Allocation Of Services Research Articles

An intelligent approach of task offloading for dependent services in Mobile Edge Computing

With the growing popularity of Internet of Things (IoT), Mobile Edge Computing (MEC) has emerged for reducing the heavy workload at the multi-cloud core network by deploying computing and storage resources at the edge of network close to users. In IoT, services are data-intensive and event-driven, resulting in extensive dependencies among services. Traditional task offloading schemes face significant challenges in the IoT scenario with service dependencies. To this end, this paper proposes an intelligent approach for minimizing latency and energy consumption which jointly considers the task scheduling and resource allocation for dependent IoT services in MEC. Specifically, we establish the system model, communication model as well as computing model for performance evaluation by fully considering the dependent relationships among services, and an optimization problem is proposed for minimizing the delay and energy consumption simultaneously. Then, we design a layered scheme to deal with the service dependencies, and present detailed algorithms to intelligently obtain optimal task scheduling and resource allocation policies. Finally, simulation experiments are carried out to validate the effectiveness of the proposed scheme.

Collective reinforcement learning based resource allocation for digital twin service in 6G networks

The 6th generation (6G) mobile communications technology will realize the interconnection of humans, machines, things as well as virtual space. The development of digital twins (DTs) and 6G has accelerated the Internet of Things (IoT) in an unprecedented way. The combination of DTs and edge intelligence (EI) enables powerful digital space synchronized with the real world constructed in the intelligent edge, bringing real-time, and adaptive services delivery of IoT. However, the dynamic features and heterogeneous resources in 6G-enabled IoT make the resource allocation for computation-intensive and delay-sensitive DTs services more challenging. In this paper, we first define the DTs implementation process as a DT service function chain (DTSFC) and address the resource allocation problem of DTs-empowered networks in form of dynamic DTSFCs orchestration. We further propose a novel collective reinforcement learning (CRL) method which is inspired by human collaboration, to realize the effective resource allocation of DTSFCs. Numerical results verify that the proposed CRL algorithm improves the learning efficiency and generalization ability compared with the benchmarks.

Machine Learning-Driven Ubiquitous Mobile Edge Computing as a Solution to Network Challenges in Next-Generation IoT

Ubiquitous mobile edge computing (MEC) using the internet of things (IoT) is a promising technology for providing low-latency and high-throughput services to end-users. Resource allocation and quality of service (QoS) optimization are critical challenges in MEC systems due to the large number of devices and applications involved. This results in poor latency with minimum throughput and energy consumption as well as a high delay rate. Therefore, this paper proposes a novel approach for resource allocation and QoS optimization in MEC using IoT by combining the hybrid kernel random Forest (HKRF) and ensemble support vector machine (ESVM) algorithms with crossover-based hunter–prey optimization (CHPO). The HKRF algorithm uses decision trees and kernel functions to capture the complex relationships between input features and output labels. The ESVM algorithm combines multiple SVM classifiers to improve the classification accuracy and robustness. The CHPO algorithm is a metaheuristic optimization algorithm that mimics the hunting behavior of predators and prey in nature. The proposed approach aims to optimize the parameters of the HKRF and ESVM algorithms and allocate resources to different applications running on the MEC network to improve the QoS metrics such as latency, throughput, and energy efficiency. The experimental results show that the proposed approach outperforms other algorithms in terms of QoS metrics and resource allocation efficiency. The throughput and the energy consumption attained by our proposed approach are 595 mbit/s and 9.4 mJ, respectively.

THE ROLE OF BK TEACHER IN DIRECTING CLASS XII STUDENTS TO CONTINUE HIGHER EDUCATION THROUGH PLACEMENT AND DISTRIBUTION SERVICES AT MAN SIBOLGA

The purpose of this research is to determine the role of BK (Guidance and Counseling) teachers in guiding twelfth-grade students to pursue higher education through placement and allocation of services at MAN Sibolga. This study employs a qualitative descriptive research design. The research sample was taken using purposive sampling and snowball sampling techniques, consisting of three students from three different classes. Data collection was conducted through interviews. Data analysis techniques include data reduction, data presentation, and drawing conclusions. The research findings are as follows: 1) The role of BK teachers in guiding twelfth-grade students to pursue higher education through placement and allocation services at MAN Sibolga is carried out effectively, and some students have started to make decisions about their post-high school education, 2) BK teachers play a role in assisting students by providing input on higher education options after high school, and 3) by implementing placement and allocation services to guide students in pursuing higher education, students are expected to be able to choose their own further education based on their own preferences, rather than feeling forced or following their peers.

Depression Is Associated With Non-Home Discharge After Coronary Artery Bypass Graft

IntroductionDepression is disproportionately high in patients with coronary artery disease and has been associated with adverse outcomes following coronary artery bypass graft (CABG). One quality metric, non-home discharge (NHD), can have substantial implications for patients and health care resource utilization. Depression increases the risk of NHD after many operations, but it has not been studied after CABG. We hypothesized that a history of depression would be associated with an increased risk of NHD following CABG. MethodsCABG cases were identified from the 2018 National Inpatient Sample using ICD-10 codes. Depression, demographic data, comorbidities, length of stay (LOS), rate of NHD were analyzed using appropriate statistical tests where a P-value < 0.05 was defined as statistically significant. Adjusted multivariable logistic regression models were used to assess independent association between depression and NHD as well as LOS while controlling for confounders. ResultsThere were 31,309 patients, of which 2743 (8.8%) had depression. Depressed patients were younger, females, in a lower income quartile, and more medically complex. They also demonstrated more frequent NHD and prolonged LOS. After adjusted multivariable analysis, depressed patients had a 70% increased odds of NHD (adjusted odds ratio: 1.70 [1.52-1.89] P < 0.001) and a 24% increased odds of prolonged LOS (AOR: 1.24 [1.12-1.38] P < 0.001). ConclusionsFrom a national sample, depressed patients were associated with more frequent NHD following CABG. To our knowledge, this is the first study to demonstrate this, and it highlights the need for improved preoperative identification in order to improve risk stratification and timely allocation of discharge services.

Incorporating resilience when assessing pandemic risk in the Arctic: a case study of Alaska

The discourse on vulnerability to COVID-19 or any other pandemic is about the susceptibility to the effects of disease outbreaks. Over time, vulnerability has been assessed through various indices calculated...

AI-based energy-efficient path planning of multiple logistics UAVs in intelligent transportation systems

In the context of Industry 5.0, artificial intelligence (AI)-based logistics Unmanned Aerial Vehicles (UAVs) have been widely applied in intelligent transportation systems due to their advantages of faster speed, lower cost, more environment-friendly, and less manpower needed. Whereas, most of the existing logistics UAV delivery models have not taken the energy consumption of the logistics UAVs and mixed time windows of the customers, which leads to their models cannot be applied in practical transportation systems. Therefore, we propose to minimize the total energy cost of multiple logistics UAVs during the customized products delivery period for a smart transportation system. Taking the energy consumption variation of the logistics UAVs, mixed time windows of the customers, as well as simultaneous delivery and pick up into consideration, we formulate a cooperative path planning problem via jointly optimizing the route of the logistics UAVs and the service allocation. To solve this large-scale integer programming problem, we employ the Large Neighborhood Search Algorithm (LNS) to accelerate the convergence rate of Genetic Algorithm (GA), and then develop an improved GA based cooperative path planning algorithm (IGCPA). The optimization procedure of the proposed algorithm IGCPA is divided into two phases, using the GA crossover operator and variational operator in the global search phase and LNS operator in the local search phase, and validating the integer programming model and the effectiveness of the solution algorithm based on different scale cases. Finally, abundant simulation results show that the energy cost of IGCPA is reduced by 17.35%, 15.18% and 9.99% compared with GA, LNS and Particle Swarm Optimization (PSO), respectively. Furthermore, the IGCPA is validated using Solomn standard data, which further verifies that the IGCPA can enhance the convergence rate of GA as well as obtain a lower delivery cost. Sensitivity analysis of the maximum UAV load and battery capacity reveals that the distribution cost tends to decrease and then increase as the increase of maximum load and battery capacity.

Portfolio Optimization Using Markowitz Model and Index Model – A Study on 10 Selected Stocks

Portfolio optimization nowadays plays a significant role in financial industry. This paper aims to analyze the asset allocation of technology, financial services, and consumer defense industries. Ten representative companies were selected from these industries. At the same time, the difference between Markowitz model and Sharpe single index model is observed by using the three constraints of quantification of actual financial market factors. On this basis, this paper optimizes the asset portfolio. This result shows that: first, the correlation coefficient between SPX500 and listed companies is very high, which is a good choice in the portfolio of balancing risk and return. Second, we test several portfolios under 3 constraints, and it is found that a higher return always occurs with a high risk. Third, without the consideration of SP index, investors can get the highest return. The results in this paper may shed light on the financial investors.

National Evaluation of HIV Service Resource Allocation in Tanzania

Using time-driven activity-based costing (TDABC), we examined resource allocation and costs for HIV services throughout Tanzania at patient and facility levels. This national, cross-sectional analysis of 22 health facilities quantified costs and resources associated with 886 patients receiving care for five HIV services: antiretroviral therapy, prevention of mother-to-child transmission, HIV testing and counseling, voluntary medical male circumcision, and pre-exposure prophylaxis. We also documented total provider-patient interaction time, the cost of services with and without inclusion of consumables, and conducted fixed-effects multivariable regression analyses to examine patient- and facility-level correlates of costs and provider-patient time. Findings showed that resources and costs for HIV care varied significantly throughout Tanzania, including as a function of patient- and facility-level characteristics. While some variation may be preferable (e.g., needier patients received more resources), other areas suggested a lack of equity (e.g., wealthier patients received more provider time) and presented opportunities to optimize care delivery protocols.

Study of Uplink Resource Allocation for 5G IoT Services by Using Reinforcement Learning

&lt;p&gt;In order to support real time IoT services, the ultra Reliable and Low Latency Communications (uRLLC) was proposed in 5G wireless communication network. Different from the grant based access in 4G, the grant free technique is proposed in 5G to reduce the random access delay of uRLLC-required applications. This paper proposes the dedicated resource for exclusive access of individual UE and the shared resource pool for the contention of multiple UEs by adopting the reinforcement learning approach. The objective of this paper is to accomplish the uplink successful rate above 99.9% under certain transmission error probability. The proposed Prediction based Hybrid Resource Allocation (PHRA) scheme allocates the access resource in a heuristic manner by referring to the activity of UEs. The dedicated resource is mainly allocated to the high activity UEs and the initial transmission of UEs with medium activity while the shared resource pool is allocated for the re-transmission of medium activity UEs and low activity UEs by using the reinforcement learning model. The burst traffic model was applied during the exhaustive experiments. And the simulation results show that the proposed scheme achieves higher uplink packet delivery ratio and more effective resource utilization than the other schemes.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Area-based comparison of risk factors and testing rates to improve sexual health care access: cross-sectional population-based study in a Dutch multicultural area

ObjectivesAreas with high sexually transmitted infection (STI) testing rates may not require additional strategies to improve testing. However, it may be necessary to intervene in areas with elevated STI risk,...

Shared Backup Allocation Model of Middlebox Based on Workload-Dependent Failure Rate

With the network function virtualization technology, a middlebox can be deployed as software on commercial servers rather than on dedicated physical servers. A backup server is necessary to ensure the normal operation of the middlebox. The workload can affect the failure rate of backup server; the impact of workload-dependent failure rate on backup server allocation considering unavailability has not been extensively studied. This paper proposes a shared backup allocation model of middlebox with consideration of the workload-dependent failure rate of backup server. Backup resources on a backup server can be assigned to multiple functions. We observe that a function has four possible states and analyze the state transitions within the system. Through the queuing approach, we compute the probability of each function being available or unavailable for a certain assignment, and obtain the unavailability of each function. The proposed model is designed to find an assignment that minimizes the maximum unavailability among functions. We develop a simulated annealing algorithm to solve this problem. We evaluate and compare the performances of proposed and baseline models under different experimental conditions. Based on the results, we observe that, compared to the baseline model, the proposed model reduces the maximum unavailability by an average of 29% in our examined cases.

Conversational Agent Interventions for Mental Health Problems: Systematic Review and Meta-analysis of Randomized Controlled Trials.

Mental health problems are a crucial global public health concern. Owing to their cost-effectiveness and accessibility, conversational agent interventions (CAIs) are promising in the field of mental health care. This study aims to present a thorough summary of the traits of CAIs available for a range of mental health problems, find evidence of efficacy, and analyze the statistically significant moderators of efficacy via a meta-analysis of randomized controlled trial. Web-based databases (Embase, MEDLINE, PsycINFO, CINAHL, Web of Science, and Cochrane) were systematically searched dated from the establishment of the database to October 30, 2021, and updated to May 1, 2022. Randomized controlled trials comparing CAIs with any other type of control condition in improving depressive symptoms, generalized anxiety symptoms, specific anxiety symptoms, quality of life or well-being, general distress, stress, mental disorder symptoms, psychosomatic disease symptoms, and positive and negative affect were considered eligible. This study followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Data were extracted by 2 independent reviewers, checked by a third reviewer, and pooled using both random effect models and fixed effects models. Hedges g was chosen as the effect size. Of the 6900 identified records, a total of 32 studies were included, involving 6089 participants. CAIs showed statistically significant short-term effects compared with control conditions in improving depressive symptoms (g=0.29, 95% CI 0.20-0.38), generalized anxiety symptoms (g=0.29, 95% CI 0.21-0.36), specific anxiety symptoms (g=0.47, 95% CI 0.07-0.86), quality of life or well-being (g=0.27, 95% CI 0.16-0.39), general distress (g=0.33, 95% CI 0.20-0.45), stress (g=0.24, 95% CI 0.08-0.41), mental disorder symptoms (g=0.36, 95% CI 0.17-0.54), psychosomatic disease symptoms (g=0.62, 95% CI 0.14-1.11), and negative affect (g=0.28, 95% CI 0.05-0.51). However, the long-term effects of CAIs for the most mental health outcomes were not statistically significant (g=-0.04 to 0.39). Personalization and empathic response were 2 critical facilitators of efficacy. The longer duration of interaction with conversational agents was associated with the larger pooled effect sizes. The findings show that CAIs are research-proven interventions that ought to be implemented more widely in mental health care. CAIs are effective and easily acceptable for those with mental health problems. The clinical application of this novel digital technology will conserve human health resources and optimize the allocation of mental health services. PROSPERO CRD42022350130; https://tinyurl.com/mvhk6w9p.

Fair and Efficient Allocation of Scarce Resources Based on Predicted Outcomes: Implications for Homeless Service Delivery

Artificial intelligence, machine learning, and algorithmic techniques in general, provide two crucial abilities with the potential to improve decision-making in the context of allocation of scarce societal resources. They have the ability to flexibly and accurately model treatment response at the individual level, potentially allowing us to better match available resources to individuals. In addition, they have the ability to reason simultaneously about the effects of matching sets of scarce resources to populations of individuals. In this work, we leverage these abilities to study algorithmic allocation of scarce societal resources in the context of homelessness. In communities throughout the United States, there is constant demand for an array of homeless services intended to address different levels of need. Allocations of housing services must match households to appropriate services that continuously fluctuate in availability, while inefficiencies in allocation could “waste” scarce resources as households will remain in-need and re-enter the homeless system, increasing the overall demand for homeless services. This complex allocation problem introduces novel technical and ethical challenges. Using administrative data from a regional homeless system, we formulate the problem of “optimal” allocation of resources given data on households with need for homeless services. The optimization problem aims to allocate available resources such that predicted probabilities of household re-entry are minimized. The key element of this work is its use of a counterfactual prediction approach that predicts household probabilities of re-entry into homeless services if assigned to each service. Through these counterfactual predictions, we find that this approach has the potential to improve the efficiency of the homeless system by reducing re-entry, and, therefore, system-wide demand. However, efficiency comes with trade-offs - a significant fraction of households are assigned to services that increase probability of re-entry. To address this issue as well as the inherent fairness considerations present in any context where there are insufficient resources to meet demand, we discuss the efficiency, equity, and fairness issues that arise in our work and consider potential implications for homeless policies.

Tackling Heterogeneous Traffic in Multi-access Systems via Erasure Coded Servers

In cloud systems, the number of servers is fixed, and each server is usually dedicated to a job type. However, the traffic of various jobs can vary across time, and hence, a rigid allocation of servers does not perform well. In this paper, we propose the addition of erasure-coded servers that can flexibly serve multiple job types without additional storage cost. We show that coding expands the service capacity region, thus enabling the system to handle variable traffic. Moreover, we characterize the response time of the coded system in various arrival rate regimes and show that adding even a small number of coded servers can significantly reduce the mean response time.

Water Governance and Its Effectiveness

Water governance refers to the political, social, economic and administrative systems in place that influence water’s use and management. Essentially, who gets what water, when and how, and who has the right to water and related services, and their benefits. It determines the equity and efficiency in water resource and services allocation and distribution, and balances water use between socio-economic activities and ecosystems. Governing water includes the formulation, establishment and implementation of water policies, legislation and institutions, and clarification of the roles and responsibilities of government, civil society and the private sector in relation water resources and services. The outcomes depend on how the stakeholders act in relation to the rules and roles that have been taken or assigned to them. The water sector is a part of broader social, political and economic developments and is thus also affected by decisions by actors outside of the water sector. New forms of governance focusing on process-oriented societal co-steering through, for example, formal and informal networks, partnerships and dialogue, have emerged within the water sector. The governance transformation is intrinsically linked to the increasing focus on the complexity of water management and the multifunctional character of water and the search for alternative forms of organisation.

Resource Allocation With Workload-Time Windows for Cloud-Based Software Services: A Deep Reinforcement Learning Approach

As the workloads and service requests in cloud computing environments change constantly, cloud-based software services need to adaptively allocate resources for ensuring the Quality-of-Service (QoS) while reducing resource costs. However, it is very challenging to achieve adaptive resource allocation for cloud-based software services with complex and variable system states. Most of the existing methods only consider the current condition of workloads, and thus cannot well adapt to real-world cloud environments subject to fluctuating workloads. To address this challenge, we propose a novel Deep Reinforcement learning based resource Allocation method with workload-time Windows (DRAW) for cloud-based software services that considers both the current and future workloads in the resource allocation process. Specifically, an original Deep Q-Network (DQN) based prediction model of management operations is trained based on workload-time windows, which can be used to predict appropriate management operations under different system states. Next, a new feedback-control mechanism is designed to construct the objective resource allocation plan under the current system state through iterative execution of management operations. Extensive simulation results demonstrate that the prediction accuracy of management operations generated by the proposed DRAW method can reach 90.69%. Moreover, the DRAW can achieve the optimal/near-optimal performance and outperform other classic methods by 3~13% under different scenarios.

Solving multi-task manufacturing cloud service allocation problems via bee colony optimizer with transfer learning

Industrial internet platform is regarded as an emerging infrastructure to increase the manufacturing efficiency via sharing resources located in multiple sites. Manufacturing cloud service allocation (MCSA) aims to assign available services to the interconnected subtasks of a complicated task such that some performance indices are optimized. Current studies on MCSA are single-task-oriented and fail to exploit the shared task-solving experiences to jointly optimize a group of tasks with enhanced solution quality and search speed. This work considers the joint optimization of multiple MCSA problems in a parallel fashion via cross-task transfer learning mechanism, and two novel transfer learning strategies are embedded into the framework of bee colony algorithm to make the best use of cross-task helpful knowledge when resolving multi-task MCSA. The first one is to design an individual-dependent transfer learning mechanism to govern the probability of whether a bee to perform intra-task self-evolution or cross-task knowledge transfer, which adaptively regulates the search behavior of each bee according to its state. The second one is to select the potential bees from foreign tasks for knowledge exchange with the aid of anomaly detection mechanism. The proposed optimizer is extensively examined on different scales of MCSA instances in multi-task scenario. Experimental results confirm the performance advantage of our proposal in comparison with other state-of-the-art peers.

Identification of Emerging Multidrug-Resistant Neisseria gonorrhoeae Isolates against Five Major Antimicrobial Agent Options

Antimicrobial drug resistance in Neisseria gonorrhoeae has been documented all over the world. However, the situation in Sub-Saharan Africa has received little attention. It is critical to establish diagnostics and extend surveillance in order to prevent the emergence of illnesses that are resistant to several treatments. Monitoring antimicrobial susceptibility is critically required in order to gather data that may be utilised to produce treatment recommendations that will result in effective therapy, a decrease in gonorrhoeae-related difficulties and transmission, and effective therapy. Government authorities may set research and preventive objectives, as well as treatment recommendations, using data from the Gonococcal Antimicrobial Surveillance Program (GISP). Local and state health authorities may use GISP data to make choices about the allocation of STI prevention services and resources, to guide preventative planning, and to disseminate information about the most successful treatment practices. Using molecular and culture approaches, we investigated the occurrence of antibiotic resistance in isolates from KwaZulu Natal, South Africa. The great majority of gonococcal isolates (48% showed absolute resistance to ciprofloxacin), with penicillin and tetracycline resistance rates of 14% each. Only one of the gonococcal isolates tested positive for azithromycin resistance, with a minimum inhibitory concentration (MIC) of 1.5 µg/mL. Ceftriaxone was effective against all gonococcal isolates tested.

Cloud-based 3D printing service allocation models for mass customization

Cloud-based 3D printing service allocation models for mass customization