Service Placement Research Articles

A Qualitative Study of Implementation Outcomes of a Group-Based Intervention for Immigrant Parents

Abstract Background Inequities in service utilization between immigrant families and native populations have emerged within the Nordic welfare countries, highlighting the need for culturally sensitive parenting support. Despite the proven efficacy of parenting interventions, their implementation phases have not been adequately addressed in extant implementation research, particularly regarding structural racism and inequities. This study aims to contribute to the literature from an implementation perspective by describing a pilot of ‘Being a Parent in Finland,’ a culturally sensitive group-based intervention for migrant parents. Its specific focus is on investigating barriers to and facilitators of implementation outcomes from the perspectives of service providers. Methods The qualitative data consisted of thematic interviews (N = 6) with service providers (N = 9) compiled at distinct phases of the pilot from 2021 to 2023. In addition, data triangulation was utilized through document analysis of 125 pages of various documents regarding the pilot as a supplementary data collection method. Content analysis was conducted on the data using Atlas.ti 23. Results The findings stress the alignment of the target group, service placement, and preventive measures for enhancing parenting intervention’s acceptability, appropriateness, and feasibility. Implicit objectives, such as perceiving the target group as ‘at-risk,’ can impede intervention success and exacerbate stigmatization of the target group. Culture- and context-specific adaptations, and community engagement foster parental trust and intervention accessibility. Conclusions The findings underscore the significance of congruent objectives, community engagement, and adaptive iterations in preventive parenting interventions. Advocating for these elements in implementation strategies is crucial for reducing stigmatization and enhancing equity and efficacy in services tailored to minority parents. Key messages • Acknowledging equity in both implementation research and strategies is paramount. • Implementing trust-building is crucial for culturally sensitive parenting interventions.

Efficient Schemes for Optimizing Load Balancing and Communication Cost in Edge Computing Networks

Edge computing architectures promise increased quality of service with low communication delays by bringing cloud services closer to the end-users, at the distributed edge servers of the network edge. Hosting server capabilities at access nodes, thereby yielding edge service nodes, offers service proximity to users and provides QoS guarantees. However, the placement of edge servers should match the level of demand for computing resources and the location of user load. Thus, it is necessary to devise schemes that select the most appropriate access nodes to host computing services and associate every remaining access node with the most proper service node to ensure optimal service delivery. In this paper, we formulate this problem as an optimization problem with a bi-objective function that aims at both communication cost minimization and load balance optimization. We propose schemes that tackle this problem and compare their performance against previously proposed heuristics that have been also adapted to target both optimization goals. We study how these algorithms behave in lattice and random grid network topologies with uniform and non-uniform workloads. The results validate the efficiency of our proposed schemes in addition to the significantly lower execution times compared to the other heuristics.

GP4ESP: a hybrid genetic algorithm and particle swarm optimization algorithm for edge server placement

Edge computing has attracted wide attention due to its ultra-low latency services, as well as the prevalence of smart devices and intelligent applications. Edge server placement (ESP) is one of the key issues needed to be addressed for effective and efficient request processing, by deciding which edge stations to equip with limited edge resources. Due to NP-hardness of ESP, some works have designed meta-heuristic algorithms for solving it. While these algorithms either exploited only one kind of meta-heuristic search strategies or separately perform two different meta-heuristic algorithms. This can result in limit performance of ESP solutions due to the “No Free Lunch” theorem. In addition, existing algorithms ignored the computing delay of edge servers (ESs) on request process, resulting in overestimation of the service quality. To address these issues, in this article, we first formulate ESP problem with the objective of minimizing the overall response time, considering heterogeneous edge servers with various service capacity. Then, to search effective or even the best ESP solutions, we propose a hybrid meta-heuristic algorithm (named GP4ESP) by taking advantage of both the powerful global search ability of genetic algorithm (GA) and the fast convergence of particle swarm optimization (PSO). GP4ESP effectively fuses the merits of GA and PS by integrating the swarm cognition of PSO into the evolutionary strategy of GA. At last, we conducted extensive simulation experiments to evaluate the performance of GP4ESP, and results show that GP4ESP achieves 18.2%–20.7% shorter overall response time, compared with eleven up-to-date ESP solving algorithms, and the performance improvement is stable as the scale of ESP is varied.

A novel niching genetic algorithm with heterosis for edge server placement

A novel niching genetic algorithm with heterosis for edge server placement

Optimizing edge server placement and allocation for enhanced energy efficiency: a multi-objective approach based on decision space and elitism

Optimizing edge server placement and allocation for enhanced energy efficiency: a multi-objective approach based on decision space and elitism

Service placement strategies in mobile edge computing based on an improved genetic algorithm

In mobile edge computing (MEC), quality of service (QoS) is closely related to optimizing service placement strategies, which is crucial to providing efficient services that meet user needs. However, due to the mobility of users and the energy consumption limit of edge servers, the existing policies make it difficult to ensure the QoS level of users. In this paper, a novel genetic algorithm based on a simulated annealing algorithm is proposed to balance the QoS of users and the energy consumption of edge servers. Finally, the effectiveness of the algorithm is verified by experiments. The results show that the QoS value obtained by the proposed algorithm is closer to the maximum value, which has significant advantages in improving QoS value and resource utilization. In addition, in software development related to mobile edge computing, our algorithm helps improve the program’s running speed.

Optimized Dynamic Service Placement for Enhanced Scheduling in Fog-Edge Computing Environments

The traditional cloud computing model struggles to efficiently handle the vast number of Internet of Things (IoT) services due to its centralized nature and physical distance from end-users. In contrast, edge and fog computing have emerged as promising solutions for supporting latency-sensitive IoT applications by distributing computational resources closer to the data source. However, these technologies are limited by their size and computational capacities, making optimal service placement a critical challenge. This paper addresses this challenge by introducing a dynamic and distributed service placement policy tailored for edge and fog environments. By leveraging the inherent advantages of proximity in fog and edge nodes, the proposed policy seeks to enhance service delivery efficiency, reduce latency, and improve resource utilization. The proposed method focuses on optimizing the placement of high-demand services closer to the data generation sources to enhance scheduling efficiency in fog computing environments. Our method is divided into three interconnected modules. The first module is the service type estimator, which is responsible for distributing services to appropriate nodes. Here, we use the Political Optimizer (PO) as a new metaheuristic algorithm for deploying IoT services. The second module is service dependency estimator, which manages service dependencies. Here, we load dependent services near the data using a path matrix based on the Warshall algorithm. Finally, the third module is resource demand scheduling, which estimates resource demand to facilitate optimal scheduling. Here, we use a popularity-based policy to manage resource demand and service execution scheduling. Implementation results demonstrate significant improvements over existing state-of-the-art policies, highlighting the efficacy of the proposed policy in enhancing service delivery within fog-edge computing frameworks.

Framework for content server placement using integrated learning in content delivery network

<p>Content placement is a significant concern in content delivery networks (CDN), irrespective of various evolving studies. Existing methodologies showcase various significant unaddressed issues concerning content placement approaches' complexities. Therefore, the proposed study presents a novel computational framework towards dynamic content placement strategy using a novel integrated machine learning approach. Simplified mathematical modelling is used to formulate and solve the content placement problem. At the same time, reinforcement learning and the sequential attentional neural network have been utilized to optimize the decision-making towards placement of content servers. Designed and assessed over a Python environment, the proposed scheme is witnessed to exhibit 35% reduced bandwidth utilization, 20% reduced delay, 23% reduced computational resource utilization, and 28% reduced algorithm processing time in contrast to existing predictive content placement schemes.</p>

Secure Edge Server Placement With Non-Cooperative Game for Internet of Vehicles in Web 3.0

Secure Edge Server Placement With Non-Cooperative Game for Internet of Vehicles in Web 3.0

MOHHO: multi-objective Harris hawks optimization algorithm for service placement in fog computing

MOHHO: multi-objective Harris hawks optimization algorithm for service placement in fog computing

Rethinking the mobile edge for vehicular services

The growing connected car market requires mobile network operators (MNOs) to rethink their network architecture to deliver ultra-reliable low-latency communications. In response, Multi-Access Edge Computing (MEC) has emerged as a solution, enabling the deployment of computing resources at the network edge. For MNOs to tap into the potential benefits of MEC, they need to transform their networks accordingly. Consequently, the primary objective of this study is to design a realistic MEC architecture and corresponding optimal deployment strategy – deciding on the placement and configuration of computing resources – as opposed to prior studies focusing on MEC run-time management and orchestration (e.g., service placement, computation offloading, and user allocation). To cater to the heterogeneous demands of vehicular services, we propose a multi-tier MEC architecture aligned with 5G and Beyond-5G radio access network deployments. Therefore, we frame MEC deployment as an optimization problem within this architecture, assuming 3 MEC tiers. Our data-driven evaluation, grounded in realistic assumptions about network architecture, usage, latency, and cost models, relies on datasets from a major MNO in the UK. We show the benefits of adopting a 3-tier MEC architecture over single-tier (centralized or distributed) architectures for heterogeneous vehicular services, in terms of deployment cost, energy consumption, and robustness.

Latency-Aware and Proactive Service Placement for Edge Computing

Latency-Aware and Proactive Service Placement for Edge Computing

The optimization of harm reduction services in Massachusetts through the use of GIS: Location-allocation analyses, 2019–2021

BackgroundFatal opioid-related overdoses (OOD) continue to be a leading cause of preventable death across the US. Opioid Overdose Education and Naloxone Distribution programs (OENDs) play a vital role in addressing morbidity and mortality associated with opioid use, but access to such services is often inequitable. We utilized a geographic information system (GIS) and spatial analytical methods to inform prioritized placement of OEND services in Massachusetts. MethodsWe obtained addresses for OEND sites from the Massachusetts Department of Public Health and address-level fatal OOD data for January 2019 to December 2021 from the Massachusetts Registry of Vital Records and Statistics. Using location-allocation approaches in ArcGIS Pro, we created p-median models using locations of existing OEND sites and fatal OOD counts to identify areas that should be prioritized for future OEND placement. Variables included in our analysis were transportation mode, distance from public schools, race and ethnicity, and location feasibility. ResultsThree Massachusetts communities – Athol, Dorchester, and Fitchburg – were identified as priority sites for new OEND locations using location-allocation models based on capacity to maximize OOD prevention. Communities identified by the models for OEND placement had similar demographics and overdose rates (42.8 per 100,000 vs 40.1 per 100,000 population) to communities with existing OEND programs but lower naloxone kit distribution rates (2589 doses per 100,000 vs 3704 doses per 100,000). Further models demonstrated differential access based on location and transportation. ConclusionOur analyses identified key areas of Massachusetts with greatest need for OEND services. Further, these results demonstrate the utility of using spatial epidemiological methods to inform public health recommendations.

Multi-Agent Dynamic Fog Service Placement Approach

Fog computing as a paradigm was offered more than a decade ago to solve Cloud Computing issues. Long transmission distances, higher data flow, data loss, latency, and energy consumption lead to providing services at the edge of the network. But, fog devices are known for being mobile and heterogenous. Their resources can be limited, and their availability can be constantly changing. A service placement optimization is needed to meet the QoS requirements. We propose a service placement orchestration, which functions as a multi-agent system. Fog computing services are represented by agents that can both work independently and cooperate. Service placement is being completed by a two-stage optimization method. Our service placement orchestrator is distributed, services are discovered dynamically, resources can be monitored, and communication messages among fog nodes can be signed and encrypted as a solution to the weakness of multi-agent systems due to the lack of monitoring tools and security.

The Durban University of Technology Faculty of Health Sciences Decentralized Clinical Training Project: Protocol for an Implementation Study in KwaZulu-Natal, South Africa.

The Durban University of Technology (DUT) Faculty of Health Sciences (FHS) in KwaZulu-Natal, South Africa, is embarking on a project to implement a Decentralized Clinical Training Program (DCTP). The DUT FHS DCTP project is being conducted in response to the growing demands of students requiring clinical service placements as part of work-integrated learning. The project is also geared toward responding to existing gaps in current practices related to the implementation of a DCTP, which has mainly been through traditional universities providing training to medical, optometry, occupational therapy, and physiotherapy students. In South Africa, a DCTP is yet to be implemented within the context of a university of technology; it is yet to be implemented within health science faculties that offer undergraduate health science programs in mainstream biomedicine and alternative and complementary disciplines. We aim to design, pilot, and establish an effective DCTP at the DUT FHS in KwaZulu-Natal, South Africa. Participatory action research comprising various designs-namely, appreciative inquiry, qualitative case study design, phenomenography, and descriptive qualitative study design-will be used to conduct the study. Data will be collected using individual interviews, focus group discussions, nominal group technique, consensus methodology, and narrative inquiry. Study participants will include various internal and external stakeholders of the DUT, namely, academic staff; students; key informants from universities currently using successfully established DCTPs; academic support staff; staff working in human resources, finance, procurement, and accounting; and experts in other disciplines such as engineering and information systems. Overall, 4 undergraduate health science programs-namely, Radiography, Medical Orthotics and Prosthetics, Clinical Technology, and Emergency Medical Care and Rescue-will be part of the project's pilot phase. Findings from the project's pilot phase will be used to inform scale-up in the other undergraduate programs in the DUT FHS. The project is being implemented as part of the university's strategic objective of devising innovative curricula and pedagogical practices to improve the mastery, skill set, and competence of health science graduates. The study has currently commenced with the situational analysis, consisting of engagement with external stakeholders implementing DCTPs. The data to be generated from the completion of the situational analysis are anticipated to be published in 2024. This project is envisioned to facilitate collaboration among the universities of technology, traditional universities, Ministry of Health, and private sector for clinical placement of undergraduate health science students in health establishments that are away from the university, thereby exposing them to real-life experiences related to health care. This will facilitate authentic learning experiences that will contribute to improved competencies of graduates in relation to the health needs of society and the multiple realities of the South African health system. PRR1-10.2196/52243.

Exploring the effectiveness of service migration strategies for virtual network embedding

Network slicing, a key component of post-5G networks, has brought virtual network embedding (VNE) to the forefront of networking research. However, existing VNE approaches are limited by their consideration of static network topologies, failing to account for the dynamic nature of real-world networks. This limitation becomes particularly problematic in the face of link failures or topology modifications, rendering these approaches ineffective. To address this, we propose a new service placement strategy that remains effective even when the network topology changes. Furthermore, we introduce several service migration strategies and thoroughly investigate their effectiveness. Our results demonstrate the adaptability of our proposed strategy, which leverages graph neural networks, in handling link failures without necessitating relearning. This adaptability underlines the potential of our approach to significantly enhance the robustness and flexibility of service migration in VNE, thereby contributing to the evolution of network slicing in post-5G networks.

Optimizing Service Placement and Enhancing Service Allocation for Microservice Architectures in Cloud Environments

With the increasing popularity of microservice architecture, there is a growing need to deploy service-based applications efficiently in cloud environments. Traditional cluster schedulers often fail to optimize service placement adequately, as they only consider resource constraints and overlook traffic demands between services. This oversight can lead to performance issues such as high response times and jitter. To address this challenge, we propose a novel approach to optimize the placement of service-based applications in clouds. Our approach involves partitioning the application into segments while minimizing overall traffic between them, and then strategically allocating these segments to machines based on their resource and traffic demands. We have developed a prototype scheduler and conducted extensive experiments on test bed clusters to evaluate its performance. The results demonstrate that our approach surpasses existing container cluster schedulers and heuristic methods, significantly reducing overall inter-machine traffic and improving application performance

CRAMP: Clustering-based RANs association and MEC placement for delay-sensitive applications

With advancements in networking technology and ubiquitous computing, there has been a significant increase in the number of edge devices and delay-sensitive applications. To facilitate efficient processing, mobile edge computing (MEC) technology provides resources through MEC servers, which are deployed at the radio access networks (RANs) of 5G networks. However, these MEC servers possess a limited amount of resources, making their effective management of these resources a critical challenge. This is due to the uneven distribution of resource utilization, where some resources become overutilized while others remain underutilized. Addressing the issue above while simultaneously satisfying user requirements for delay-sensitive applications poses a significant challenge at the edge. In this paper, we propose a clustering-based efficient RANs association and MEC server placement model to tackle this challenge. Our primary objective is to minimize MEC server deployment costs while ensuring that the delays of these applications are effectively managed. We propose a greedy algorithm called the clustering-based radio access networks association and mobile edge computing placement (CRAMP) algorithm, which determines the optimal location of MEC servers to associate with RANs. Simulation results demonstrate that our proposed algorithm outperforms existing approaches regarding cost efficiency and delay management.

Service placement in fog–cloud computing environments: a comprehensive literature review

Service placement in fog–cloud computing environments: a comprehensive literature review

Graph Partition and Multiple Choice-UCB Based Algorithms for Edge Server Placement in MEC Environment

Graph Partition and Multiple Choice-UCB Based Algorithms for Edge Server Placement in MEC Environment