment in a static multicloud environment whose goal is to minimize the hourly cost of infrastructure utilization. A genetic algorithm was used to solve this problem. Different penalty functions for the genetic algorithm were considered. Also, a novel parameter optimization method is proposed for selecting the parameters of the penalty function. Results. A series of experiments were conducted to compare the results of different penalty functions. The results demonstrated that the penalty function with the proposed parameter selection method, in comparison to other penalty functions, on average found the best solution that was 8.933% better and took 18.6% less time to find such a solution. These results showed that the proposed parameter selection method allows for efficient exploration of both feasible and infeasible regions. Conclusion. A novel cost optimization method for informational infrastructure deployment in a static multi-cloud environment is proposed. However, despite the effectiveness of the proposed method, it can be further improved. In particular, it is necessary to consider the possibility of involving scalable instances for informational infrastructure deployment.

Policy interventions and urban characteristics in modeling electric vehicle charging infrastructure utilization

The surge in electric vehicles adoption necessitates understanding the impact of policy interventions on public electric vehicle charging infrastructure in urban areas. This research investigates the influence of pricing frameworks on the usage of public charging facilities by analyzing both behavioral and spatial attributes of these infrastructures. Utilizing open data from Palo Alto, United States, this study employs descriptive statistical methods and interpretable machine learning approaches to scrutinize the relationship between policy initiatives and charging behaviors. The analysis underscores the significance of spatial attributes on charging behaviors. Policy interventions yield noticeable alterations in charging metrics, with locations near commercial hubs showing higher utilization, while local and frequent users resist fee adjustments. The research emphasizes the necessity for customized strategies to optimize infrastructure development and management, offering a framework for policymakers and stakeholders in sustainable urban transportation. Future research should explore similar interventions in diverse urban settings using real-time data and advanced optimization techniques to better tailor policies to the unique characteristics of specific facilities.

UTILIZATION OF CLASSROOM ENVIRONMENT AND ITS EFFECTS ON ACADEMIC PERFORMANCE IN SUB COUNTY LEVEL SECONDARY SCHOOLS IN UASIN GISHU COUNTY, KENYA

School infrastructure is a key pillar for effective learning process in any educational institution. The purpose of this study was to utilization of classroom environment affect academic performance in Sub County level secondary schools in Uasin Gishu County. This study was guided by Production theory. The study was guided by pragmatism philosophy and adopts descriptive survey research design. The study area was carried out in Uasin Gishu County. The target population of the study was 18960 respondents. Stratified simple random and purposive sampling was employed to select the respondents. The sample size was 530 respondents comprising of 48 principals, 97 teachers, 384 students and a Quality Assurance and Standards Officer The data collection instruments were questionnaire, interview schedules and observation check list. A pilot study was carried out in five public schools in the neighboring Trans Nzoia County to check on the consistence of the instruments. The researcher yielded both quantitative and qualitative data. Quantitative data was analyzed using descriptive statistics that resulted in percentages and frequencies that were represented in tables, pie charts and bar graphs and inferential statistics. Qualitative data was analyzed thematically. Therefore, from the research findings based on the multiple regression models, the coefficient of determination (R squared) of 0.441 showed that 44.1% of the variation in student performance was explained by the utilization of selected school infrastructure. The study found out that the classroom environment influenced academic performance. The study concluded that the more conducive the classroom environment the higher the academic performance. The research recommended that sub county level secondary schools management in Uasin Gishu County should develop infrastructural development initiatives to solve the inadequacy of these facilities and enhance their utilization in the schools. There should be consistent routine supervision of quality of facilities in schools done by the government to ensure that schools are operating with good facilities that would help the learners. KEY WORDS: School infrastructure utilization of classroom environment, academic performance

Institutional fragmentation and smallholder irrigation performance: a case of Mkoba and Silalatshani Irrigation Schemes, Zimbabwe

ABSTRACT The study sought to understand the influence of institutions on irrigation infrastructure utilization using two irrigation schemes in Zimbabwe. The results show that duplication, overlap and fragmentation of mandates across institutions reduce the influence of formal rules while increasing the influence of socially embedded institutions yet without an official change of roles. Such conditions weaken cooperation among the irrigators and hinder infrastructure utilization and maintenance. Attention to reducing institutional fragmentation while giving space for collective efforts and other ways of knowing and doing irrigation may improve infrastructure utilization in irrigated agriculture.

Digital twins for resource optimization in multi-purpose ports: A design approach for data-driven decision making

Abstract Multi-purpose ports’ ef/icient and sustainable operation relies on seamless coordination and decision-making among multiple organizations. This paper underscores the critical importance of forecasting resource and infrastructure utilization for informed operational, tactical, and strategic decision-making. The proposed approach draws on digital twin technology to enable collaborative decision-making by modeling complex port environments to enable shared situational awareness among stakeholders. Illustrated through a collaborative project involving the RISE Research Institutes of Sweden, National University of Singapore, Grieg Connect, Umeå University, Kvarken Ports Umeå, and INAB, we propose a digital twin design to empower the port as a decision-maker in multi-organizational settings to proactively plan and optimize its utilization of present and future resources.

Increasing the theoretical and practical capacity of the Eastern Operating Domain by means of advanced train separation systems

Aim. Currently, the mainline railway network does not fully meet the market’s needs in terms of capacity, which is why every day JSC RZD is forced to reject 2 to 4% of applications for the transportation of goods across routes passing through limited-capacity railway infrastructure facilities. Increasing the practical capacity of railway lines by building additional tracks at stations and on open lines is unacceptably long and requires massive capital investments. Methods. It is proposed to solve the above problem by increasing the utilization of railway infrastructure by reducing the succession times and station operation times in the traffic schedule by deploying moving block and virtual coupling. This approach allows reducing capital investments up to 32 times and the duration of project implementation up to 8 times with a comparable capacity increase. The paper details the functionality of train separation technology and its technical parameters. Conclusion. The train separation technology has been deployed and is in operation on the Far Eastern, Trans-Baikal, East Siberian, and Krasnoyarsk Railways, branches of JSC RZD, that are part of the Eastern Operating Domain of JSC RZD network. By the end of 2023, the total length of track covered by the train separation solution was 5,734 km. Conclusion. In 2023, the lines of the Eastern Operating Domain handled almost 36 thousand pairs of trains with reduced spacing. The train separation technology also allows improving the safety and the level of digitalisation of the transportation process.

User-needs-oriented shared DC charging resources optimal configuration and operation for improving EV penetration in old residential communities

The charging limitation of electric vehicles (EVs) in old residential communities (ORCs) derived from insufficient electricity capacity and parking spaces significantly impacts residents' quality of life and impedes urban sustainability. Considering economic and social factors, ORCs are likely to persist worldwide for the foreseeable future. To tackle these challenges, this paper proposes the scheme of shared DC fast charging stations to address the EV charging issues in ORCs. Specific methods involve minimizing the charging station spaces and charging infrastructure investment, while optimizing the photovoltaic (PV) and energy storage capacity. Additionally, a coordinated charging strategy based on dynamic price is designed to guide users in choosing the initial charging time, enhancing EV charging infrastructure utilization and EV penetration. This work is predominantly driven by human needs, integrating surveys on human behavior related to charging station locations and charging times. This case study is based on a real-life ORC in Shenzhen, China. By utilizing the proposed dynamic-pricing coordinated charging strategy tailored to the station capacity, the maximum EV penetration rate is to be achieved with satisfying EV charging demand. Furthermore, the implementation of dynamic pricing effectively guides users to adjust charging behavior, achieving the peak-shaving and valley-filling performance of load curves.

Evaluating urban population carrying capacity of Surat: probability-satisfaction approach

With around 36% of urbanisation, Indian cities are becoming saturated because of increased migration. This results in excessive utilisation of natural resources and physical infrastructure. Given its limited resources and infrastructure, it is important to evaluate an urban area’s true population carrying capacity. It helps planners and policymakers in managing cities better. This paper shows an estimation of the urban population carrying capacity of Surat City using the probability-satisfaction method with the help of factors like water treatment, solid waste management, employment, and hospital beds, which cover physical as well as social factors. Analysis shows the population carrying capacity of Surat City at different probability-satisfaction levels and compares it with the current scenario. Some important results are that when the probability-satisfaction level changes from acceptable to ideal level, Surat City’s health infrastructure has a maximum population carrying capacity of 112.1 to 102.2 million simultaneously and solid waste management has the lowest population carrying capacity, which is 4.7 to 3.9 million simultaneously. Also, it is observed that factors such as healthcare, wastewater treatment, and green area show less rigidness, while factors such as wastewater treatment, road area, and electricity show more rigidness in increasing carrying capacity.

Study of Office Facilities and Infrastructure Management in Supporting Administrative Activites at PT X

This research aims to determine the process of managing and utilizing office facilities and infrastructure in the Human Capital Department of PT X. The research method used is qualitative research descriptive analysis which involves researchers directly with the situation in the field so that researchers will more easily understand the processes carried out and the obstacles that occur in the management and utilization of office facilities and infrastructure carried out in the field. In this qualitative research, researchers observe objects in depth by collecting data about the object being studied and analyzing the data. Data collection techniques were obtained through observation, interviews, documentation and literature study. This research is expected to provide new knowledge regarding the process of managing and utilizing office facilities and infrastructure to support administrative activities carried out in the Human Capital Department of PT X.

From perception to action: Participatory water risk assessment in Nagaon District of Assam, India

This paper presents an examination of water risks in rural communities within Nagaon district of Assam, India. It employs a participatory approach to integrate community perspectives into the assessment process. The objective is to engage local people, gain an understanding of water risks they encounter in their everyday lives, and incorporate their opinions and experiences. To achieve this objective, the present analyses employ mixed methods, including stakeholder interviews, focus group discussions, household surveys, and community forums. Additionally, the development of the Participatory Water Risk Index (PWRI) provides a novel metric for quantifying and prioritizing water risks, aligning with local priorities and perspectives. The findings highlight the identification and evaluation of seven key parameters of water risk which include availability and sufficiency of drinking water, water quality issues, prevalence of water-related diseases, irrigation facilities, vulnerability to floods, and utilization of public water infrastructure. The study underscores the importance of community involvement and empowerment in addressing water-related challenges and promoting resilience. Policy recommendations derived from the research emphasize the need for context-specific approaches to water resource management, recognizing the unique challenges and strengths of each community within the district. The transferability of the PWRI to other locations offers substantial opportunities for improving water security worldwide, contingent upon careful consideration of context-specific parameters and community engagement. Thus, this paper contributes to the advancement of sustainable water management practices by integrating local knowledge into the assessment process, guiding future interventions to build a water-secure future for rural communities in Nagaon district and beyond.

Utilization of School Infrastructure and Its Influence on Students' Academic Performance in Public Secondary Schools in Makueni County, Kenya

School infrastructure forms a major part of housing learners in learning institutions. Utilization of school infrastructure in sheltering students against harsh weather conditions facilitates better attainment of school goals. Academic performance of learners in public county secondary schools in Makueni County was low a reason to carry out this study. The purpose of this study was to determine utilization of school infrastructure and its influence on academic performance in secondary schools in Makueni County. The objective of this study was to establish the level of utilization of school infrastructure and its influence on student’s academic performance in public county secondary schools in Makueni County. The study adopted a mixed methods research design, specifically a convergent parallel research design and was supported by Education Production Function Theory. Research instruments were questionnaires and interview schedules. Validity was achieved through expert judgment and reliability by test-retest technique. The target population was 60 county secondary schools, 60 principals and 1004 teachers. Simple random and stratified sampling techniques were used to select the schools while systematic and purposive sampling was used to sample teachers and principals. The selected sample was 30 county schools, 30 principals, and 286 teachers. Total number of respondents was 316. Descriptive and inferential statistics (means, percentages, and multiple regression) and thematic analysis worked out the level of utilization of school infrastructure on students’ academic performance. Multiple regression showed that Utilisation of school infrastructure showed moderate means of 3.86, which revealed moderate influence existed between its utilisation and students’ academic performance. Qualitative data revealed different themes meant that effective utilization of school infrastructure positively influenced students’ academic performance in county secondary schools in Makueni County. Major conclusion was that there was needed to ensure effective utilisation of school infrastructure in order to improve academic performance. This study would provide important information useful to education policy makers in formulating additional policies leading to effective utilisation of school infrastructure in secondary schools.

Assessing the priority levels of motivational factors for lecturers: A case study at vietnam national university, Hanoi

Creating motivation plays a crucial role in enhancing the job performance of university lecturers. This study aims to assess the motivational factors among lecturers at Vietnam National University, Hanoi. Five factors with 30 sub-factors influencing lecturers' motivation are considered in this study, including: salary and benefits (S_B, including 6 sub-factors), leadership skills (L_S, including 7 sub-factors), training and professional development opportunities (T_D, including 6 sub-factors), working conditions and environment (W_C, including 5 sub-factors), and recognition and rewards for accomplishments (R_R, including 6 sub-factors). This study applies the Analytic Hierarchy Process (AHP) method using a generalized fuzzy set for analysis. The steps of the fuzzy Analytic Hierarchy Process include: determining the comparison matrix of generalized trapezoidal fuzzy numbers, calculating the values of the fuzzy synthetic extents, and defining the weight vector of generalized trapezoidal fuzzy numbers. Data for the study were collected through expert interviews employing a questionnaire. The analysis results indicate that leadership skills and salary and benefits are the most influential factors on lecturers' motivation at Vietnam National University, Hanoi, followed by working conditions and environment, recognition and rewards for accomplishments, and training and professional development opportunities. Based on the analysis results, the study proposes several solutions to create motivation among lecturers at Vietnam National University, Hanoi, including: VNU-Hanoi and its affiliated units should prioritize bolstering the training of lecturer teams, particularly focusing on educational and training management staff. Furthermore, additional resources should be allocated to improve lecturer income, welfare, and rewards, alongside increasing investment and optimizing the utilization of infrastructure and educational materials for teaching and research purposes.

Software compliance in various industries using CI/CD, dynamic microservices, and containers

Abstract The microservices architecture is widely used in modern businesses due to its ability to offer speed, efficiency, adaptability, autonomy, and usability. On the other hand, this architectural paradigm demands a well-designed infrastructure for optimal container and cluster utilization. Establishing version control and a solid continuous integration/continuous deployment (CI/CD) infrastructure becomes critical for accelerating software delivery to production and ensuring code alignment with best practices. This scientific investigation investigates the development of customized, specialized CI/CD procedures for software compliance, expanding its scope beyond traditional software delivery to include the complexities of design, testing, and server deployment. By integrating real-world examples from industry and reviewing crucial tools, the goal is to provide organizations with empirical information to navigate the difficulties of modern software development. The upcoming research seamlessly fits into the larger discussion, providing a deep understanding of sophisticated structures and their design. This interdisciplinary research combines the scientific principles governing microservices with the practical specifics of CI/CD methodologies, giving businesses a thorough understanding and practical insight into the tools needed to navigate the diverse landscape of modern software engineering. Among the main findings of this research is a suggestion for a new approach known as General repository compliance operations.

Machine learning-augmented multi-arrayed fiber bragg grating sensors for enhanced structural health monitoring by discriminating strain and temperature variations

AbstractFiber optic sensors (FOS) in long-term structural health monitoring (SHM) have drawn significant attention due to their pivotal role in detecting defects and measuring structural performance in diverse infrastructures. While using FOS, temperature variation due to environmental factors is still considered one of the major challenges to isolating sensing parameters. To address this issue, we reported a machine learning (ML)-augmented multi-parameter sensing system that enables simultaneous detection of strain and temperature effects based on one single fiber Bragg gratings (FBGs) sensor for SHM. The initial phase entailed designing, fabricating, and characterizing a novel FBG sensor in the laboratory, incorporating a set of four FBGs, each distinguished by distinct Bragg wavelengths. In the next phase, ML algorithms are employed to separate temperature effects from strain variations. As a proof of concept, mechanical loading tests are conducted on the sensor, exposing the FBG portion to various temperature conditions. In the final phase, data collected from a post-tensioned concrete bridge embedded with both strain and temperature FBG sensors are utilized, and the developed ML models are applied to observe real-environment outcomes. Despite the limited feature points of collected FBG spectrums, the developed ML models effectively address cross-sensitivity issues induced by temperature perturbations. The long-term benefit of using FOS is that it will enable a better understanding and utilization of aging infrastructure. This will potentially reduce embodied carbon of infrastructure in the future and assist in the global efforts to achieve Net-Zero.

Exploring cost performance tradeoffs and uncertainties for electric- and autonomous electric trucks using computational experiments

The recent development of battery electric trucks (BETs) suggests that they could play a vital role in transitioning to zero-emission road freight. To facilitate this transition, it is important to understand under which conditions BETs can be a viable alternative to internal combustion engine trucks (ICETs). Concurrently, the advancement of autonomous driving technology adds uncertainty and complexity to analyzing how the cost competitiveness of future zero-emissions trucks, such as autonomous electric trucks (AETs) may develop. This study examines the cost performance of BETs and AETs compared to ICETs, and how it varies over different market and technology conditions, charging strategies, and transport applications. Focus is on heavy-duty tractor-trailer trucks operating full truckload shuttle-flows in Sweden. Due to the inherent uncertainty and interactions among the analyzed factors, the analysis is performed as computational experiments using a simulation model of BET, AET, and ICET shuttle flow operations and associated costs. In total, 19,200 experiments are performed by sampling the model across 1200 scenarios representing various transport applications and technical and economic conditions for sixteen charging strategies with different combinations of depot, destination, and en route charging. The results indicate that both BETs and AETs are cost competitive compared to ICETs in a large share of scenarios. High asset utilization is important for offsetting additional investment costs in vehicles and chargers, highlighting the importance of deploying these vehicles in applications that enable high productivity. The cost performance for BETs is primarily influenced by energy related costs, charging strategy, and charging infrastructure utilization. The AET cost performance is in addition heavily affected by remote operations cost, and costs for the automated driving system. When feasible, relying only on depot charging is in many scenarios the most cost-effective charging strategy, with the primary exceptions being highly energy-demanding scenarios with long distances and heavy goods in which the required battery is too heavy to operate the truck within vehicle weight regulations if not complemented by destination, or en route charging. However, many experiments do not lead to a reduced payload capacity for BETs and AETs compared to ICETs, and a large majority of the considered scenarios are feasible to operate with a BET or AET within current gross vehicle weight regulations.

Performance Evaluation of OpenStack Cloud Integrated with SDN Controller

The Internet is undergoing a revolution thanks to the development of a very popular recent technology trend - “Cloud Computing”. Large, small, and medium-sized businesses organizations, as well as individuals increasingly prefer to use cloud computing services due to their many advantages. Furthermore, the widespread implementation of cloud computing in large enterprises is an indicator of the technological advancement of information technology companies. It helps in the efficient allocation and utilization of infrastructure, power, and resources. In the meantime, centralized control provided by software-defined networking (SDN) allows for flexibility in operations and management. For that reason, there has been an adequate amount of research and application of combining these two objects over the years. In this study, we will evaluate network performance between virtual machines in terms of bandwidth, latency, and system performance interms of CPU, RAM, etc. in OpenStack with and without integration of SDN Controller - Tungsten Fabric (TF).

Digital transformation model for universities: A preliminary proposal

This article presents the results of a research project aimed at designing a theoretical model for digital transformation at the Autonomous University of Chile. The research methodology adopts a case study approach with a mixed method combining quantitative and qualitative techniques. This approach provides a nuanced and comprehensive understanding of the dimensions and factors shaping the digital transformation model, incorporating insights from 97 students and eight experts. The model comprises three phases, nine dimensions, and 54 indicators, emphasizing the integral connection between digital maturity and value creation for students. Key success determinants in the university's digital transformation processes are outlined, including the strategic approach, organizational culture, utilization of technological infrastructure, and technology management capabilities. Practical implications underscore the significance of acknowledging the impact of power relations during the digital transformation journey, influencing internal processes, IT governance coordination, roles of personnel in technical and managerial positions, teachers’ performance, and students' trust and loyalty to the institution. The article concludes by identifying factors that influence the institution's capacity to implement technological changes aligned with its strategic, technical, and logistical requirements. Future studies could redefine the roles of administrative, teaching, and student staff, fostering increased digital maturity, continuous innovation in services and processes, and the creation of new digital learning spaces centered around student needs.

Computing Diversity Paradigm for the Utilization of Unused Telephony and Marine Infrastructure

Computing systems play a significant role in data storage and processing. They enable the execution of data processing in data intensive domains such as astronomy. Therefore, having access to computing platforms is important for capital constrained astronomy organizations (CCAOs). The CCAOs need computing facilities to process the data acquired from an increasing number of telescopes. It is important to ensure that CCAOs execute the required computing at low cost. The research proposes the astronomy computing diversity paradigm to address this challenge for the CCAO. The proposed astronomy computing diversity paradigm uses disaggregated servers to design Class A and Class B data centres. The low–cost computing is realized by reducing computing platform operational costs. In the proposed solution, the Class A data centre uses unutilized telephony infrastructure intended for telephony. The Class B data centre is hosted in aquaria arrays. The Class B data centre reduces the cooling costs by using water in big aquaria. The performance evaluation is done focusing on the power usage effectiveness (PUE) and the angular resolution. Results show that using the aquaria-based data centre enhances the PUE and angular resolution by 22.3% and 40.4% on average, respectively. It reduces the operational costs by 79% on average.

Patient-Reported Outcome and Experience Measures in Advanced Nursing Practice: What Are Key Considerations for Implementation and Optimized Use?

ObjectiveTo discuss the opportunities and challenges of implementing patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) within advanced practice nursing services in cancer care. MethodsThis discussion paper has been informed by an environmental scan of evidence from systematic reviews and primary studies evaluating the use and implementation of PROMs and PREMs. Literature from the contexts of cancer and chronic disease, including nursing and multidisciplinary supportive care literature, has been included. ResultsAdvanced practice nurses are well-positioned to evaluate and respond to PROMs and PREMs data; several studies have highlighted improved patient outcomes concerning quality of life, symptom distress, and functional status within nurse-led services. Nevertheless, the implementation of PROMs and PREMs in cancer care and nurse-led services is variable. Previous studies have highlighted implementation challenges, which can hinder comparability and generalizability of PROMs and PREMs instruments. Advanced practice nurses should consider these challenges, including ways to use standardized PROM instruments. Electronic PROMs, while efficient, may exclude individuals at risk of inequity. Complex, lengthy, and frequent administration of PROMs may also overburden people living with or after cancer, with people affected by cancer expressing preference for flexible use in some studies. Therefore, the involvement of people affected by cancer in planning for PROMs/PREMs implementation may overcome this challenge. Finally, organizational considerations in implementation should address financial investments, including initial costs for technology and training and consideration of the operationalization of PROMs within existing infrastructure for the seamless utilization of PROMs data. ConclusionDespite the potential of advanced practice nursing services to enhance patient-reported outcomes and experiences, variability in the implementation of PROMs and PREMs poses challenges. Use of validated measures, electronic or paper-based instruments, and the preferences of people affected by cancer for the use of PROMs and PREMs must be carefully considered in consultation with end users for successful implementation. Implications for PracticeIn planning for the implementation of PROMs and PREMs within nurse-led services, implementation risks may be mitigated through establishing clear guidelines for their use, investment in the development of the required infrastructure, user education, and rigorous implementation processes, including patient involvement in PROMs/PREMs selection.

5G and edge: A reinforcement learning approach for Virtual Network Embedding with cost optimization and improved acceptance rate

5G technologies are fueling a revolution across numerous industries, including manufacturing, healthcare, and entertainment, by enabling the development and deployment of novel applications at the network’s edge. To meet the demanding service level agreements of these industries, a dynamic and adaptable infrastructure strategy that combines cloud and edge computing models is needed. This hybrid approach offers the benefits of both centralized cloud processing and decentralized edge computing, optimized for responsiveness and efficiency. A key element for success is an orchestration mechanism that dynamically allocates resources to ensure the infrastructure can adapt to fluctuating demands in real time, optimizing resource utilization and meeting SLA requirements. Among these mechanisms, virtual network embedding (VNE) and dynamic resource management (DRM) have emerged as tools for defining where and how edge technology should be used. However, current VNE approaches struggle to adapt to real-time fluctuations in demand across geographically distributed edge resources. This work introduces a novel resource allocation algorithm, the VNE-CRS, which uses an Artificial Intelligence technique called Reinforcement Learning to orchestrate resources across multiple domains. This approach benefits from the strength of Reinforcement Learning: its ability to consider the entire problem from beginning to end while incorporating various aspects of 5G Quality of Service Indicators for optimal decision-making. Experiments were conducted to evaluate the performance of VNE-CRS against state-of-the-art algorithms for multi-domain edge environments. Results have shown that employing Reinforcement Learning techniques for VNE resource allocation yields performance gains of 12.32 percentage points in comparison with the GRC algorithm and 28.80 percentage points in comparison with the base edge environment, presenting an acceptability rate closer to the Public Cloud environment with all benefits of edge environment. In conclusion, VNE-CRS offers an efficient solution for resource allocation in 5G environments, achieving superior performance and transforming the VNE architecture into a comprehensive orchestration system that optimizes infrastructure utilization for strategic long-term benefits.