Concurrent Services Research Articles

Physical activity and heart rate in PTSD inpatients: Moderation by custody of a service dog

Posttraumatic stress disorder is associated with autonomic hyperarousal often shown to involve elevated resting heart rate and, simultaneously if somewhat paradoxically, reduced physical activity. Both are risk factors for cardiovascular disease and so may contribute to its elevated prevalence in persons with this diagnosis. Epidemiological studies have observed dog owners to exhibit lower rates of cardiovascular disease. Non-randomized between-group studies have found service dog companionship to be associated with increased physical activity and with lower resting heart rate. A challenge to research in this area is its vulnerability to selection biases in group assignment which could lead to over-estimation of advantages or disadvantages associated with pet or service dog contact and companionship. The current study executed a within-subjects design investigating physical activity and heart rate in a sample of U.S. military Veterans engaged in residential treatment for PTSD and a concurrent service animal training intervention. A mean of 37 days of continuously-recorded day-time physical activity and heart rate were obtained from 45 participants. On days when they had custody of a familiar service-dog-in-training, participants exhibited increased physical activity accompanied by a small reduction in concurrent, activity-adjusted heart rate. Though observed in a specialized context, these short-term findings align with prior observations of lowered risk of cardiovascular disease in association with pet dog ownership. Extended longitudinal designs will be necessary to determine whether such short-term effects truly mediate lowered long-term risk.

Prevalence and predictors of multiple, concurrent public early childhood education utilization among head start children

With growing public investment in early childhood services, families enrolled in Head Start (HS) have increasing opportunities to participate in other public early care and education (ECE) programs. Using integrated administrative data from across Iowa’s service systems, we examined the prevalence and predictors of HS children’s multiple concurrent ECE service utilization the year before entering kindergarten, focusing on state pre-K and child care subsidy (CCS). Results showed that 61 % of children participating in HS additionally utilized other public ECE programs, with 44 % participating in state pre-K, 8.3 % in CCS, and 8.7 % in both. Utilization patterns differed based on child and family characteristics. Children who were Black, living in urban areas, or born to single mothers were more likely to participate in multiple, concurrent services, especially a combination of services that included CCS. Results imply that improving coordination and collaboration across various ECE public programs may support HS families’ needs.

Concurrent service auto-scaling for Knative resource quota-based serverless system

Serverless computing platforms currently provide application developers with two ways to control their services’ resource usage cost: resource usage-based and resource quota-based. In usage-based systems, resources are assigned to services based on the amount they consume. Meanwhile, a fixed resource amount is reserved and shared between services for quota-based systems. Several studies have been proposed to enhancing the default serverless auto- scaling algorithms to optimize resource usage and service quality. However, almost all previous works targeted usage-based systems and optimized auto-scaling performance for each separate service. In contrast, this work targets auto-scaling of concurrent services in serverless quota-based systems. In serverless quota-based systems, over-quota resource usage and latency Service Level Objective (SLO) violation may occur when auto-scaling new instances during burst traffic moments. Hence, we aim to find an optimal hybrid auto-scaling decision to minimize over-quota resource usage and latency SLO violation. To solve this problem, We applied deep reinforcement learning and traffic prediction techniques. Our solution is developed and implemented based on the characteristics of the most popular open-source serverless platform Knative. For evaluation, we compared our solution with the default Knative auto-scaler and our previously proposed usage-based hybrid Knative auto-scaler. We configured the default Knative auto-scaler with two different settings that can optimize the total resource usage of all services or minimize service latency. Compared to the optimized resource usage version, our solution traded a slightly higher resource usage for an 11.3% latency SLO violation duration reduction. Meanwhile, compared with the optimized latency version, our solution reduced the over-quota resource usage rate by 18.25% while achieving a similar latency SLO preservation performance. Compared to the usage-based hybrid auto-scaler, the latency SLO violation rate and over-quota resource usage were approximately reduced by half.

Performance analysis of parallel composite service-based applications in clouds

When processing composite service application jobs containing parallel tasks, service providers can optimize their quality of services (QoS) based on refined parallelism settings and resource allocation schemes by leveraging analytical models. However, building such analytical models is particularly challenging due to the fact that an accurate model is required to capture the dependence among sequential or concurrent services and predict response time of each service with varying degrees of parallelism (DoPs). Faced with these challenges, we propose a novel multiclass multi-pool analytical model for composite service applications deployed in clouds. Specifically, we consider embarrassingly parallel services, which do not require additional overhead to split tasks into multiple independent subtasks. We first establish a multi-pool queue network that takes into account the dependence among services and analyze task parameters of each service. To optimize the QoS of embarrassingly parallel services, we present a differentiated parallel processing mechanism which can set varying DoPs for tasks. We also propose an original modulating partition method to predict important performance indicators of each service. By leveraging the proposed model, service providers can obtain optimal settings for the DoPs, resource allocation, and the number of cloud servers, to achieve specific performance levels. Through extensive experiments based on the rendering service dataset and Alibaba’s open cluster traces, we demonstrate that the proposed model can not only provide accurate prediction results but also significantly reduce jobs’ response time by at least 20%.

Optimized resource usage with hybrid auto-scaling system for knative serverless edge computing

In the most popular serverless platform - Knative, dynamic resource allocation is implemented using horizontal auto-scaling algorithms to create or delete service instances based on different monitored metrics. However, the assigned resources for each instance are fixed. Vertical scaling up or down assigned resources per instance is required to avoid over-provisioning resources which are limited at the edge. Hybrid (horizontal and vertical) auto-scaling solutions proposed by existing works have several limitations. These solutions are optimized for separated services and get degraded performance when applied in a normal environment with multiple concurrent services. Further, most methods make significant changes to the original Knative platform, and have not been considered to be adopted since then. In this article, instead of Knative modification, we developed separated Kubernetes operators and custom resources (CRs) that can assist the Knative auto-scaler with optimal hybrid auto-scaling configurations based on traffic prediction. First, we characterize each service with a profile of different assigned resource levels pairing with their optimal target Knative’s horizontal scaling request concurrency. Then, based on these profiles, we calculate the best-assigned resources level, target concurrency level, and the number of required instances corresponding to each future time step’s predicted traffic. Finally, these configurations are applied to Knative’s default auto-scaler and services’ CR. Experiments done on our testbed compared our solution with a Knative hybrid auto-scaler solution that does not consider the service’s target request concurrency, and the default Knative horizontal auto-scaler. The results show our solution improvements up to 14% and 20% in terms of resource usage, respectively.

Evaluating the effectiveness of concurrent sessions and counselors' attention allocation in online counseling.

With its anonymity and accessibility, text-based online counseling has shown great potential in reaching people with mental health needs. One strategy adopted to meet the service gap is concurrent counseling, that is, each counselor attending to more than one client at a time. To date, there is no reported evidence supporting its rationality and effectiveness. This study investigated the potential opportunities, effectiveness, and caveats in concurrent service delivery and identified the optimal cutoff number of concurrent sessions while maintaining the quality of service at or above a set threshold. We analyzed the transcript of 54,716 online counseling sessions from Open Up, a free, 24/7 text-based counseling service, to develop an attention score that measures the attention allocation of counselors and examined the impact of the counselor's attention allocation on client satisfaction and service outcomes. On average, compared to nonconcurrent sessions, concurrent sessions were longer, more likely to end prematurely, and had lower client satisfaction. We also identified an optimal attention score of approximately 0.4 (out of 1.0, which denotes full attention), which translates to two to three concurrent sessions. This study provides empirical evidence for the feasibility of conducting multiple text-based sessions concurrently without compromising service quality and client experience. Our method of measuring the counselor attention allocation offers a way to systematically assess and evaluate concurrent sessions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Diffusion-level universality of many-server systems with concurrent service

We present diffusion limits for a queuing system with n parallel servers with concurrent service capabilities. The diffusion limits are established in the many-server heavy-traffic regime where (1−ρn)nα→β as n→∞, for α∈(0,∞)∖{1/2} and ρn a notion of normalized system load. This encompasses the so-called sub-Halfin-Whitt, super-Halfin-Whitt, non-degenerate slowdown, and super-slowdown regimes. The diffusion limits are universal in that they depend only on whether α<1/2 or α>1/2 and do not depend on specific values of α and β in both cases.

Predictive Models to Optimize Resources in Tele Critical Care in Distributed Hospital Networks

Background: Telemedicine creates the opportunity – in pandemic conditions and otherwise -- to spread health care to regions where intensivists and health services may not beavailable. It offers the opportunity to provide better patient care, decrease healthcare costs, and overall improve population health. Introduction: Critical care telemedicine has increased in deployment due to its impact on providing care at all times of the day as well as in reaching remote regions of the world. Tele-critical care (Tele-CC) systems can provide concurrent service to several hospitals andcan manage available resources more efficiently than traditional ICUs. Materials and Methods: This study utilizes the Philips eICU system and its CollaborativeResearch Database (eICU-CRD) to evaluate intensive care operations in the electronic ICU setting, with the objective of analyzing where and how system engineering techniques can bepotentially applied to enhance the effectiveness of such environments. Results: Several metrics are evaluated, including patient outcomes, APACHE score, length of stay and type of unit in regard to the age of the patient. Prediction models based on decision and regression trees are presented to estimate mortality and length of stay. Discussion: Prediction models offer the potential to optimize the Tele-CC environment by helping to estimate the number of patients who will remain in the ICU during the following days. Conclusion: Prediction models accurately estimate mortality and length of stay in ICU. The estimation of future number of patients can be used to determine the resources needed at each hospital, as well as to provide insight on potential savings when Tele-CC centers provide concurrent services to multiple hospitals.

Coproduced urban water services: When technical and governance hybridisation go hand in hand

This study questions the processes of technical and governance hybridisation in the coproduction of water services in cities of the Global South. The existing literature addresses the compensatory role that water services coproduction plays in urban and peri-urban areas, where access to centralised and reliable water resources is often lacking. However, less research focuses on the evolution of coproduced practices in relation to wider transitions of urban spaces, water resources, centralised infrastructure, and service delivery strategies. Still, the resulting technical and governance configurations stemming from these situations are largely unexplored. This study considers four cities, namely, Hanoi (Vietnam), Dar es Salaam (Tanzania), Cochabamba (Bolivia), and Addis Ababa (Ethiopia). All our case studies are somewhat characterised by rapid land-use changes, juxtaposition of rural and urban activities, varying urban typologies, and increasing poverty, sociospatial inequality, and exclusionary service provision. We draw on data collected from field surveys and participatory workshops with inhabitants and institutional actors between 2017 and 2020 as part of a recent research project. We explore the evolution of water coproduction from technological and governance perspectives. The cases analysed in the research highlight that the time and social development of water coproduction do not follow a linear path. It is rather characterised by cycles of emergence, maturation, and decline. It may build upon pre-existing forms of community-based water management that were established in rural areas (for irrigation or water harvesting, for instance). The results show that water coproduction may have different evolutions, entailing different hybridisation processes. Water coproduction can be characterised by either complementary or concurrent service configurations, by blurring actor categories, and by different drivers in the hybridisation process. Ultimately, evolution in urban coproduced practices appears as a process of technical and governance hybridisation, rather than as final and fixed hybrid systems.

Economic Scheduling of Gaseous-liquid Hydrogen Generation and Storage Plants Considering Complementarity of Multiple Products

The accessible and convenient hydrogen supply is the foundation of successful materialization for hydrogen-powered vehicles (HVs). This paper proposes a novel optimal scheduling model for gaseous-liquid hydrogen generation and storage plants powered by renewable energy to enhance the economic feasibility of investment. The gaseous-liquid hydrogen generation and storage plant can be regarded as an energy hub to supply concurrent service to both the transportation sector and ancillary market. In the proposed model, the power to multi-state hydrogen (P2MH) process is analyzed in detail to model the branched hydrogen flow constraints and the corresponding energy conversion relationship during hydrogen generation, processing, and storage. To model the coupling and interaction of diverse modules in the system, the multi-energy coupling matrix is developed, which can exhibit the mapping of power from the input to the output. Based on this, a multi-product optimal scheduling (MPOS) algorithm considering complementarity of different hydrogen products is further formulated to optimize dispatch factors of the energy hub system to maximize the profit within limited resources. The demand response signals are incorporated in the algorithm to further enhance the operation revenue and the scenario-based method is deployed to consider the uncertainty. The proposed methodology has been fully tested and the results demonstrate that the proposed MPOS can lead to a higher rate of return for the gaseous-liquid hydrogen generation and storage plant.

Industrial Shared Wireless Communication Systems—Use Case of Autonomous Guided Vehicles with Collaborative Robot

Dedicated fieldbuses were developed to provide temporal determinisms for industrial distributed real-time systems. In the early stages, communication systems were dedicated to a single protocol and generally supported a single service. Industrial Ethernet, which is used today, supports many concurrent services, but usually only one real-time protocol at a time. However, shop-floor communication must support a range of different traffic from messages with strict real-time requirements such as time-driven messages with process data and event-driven security messages to diagnostic messages that have more relaxed temporal requirements. Thus, it is necessary to combine different real-time protocols into one communication network. This raises many challenges, especially when the goal is to use wireless communication. There is no research work on that area and this paper attempts to fill in that gap. It is a result of some experiments that were conducted while connecting a Collaborative Robot CoBotAGV with a production station for which two real-time protocols, Profinet and OPC UA, had to be combined into one wireless network interface. The first protocol was for the exchange of processing data, while the latter integrated the vehicle with Manufacturing Execution System (MES) and Transport Management System (TMS). The paper presents the real-time capabilities of such a combination-an achievable communication cycle and jitter.

Optimal Scheduling in the Multiserver-job Model under Heavy Traffic

Multiserver-job systems, where jobs require concurrent service at many servers, occur widely in practice. Essentially all of the theoretical work on multiserver-job systems focuses on maximizing utilization, with almost nothing known about mean response time. In simpler settings, such as various known-size single-server-job settings, minimizing mean response time is merely a matter of prioritizing small jobs. However, for the multiserver-job system, prioritizing small jobs is not enough, because we must also ensure servers are not unnecessarily left idle. Thus, minimizing mean response time requires prioritizing small jobs while simultaneously maximizing throughput. Our question is how to achieve these joint objectives. We devise the ServerFilling-SRPT scheduling policy, which is the first policy to minimize mean response time in the multiserver-job model in the heavy traffic limit. In addition to proving this heavy-traffic result, we present empirical evidence that ServerFilling-SRPT outperforms all existing scheduling policies for all loads, with improvements by orders of magnitude at higher loads. Because ServerFilling-SRPT requires knowing job sizes, we also define the ServerFilling-Gittins policy, which is optimal when sizes are unknown or partially known.

A turnaround control system to automatically detect and monitor the time stamps of ground service actions in airports: A deep learning and computer vision based approach

As it is widely known, several ground services are provided by the airports for the domestic and international flights of the commercial passenger aircraft. Some of these services are conducted during the period called as the turnaround which starts with the parking of the aircraft in the aprons before the flight and ends with their leave from the aprons for the flight. Turnaround processes achieved in short time periods allow using the limited airport resources including the service vehicles and staff effectively. In addition, commercial reputation losses and financial losses that may arise from delays can be reduced as well as the delay-associated turnaround penalties. In this article, a deep learning and computer vision based system that detects and allows monitoring the airport service actions is proposed. The proposed system is capable of analyzing all the primary ground services for an aircraft parking on its apron by employing the RGB video frame sequences obtained from a single fixed camera focusing on the apron. In the service detection and analysis modules of the proposed airport ground service analysis system, some deep learning-based subsystems and in-house-developed algorithms were included and utilized. For the training of the machine learning models, a study-specific dataset was used and the constructed learning models were evaluated on real-life cases. Experimental results obtained as a result of the performance evaluations show that the proposed system is quite successful with precision rates over 90% in the detection and analysis of the airport ground services. This study is one of the limited research studies in which deep learning and computer vision techniques have been applied to detect and analyze the ground service actions. The proposed system is also capable of real-time data processing/analysis and concurrent service action monitoring. Furthermore, it allows monitoring when the service is received by stamping the times of service start/end. In a consideration of industrial relevance or operational perspective, such a system may facilitate the airport ground service management noticeably and reduce the delay-associated costs caused by the timing of the ground services.

Adaptive Optimal Management of EV Battery Distributed Energy for Concurrent Services to Transportation and Power Grid in a Fleet System Under Dynamic Service Pricing

Deployment of electric vehicles (EVs) in a fleet system to deal with environmental issues has been at the center of attention over the past several years. While the battery of each EV offers small storage, hundreds of EVs collectively can offer large energy storage to serve a power grid. This article develops a model for a central controller in a fleet system that allows adaptive utilization of EV batteries distributed energy for concurrent services to the transportation and power grid. The optimization model integrates various slack variables and control parameters for managing real-time fare prices, adaptive energy, and reserve margin allocation, interaction with the grid operator, and meeting the fleet target revenue. The proposed model incorporates EV driver's input into the scheduling process to allow the driver to flexibly manage their battery capacities based on their availability and assessment of the transportation services demand. A dynamic pricing mechanism is developed for real-time calculation of fare rates to allow the EV fleet optimization problem to achieve a daily revenue target while limiting fare prices in a competitive market. Numerical results indicate that the model can manage several EVs for various services while enhancing the fleet financial metrics.

Characterizing female infectious syphilis cases in British Columbia to identify opportunities for optimization of care.

The rate of infectious syphilis continues to increase among females in British Columbia (BC) and Canada, raising concerns of increased incidence of congenital syphilis. We characterized syphilis cases among females in BC to identify opportunities to prevent syphilis and optimize its care. All cases of infectious syphilis diagnosed in BC between March 13, 2018 and December 31, 2020 and reported as female gender were reviewed. Demographics, risk factors and concurrent conditions were collected from a provincial surveillance system. Subgroup analyses comparing cases with and without housing instability, substance use, mental illness and a recent sexually transmitted infection (STI) were conducted to understand differences between these subgroups. Statistical associations were calculated using chi-square or t-tests. There were 226 reported cases of female infectious syphilis in BC during this period: 38 (16.8%) in 2018; 74 (32.7%) in 2019; and 114 (50.4%) in 2020. Mean age was 32 years (range 15-75 years). Of those who reported concurrent conditions, most cases had experiences with housing instability (71.1%), substance use (68.2%) and mental illness (83.9%), while 42.9% had a recent STI. Cases who reported housing instability or substance use were significantly more likely to have experiences with a recent STI, street involvement, transactional sex, mental illness and income assistance (all p<0.01). Our findings highlight the importance of fostering an enabling environment for syphilis care. Concurrent services to support individuals with syphilis as well as housing instability, substance use and mental illness, may help prevent syphilis and improve wellbeing.

Repatriation in university museum collections: Case studies from the Phoebe A. Hearst Museum of Anthropology

AbstractUniversity-based anthropology museums are uniquely positioned to pursue nuanced decisions concerning the disposition of collections in their care, setting best practice for the field. The authors describe a three-staged approach to repatriations that they led during their concurrent service as head of cultural policy and repatriation (Jordan Jacobs) and director (Benjamin Porter) of the University of California, Berkeley’s Phoebe A. Hearst Museum of Anthropology between 2015 and 2019. Examples involving human remains and cultural objects from Australia, Canada, Democratic Republic of the Congo, Iraq, Japan, Mexico, Panama, Peru, Saipan, Senegal, Vanuatu, Venezuela, and South Carolina in the United States demonstrate the benefits of transparency, open communication, and rigorous investigation of provenance and provenience, which may or may not lead to transfer based on the criteria and priorities of potential recipients. This article also provides a history of the Hearst Museum’s Cultural Policy and Repatriation division, which was disbanded in 2021.

A proactive caching and offloading technique using machine learning for mobile edge computing users

The mobile edge computing (MEC) paradigm provides cloud and application services at the “edge” of user networks for providing ubiquitous access to resources. The heterogeneous services cause varying network traffic that sometimes increases delay. In edge-based services, concurrency in data distribution requires caching and offloading features. This article introduces a proactive caching technique with offloading (PCTO) ability by considering the need for parallel user services. The proposed method performs demand-aware offloading to meet the concurrent service dissemination requirements. Network-level caching and its forecast in concurrent service distribution are performed to reduce the response time. The offloading and caching processes are streamlined using deep recurrent learning for the failing service distribution intervals. In the learning process, the machine is trained for prior failures and for pursuing offloading instances. Based on the learning output, the caching level and offloading rate are determined for the queuing services. The performance of the proposed method is verified using the metrics service ratio, response failures, latency, offloading rate, and caching ratio.

Empirical Performance and Energy Consumption Evaluation of Container Solutions on Resource Constrained IoT Gateways.

Containers virtually package a piece of software and share the host Operating System (OS) upon deployment. This makes them notably light weight and suitable for dynamic service deployment at the network edge and Internet of Things (IoT) devices for reduced latency and energy consumption. Data collection, computation, and now intelligence is included in variety of IoT devices which have very tight latency and energy consumption conditions. Recent studies satisfy latency condition through containerized services deployment on IoT devices and gateways. They fail to account for the limited energy and computing resources of these devices which limit the scalability and concurrent services deployment. This paper aims to establish guidelines and identify critical factors for containerized services deployment on resource constrained IoT devices. For this purpose, two container orchestration tools (i.e., Docker Swarm and Kubernetes) are tested and compared on a baseline IoT gateways testbed. Experiments use Deep Learning driven data analytics and Intrusion Detection System services, and evaluate the time it takes to prepare and deploy a container (creation time), Central Processing Unit (CPU) utilization for concurrent containers deployment, memory usage under different traffic loads, and energy consumption. The results indicate that container creation time and memory usage are decisive factors for containerized micro service architecture.

Incremental, Distributed, and Concurrent Service Coordination for Reliable and Deterministic Systems-of-Systems

Systems-of-systems (SoS) are gaining increasing attention for the realization of safety-relevant applications with reliability and real-time requirements, by coordinating autonomous constituent systems from different application areas. For a given application that is initiated at a constituent system, the provision and use of services between constituent systems must be coordinated. This involves identifying constituent systems that can provide the services, optimizing the use of the services driven by extra-functional properties, performing admission control for service provision, reserving resources for provided services, and recursively using services from subcontracted constituent systems to realize service provisions. In this article, new conceptual models with service provisioning for reliable and deterministic SoS applications, as well as, distributed algorithms for the discovery, optimized selection, and admission of service provisions are introduced. The proposed models will result in a two-step admission control algorithm with short- and long-term admissions based on schedulabiliy tests, resulting in service provisions between constituent systems. Furthermore, this article will investigate the integration of cloud resources for the on-demand instantiation of services. The models and the service coordination are evaluated in a use case from the health-care domain, which investigates the execution time and the scalability of the service coordination.

Classification and Storage Method of Marine Multi-source Transmission Data under Cloud Computing

Xu, H., 2020. Classification and storage method of marine multi-source transmission data under cloud computing. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 84-86. Coconut Creek (Florida), ISSN 0749-0208.With the development of the Internet of Things, the types and quantity of Internet of Things devices are increasing rapidly, and the amount of data collected is even more difficult to count. Digital marine multi-source data transmission involves a wide range of technologies, many application fields, complex levels and large amount of data, which is a huge system engineering. Data is the center of information application for various offshore multi-source transmission data. The traditional data storage system has been unable to adapt to the changes of multi-source data transmission at sea under the Internet of Things environment. According to the changing characteristics of massive data, combined with the advantages of cloud storage system, the marginal storage of offshore multi-source transmission data is carried out to provide users with real-time and efficient concurrent access services. Based on this, this paper will make a detailed analysis on the classification and storage methods of marine multi-source transmission data under cloud computing, so as to provide effective support for related research.