Scalable Services Research Articles

A stepped wedge randomised controlled trial assessing the efficacy and patient acceptability of virtual clinical pharmacy in rural and remote Australian hospitals.

Despite medication being the most common healthcare intervention and medication-related incidents being common in hospitals, many rural and remote hospitals in Australia lack onsite pharmacy services due to resource constraints. A Virtual Clinical Pharmacy Service (VCPS) staffed by two senior, rural generalist hospital pharmacists assigned to four hospitals each was implemented in rural and remote facilities to determine whether the VCPS increased adherence to National Safety and Quality Health Service Standards (NSQHS). A stepped-wedge randomised controlled trial was employed to sequentially implement a telehealth pharmacy service at one-month intervals in eight hospitals. The primary outcomes were patient-level medication reconciliation completion rates on admission and discharge. Secondary measures evaluated compliance with other NSQHS standards (including Best Possible Medication History, Medication Reconciliation and venous thromboembolism risk assessment), patient outcomes (including representation within 48h, readmission within 28 days and length of stay), and detection of potential medication-related harms (including pharmacist identified medication related problems, reported medication errors and falls). Patients were invited to complete a patient-reported experience questionnaire. Data were collected from electronic medical records and analysed using mixed logistic regression models to estimate the effectiveness of the VCPS. Antimicrobial usage, falls, and medication errors were analysed at the facility level, while other data were analysed at the patient level. Compared to control (n = 535), patients in the intervention period (n = 527) were more likely to have an admission medication reconciliation completed (Odds Ratio (OR) 11.16, 95% confidence interval (CI) 5.59-22.30, p < 0.001) in models adjusted for the study period. A similar improvement was observed for discharge medication reconciliation completion (OR 4.07, CI 2.38-6.95, p < 0.001), whereas a 33-fold improvement was seen in Best Possible Medication History completion (OR 33.27, CI 17.53-63.14, p < 0.001). The VCPS documented 879 medication related problems, with 61% of patients having at least one medication-related problem documented by a pharmacist. There was no change in length of stay, falls, readmission rates or reported medication error rates; however, the study was not powered to detect these changes. Patient feedback was positive and comparable to in-person care, with 95% (179/189) reporting their overall experience as 'good' or 'very good.' No unintended harms were reported. The VCPS improved compliance with national standards for medication safety, had high patient acceptability and resulted in the detection of clinically relevant medication-related issues in rural and remote settings. The applicability of virtual pharmacy should be explored in further rural and remote locations in addition to other settings such as metropolitan locations with no onsite clinical pharmacists. GWHREC 2019/ETH13355. ANZCTR registration number ACTRN12619001757101. Registered on 11/12/2019. Published trial protocol: A stepped wedge trial of efficacy and scalability of a virtual clinical pharmacy service (VCPS) in rural and remote NSW health facilities.

Integrating FinOps in healthcare for optimized financial efficiency and enhanced care

The integration of Financial Operations (FinOps) in healthcare is increasingly recognized as a critical strategy for optimizing financial efficiency and enhancing patient care. This paper explores the principles and practices of FinOps, emphasizing its role in improving cost management, fostering financial transparency, and ensuring better resource allocation in healthcare organizations. By addressing traditional inefficiencies in healthcare financial management, FinOps promotes collaboration between finance and operations, enabling real-time financial decision-making that aligns with both operational goals and patient outcomes. Additionally, the paper outlines the challenges healthcare organizations face in managing rising costs and complex billing systems, and provides practical strategies for the successful implementation of FinOps, including the use of advanced technologies such as cloud-based platforms and data analytics tools. Ultimately, the adoption of FinOps not only enhances short-term financial efficiency but also contributes to the long-term sustainability and scalability of healthcare services.

GenesisRM: A state-driven approach to resource management for distributed JVM web applications

Reducing resource waste while maintaining end-to-end latency service-level objective (SLO) by simultaneously managing CPU bandwidth, memory allocation, and pod number of web applications running on Java virtual machine (JVM) is challenging. The challenges stem from the complexity of the multi-type resource allocation optimization problem, the high sensitivity of JVM performance to resource scaling actions, and the lack of low-level resource scaling mechanisms. We present GenesisRM, a resource management framework with a novel state-driven architecture. Specifically, we design a state control model for JVM web applications that encompasses seven pod states. This model serves as an abstraction layer, decoupling the centralized resource management system into a global state manager and distributed pod managers. The state manager controls the state transitions of the pods based on the overall workload, while the pod managers dynamically allocate resources for each pod according to the state and local workload.Then, we propose a multi-frequency control model with two predictive state controllers and a reactive state controller to manage the state of pods based on the state control model. In addition, GenesisRM brings new mechanisms to scale JVM pods’ low-level resources without damaging their performance. We evaluate our work using a real-world JVM web application benchmark in three different scale server clusters of Pengcheng Laboratory Developer Cloud, and the 21-day experimental results show that GenesisRM saves 31.70% CPU and 17.60% memory compared to the best-performing state-of-the-art solutions while guaranteeing the SLO imposed on end-to-end latency.

Co-producing and co-assessing a new service solution for enhancing health and social care integration: a participatory research protocol

BackgroundThis paper describes a study protocol for co-producing and co-assessing a new sustainable and scalable service solution that enhances health and social integration by involving providers and volunteers delivering services for elderly people in the province of Cremona (Italy), where the elderly population will reach 27% in 2023.MethodsThis upcoming study involves mixed-method participatory research and is structured in three study phases and related objectives. First, it will co-produce a new, accessible and sustainable service solution using an iterative design and management method, Plan-Do-Check-Act by involving professionals and volunteers of a heterogeneous group of health, social and third sector organizations located in the city of Cremona (Italy). Second, the study protocol will co-assess the outcomes of the new service solution using a mixed-method approach for measuring the outcomes on: professionals and volunteers (micro level) and their health, social and third sector organizations (meso level). Third, this study will co-investigate the scalability of the new solution promoting health and social integration in other similar urban areas of the Province of Cremona via the Intervention Scalability Assessment Tool (macro level). The data will be collected through the analysis of official documents, websites, policies and participatory workshops.DiscussionThis protocol proposes an innovative intervention, a novel participatory approach, and an unexplored scalability assessment tool in the context of health and social care integration. This study aims to support professionals from health and social care service providers and volunteers from third-sector organizations to collaborate and integrate each other’s resources. In doing so, the participatory approach will facilitate the co-creation of an effective response to the need of health and social integration, and the development of trustful relationships between health and social care service providers. Moreover, the adoption of Plan-Do-Check-Act and Intervention Scalability Assessment Tool will ensure the quality, scalability and sustainability of the new service solution in other settings.

Cloud Network Function Virtualization (NFV)

Cloud Network Function Virtualization (NFV) is a groundbreaking technology that revolutionizes the way network services are delivered. By virtualizing network functions, NFV enables faster deployment, scalability, and management of network services. This article explores the benefits and use cases of NFV, including virtualized load balancers, firewalls, and intrusion detection devices. We also discuss the evolution of NFV to Cloud-Native Network Functions (CNF) and the challenges and solutions associated with NFV adoption. Discover how Cloud NFV is transforming network architecture and enabling organizations to achieve greater agility, flexibility, and cost savings.

해양 모빌리티 서비스 다양화를 위한 상업용 스마트 잠수정 디자인 연구

Coastal areas worldwide, including South Korea, are actively promoting the marine leisure industry as an economic growth driver. With the emergence of experiential consumption trends today, there is a growing demand for underwater leisure activities, highlighting the need for various underwater transportation devices to meet market demands. Based on the government's policy to develop the marine mobility industry, this study aims to conduct a case analysis of tourist and commercial submersibles and propose new designs accordingly. The proposed design is an 11-seater tourist submersible equipped with high-efficiency electric batteries and AI-based autonomous systems to enhance efficiency and safety, thereby maximizing the scalability of marine mobility services while protecting sustainable tourism and economic productivity. The next-gen submersible opens up new dimensions for underwater tourism, providing immersive experiences to travelers. Furthermore, it presents environmental management, such as ecotourism in coastal areas and marine protected areas, fostering future possibilities for expanding the marine leisure industry, promoting environmental conservation, and stimulating economic growth.

Optimizing The Use Of Cloud Technology In Public Sector Management Control (Case Study Of E-Government In Bandung City)

This research aims to explore and analyze the optimization of cloud technology usage in public sector management control, focusing on the implementation of e-government in Bandung City as a case study. The research method used is a qualitative case study, collecting data through in-depth interviews, observations, and related document analysis. The results indicate that the application of cloud technology has brought significant benefits, including increased efficiency, accessibility, and scalability of e-government services. However, challenges related to data security and technological infrastructure limitations were also identified. Based on these findings, recommendations are provided to strengthen infrastructure, train employees, establish partnerships with the private sector, and conduct regular evaluations to enhance the application of cloud technology in e-government management in Bandung City and the public sector in general. This research is expected to provide valuable insights for the development and optimization of e-government services in various governmental contexts.

Data-Driven Geofencing Design for Point-of-Interest Notifiers Utilizing Genetic Algorithm

This study proposes a method for generating geofences driven by GPS trajectory data to realize scalable point-of-interest (POI) notifiers, encouraging walking tourists to discover new local spots. The case study revealed that manual geofence settings degrade the location relevance and user coverage—key objectives of POI notifiers—and hinder the scalability and reliability of services. The formalization presented computationally equips geofence designers with practical solutions through two implementations based on prior GPS trajectory logs: (1) a multiobjective genetic algorithm that suggests cost-effective geofences by providing trade-off visualizations and (2) a user coverage-penalized genetic algorithm that determines an optimal geofence based on the designers’ expectations. The feasibility and stability of the proposed implementations were tested in areas with varying tourist flow patterns. A comparative survey among manual settings, settings incorporating a reliability simulation, and data-driven settings demonstrates significant performance improvements for geofence services.

Dynamic edge clustering and task scheduling for edge assisted metaverse system in the field of remote work and collaboration

SummaryThe metaverse, a future digital world for living, working, learning, and interacting, is rapidly gaining significance, particularly in the domain of remote work and collaboration. This emerging digital landscape demands high‐performance, low‐latency, and scalable services to provide an immersive user experience. The current technology used in metaverse systems has some limitations, which emphasize the importance of adopting emerging technologies like Edge Computing (EC). However, as the number of users and data volume increases, it can impact both system performance and scalability of the edge‐assisted metaverse system. Additionally, the uneven distribution of edge servers can cause inconsistencies and result in high latency. To overcome these challenges, this paper proposes a dynamic edge clustering and task scheduling approach for edge‐assisted metaverse systems (DTAM) in the field of remote work and collaboration. The proposed approach addresses the challenges of high user volume and uneven resource distribution by incorporating dynamic clustering and edge server assistance to improve clustering performance. Furthermore, a Prioritized Experience Replay‐based Deep Q‐learning algorithm with state augmentation (PERDQSA) for task scheduling is introduced to improve sample efficiency and performance. The performance of the proposed DTAM is evaluated against existing techniques, and experimental results demonstrate its significant superiority in terms of specific metrics such as bandwidth, task response time, energy efficiency, and latency. The experiments demonstrated that DTAM outperforms Transformation‐based Edge Computing Deep Q‐Learning (TransEC‐DQL) in several key metrics. Specifically, DTAM achieves 28.5% reduction in latency, 13.7% reduction in response time, and 6.4% improvement in bandwidth compared to TransEC‐DQL. These results signify that DTAM can deliver a significantly enhanced user experience in the metaverse, particularly in the context of remote work and collaboration.

An enhanced bio‐inspired energy‐efficient localization routing for mobile wireless sensor network

SummaryEnvironmental disasters such as earthquakes, tsunamis, floods, and landslides disrupt natural resources, human casualties, and so on. Early warning and alert of such events will help for preparedness to avoid human and economic causalities. Managing a disaster or emergency scenario is a difficult endeavor. These episodes of mass devastation, whether caused by natural disasters or man‐made disasters, result in massive losses of money, property, and lives as a result of inadequate response by the government and management authorities. As a result, actions must be taken to avoid these circumstances from occurring by discovering the causes of these catastrophes in advance and providing swift rescue procedures once the crisis happens. In a disaster situation, communication plays a vital role, and a lot of people turn towards it as a source of information conveyance/delivery for controlling the situation. Even these communication networks are damaged and incur tremendous losses during disasters. All the communication networks, including mobile wireless sensor networks (WSNs) deployed for communication, also suffer huge damage and face many challenges during disasters. Nature appears to deal with these issues well. WSN is inspired by several biological systems and processes that have intrinsically attractive properties such as adaptation, self‐organization, and self‐healing. In this paper, we proposed an enhanced bio‐inspired energy‐efficient localization (EBEEL) routing for Mobile WSN. The main focus of the proposed EBEEL algorithm is to overcome the challenges faced by peer‐existing routing protocols such as data aggregation, limited bandwidth, scalability, data redundancy, energy consumption, packet drops, load balancing, and Quality of Service (QoS).

A hybrid prognostic & health management framework across multi-level engineering systems with scalable convolution neural networks and adjustable functional regression models

With the rapid development of sensor technology and mechatronics integration, there are emerging demands for achieving prognostic and health management (PHM) within a general framework across multi-level engineering systems. However, current PHM frameworks are mainly supported either by field data or degradation models, which are always limited in inadequate robustness to systematically increase operational availability and reduce maintenance costs. For extending the scalability and flexibility of PHM services, this paper proposes a novel hybrid framework that integrates the scalable convolution neural network (SCNN) with the adjustable functional regression model (AFRM) through a modified linear filter. This proposed framework first establishes a data-driven SCNN with scalable time sequences and kernel sizes to enhance the remaining useful life (RUL) generalization. Then, enabled by an adjustable semi-parametric degradation process model, AFRM with flexible lifetime distributions is designed to evaluate time-to-failure (TTF) in real time. Finally, a modified Kalman filter has been explored to coordinate data-driven RUL results with model-based TTF values to schedule predictive maintenance (PdM). High-accuracy prediction, multi-level systems, high-flexibility evaluation are thus encompassed within the hybrid progressive modification. The proposed framework has been practically validated through multi-level studies including the component-level bearings, subsystem-level engines, and system-level machines. Results demonstrate that our hybrid PHM framework achieves significant improvements in prediction accuracy, evaluation adaptability, and scheduling effectiveness.

Clinical Service Incorporating Mobile Technology on Weight Loss in Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease: A Translation From Research Trial.

The prevalence and healthcare cost of metabolic dysfunction-associated steatotic liver disease (MASLD) has increased alongside the epidemic surge in obesity and Type 2 diabetes. Weight loss through lifestyle modification remains the primary effective therapy for MASLD. Incorporation of mobile technology in lifestyle interventions has been previously found to be efficacious and cost-effective in facilitating weight loss. However, there is a paucity of studies that have successfully translated lifestyle research into clinical service for weight loss to alleviate disease burden. Our study aimed to describe the process of translating a mobile technology-enabled trial into a tertiary hospital outpatient dietetics service for patients with MASLD. The Iowa Model of Evidence-Based Practice to Improve Quality Care was used as a framework for this paper to guide implementation at the organizational level. Regular engagement of key operational staff and the hospital management team facilitated open discussions of the challenges faced and enabled rapid implementation of strategies that contributed to the smooth piloting of the service. A service adoption rate of 81% was achieved. Preliminary outcome evaluation found that the percentage of patients achieving ≥ 5% weight loss from baseline at 6 months was comparable at 54% and 52% for the service and trial groups, respectively. Evaluation of the implementation process found that a hybrid model of care (in-person consultation supplemented with app coaching) preserved interpersonal connections while maximizing the convenience and scalability of mobile app-enabled service. Although high digital acceptance and adoption rates propelled by COVID-19-supported telehealth, it is prudent to assess patient's access to technology and digital literacy and offer resources to help them benefit from telehealth services.

ENHANCING GLOBAL ECONOMIC INCLUSION WITH FINTECH INNOVATIONS AND ACCESSIBILITY

The objective of this paper is to explore the transformative role of financial technology (fintech) innovations in enhancing global economic inclusion. Recognizing the persistent barriers that impede access to financial services for marginalized communities worldwide, this study employs qualitative case studies across several economies. The research methodology includes the examination of mobile payment systems, blockchain-based solutions, and digital banking platforms to assess their impact on financial inclusivity. Key findings indicate that fintech innovations significantly lower the cost of delivering financial services and expand their accessibility, especially in remote areas lacking traditional banking infrastructure. Notably, mobile payment systems have empowered individuals by facilitating peer-to-peer transactions and enabling micro-lending practices. Blockchain technology has further enhanced transparency and security, encouraging higher usage rates among underserved populations. The paper concludes that while fintech innovations hold substantial potential to drive economic inclusion, challenges such as regulatory frameworks, digital literacy, and infrastructure still require comprehensive strategies to maximize their impact. It suggests that ongoing collaboration between governments, technology providers, and financial institutions is crucial to developing inclusive fintech ecosystems that not only promote access but also ensure the sustainability and scalability of financial services globally. Keywords: Fintech, Financial Inclusion, Emerging Markets, Accessibility Barriers, Mobile Payments, Digital Banking, Blockchain, Digital Divide

An advancing method for web service reliability and scalability using ResNet convolution neural network optimized with Zebra Optimization Algorithm

AbstractWeb service reliability and scalability is an important mission that keeps web services running normally. Within web service, the web services invoked by users not only depend on the service itself, but also on web load condition. Due to the features of web dynamics, traditional reliability and scalability methods have become inappropriate; at the same time, the web condition parameter sparsity problem will cause inaccurate reliability prediction. To address these challenges, Web Service Reliability and Scalability Determination Using ResNet Convolutional Neural Network optimized with Zero Optimization Algorithm (WRS‐ResNetCNN‐ZOA) is proposed in this manuscript. Initially, the input data is collected from WSRec dataset. The ResNet convolutional neural network (ResNetCNN) with Business Process Execution Language (BPEL) specification is introduced to forecast the reliability and scalability of web service. The results are categorized as right and wrong based on ResNetCNN. The weight parameters of the ResNetCNN is optimized by Zebra Optimization Algorithm to improve accuracy of the prediction. The performance of the proposed method is examined under some performance metrics, like F‐measure, reliability, scalability, accuracy, sensitivity, specificity, and precision. The proposed technique attains 15.36%, 35.39%, 23.87%, 20.67% better reliability, 42.39%, 11.39%, 34.16%, 25.78% better accuracy when analyzed to the existing methods, like Web Reliability based on K‐clustering, (WRS‐KClustering), Web Reliability prediction based on AdaBoostM1 and J48 (WRS‐AdaM1‐J48), Web Reliability prediction based on Online service Reliability (WRS‐OPUN), and Web Reliability prediction based on Dynamic Bayesian Network (WRS‐DBNS), respectively.

Redefining governance: a critical analysis of sustainability transformation in e-governance.

With the rapid growth of information and communication technologies, governments worldwide are embracing digital transformation to enhance service delivery and governance practices. In the rapidly evolving landscape of information technology (IT), secure data management stands as a cornerstone for organizations aiming to safeguard sensitive information. Robust data modeling techniques are pivotal in structuring and organizing data, ensuring its integrity, and facilitating efficient retrieval and analysis. As the world increasingly emphasizes sustainability, integrating eco-friendly practices into data management processes becomes imperative. This study focuses on the specific context of Pakistan and investigates the potential of cloud computing in advancing e-governance capabilities. Cloud computing offers scalability, cost efficiency, and enhanced data security, making it an ideal technology for digital transformation. Through an extensive literature review, analysis of case studies, and interviews with stakeholders, this research explores the current state of e-governance in Pakistan, identifies the challenges faced, and proposes a framework for leveraging cloud computing to overcome these challenges. The findings reveal that cloud computing can significantly enhance the accessibility, scalability, and cost-effectiveness of e-governance services, thereby improving citizen engagement and satisfaction. This study provides valuable insights for policymakers, government agencies, and researchers interested in the digital transformation of e-governance in Pakistan and offers a roadmap for leveraging cloud computing technologies in similar contexts. The findings contribute to the growing body of knowledge on e-governance and cloud computing, supporting the advancement of digital governance practices globally. This research identifies monitoring parameters necessary to establish a sustainable e-governance system incorporating big data and cloud computing. The proposed framework, Monitoring and Assessment System using Cloud (MASC), is validated through secondary data analysis and successfully fulfills the research objectives. By leveraging big data and cloud computing, governments can revolutionize their digital governance practices, driving transformative changes and enhancing efficiency and effectiveness in public administration.

Designing a Secure and Scalable Service Agent for IoT Transmission through Blockchain and MQTT Fusion

With the rapid development of the Internet of Things (IoT) in recent years, many IoT devices use communication systems to transmit data. Data packets are inevitably at risk of tampering during data transmission, which can lead to information errors and damage during communication. Since IoT terminals are often operated under human supervision, it is essential to improve and ensure the transmission of information, avoid data tampering, and ensure the accuracy of packet transmission. This research successfully improves the message transmission method of IoT communication and the communication model by combining it with blockchain architecture. Combining communication protocols with blockchain serial connections eliminates the need to control operating and managing processes. By replacing message transmission with coverage, the reliability of data transmission in the IoT communication system is improved, and the flexibility of the data transmission process is enhanced. Through practical verification through experimentation, the study successfully improved the incapability of effectively constructing a cross-coded message transmission mode in the Message Queuing Telemetry Transport (MQTT) communication protocol, removed a single layer of encryption rule, and created an encryption mode capable of providing complex arrangement organization.

Building a digital tool to support focused acceptance and commitment therapy practitioners in New Zealand primary care: A qualitative exploration of user needs to guide software feature development

Mental health service scalability needs to be improved to meet the growing global demand. The scalability of Focused Acceptance and Commitment Therapy (fACT), a popular evidence-based brief behavioural intervention used in primary care contexts, could be improved through the development and implementation of a digital tool that supports practitioners to overcome service-delivery problems within their practice context.Through semi-structured in-depth interviews with 12 fACT practitioners, we examined the service-delivery problems that they face within the New Zealand primary care context and identified organisational factors which may be contributing to these problems. From these interviews, six key themes emerged: (1) The brief model works for most clients but is not suitable for every client, (2) practitioners often struggle to access culturally appropriate fACT congruent exercises and psychoeducation material, (3) practitioners feel that they need additional training to maintain good model fidelity, (4) short session times can negatively impact model delivery, (5) public health employed practitioners have high workloads and are often unable to refer clients to secondary or crisis services, and (6) fACT practitioners are unable to effectively follow up with their clients post-session.This study is part of a broader project aimed at developing an adjunctive digital tool to support the delivery of fACT in New Zealand primary care. These interviews will help us to understand the problems practitioners face in this service-delivery context, identify context-specific factors that may be causing these problems, and offer insights into the necessary features of an engaging digital tool designed to improve model scalability.

Empirical analysis of factors affecting industrial maintenance services selection

The process of selecting industrial maintenance services needs careful consideration of various factors to ensure that the chosen provider can effectively meet the specific needs of the organization/facility. This research paper examines the key factors that play a significant role in the decision-making process for selecting industrial maintenance services. These factors encompass the nature of services required, the expertise and experience of the service provider, cost and budget constraints, availability and responsiveness, establishment of service level agreements, evaluation of reputation and references, compliance with safety regulations, assessment of technological capabilities, flexibility and scalability of services, and sustainability practices. Through a thorough examination of these factors, industrial facilities can make well-informed decisions that align with their unique requirements and priorities.

The role of spiritual support in palliative care-A case illustration.

Key points Spiritual care is an important component of palliative care. Timely and comprehensive spiritual care can lead to improved patient care outcomes including pain relief and reduced opioid use in palliative care patients. Local religious and cultural factors need to be considered when providing spiritual care and developing services. The introduction and integration of current spiritual care education and quality frameworks into current curricula and practice is essential for the development of the field. A generalist‐specialist approach to spiritual care may improve both efficacy and scalability of services.

An improved resource allocation method for mapping service function chains based on A3C

AbstractNetwork function virtualization (NFV) technology deploys network functions as software functions on a generalised hardware platform and provides customised network services in the form of service function chain (SFC), which improves the flexibility and scalability of network services and reduces network service costs. However, irrational resource allocation during service function chain mapping will cause problems such as low resource utilisation, long service request processing time and low mapping rate. To address the unreasonable problem of service mapping resource allocation, an improved service function chain mapping resource allocation method (SA3C) based on the Asynchronous advantageous action evaluation algorithm (A3C) is proposed. This study proposes an SFC mapping model and a mathematical model for joint allocation, which modeled the minimization of processing time as a Markov process. The main network was trained and multiple sub‐networks were generated in parallel using the ternary and deep reinforcement learning algorithm A3C, with the goal of identifying the optimal resource allocation strategy. The experimental simulation results show that compared with the Actor‐Critic (AC) and Policy Gradient (PG) methods, SA3C algorithm can improve the resource utilisation by 9.85%, reduce the total processing time by 10.72%, and improve the mapping rate by 6.72%, by reasonably allocating node computational resources and link bandwidth communication resources.