Provider's Revenue Research Articles

A Novel Clinching Auction Mechanism for Edge Computing Resource Allocation With Budget Limits

ABSTRACTAn auction mechanism is an effective resource allocation method that can increase the revenue of resource providers in the field of edge computing. Existing auction mechanism designs mostly aim to maximize social welfare when allocating resources, but these schemes lead to low revenue. In contrast, clinching auctions have achieved good results in spectrum allocation and advertising due to their high revenue. Therefore, a clinching auction mechanism is a promising tool for allocating edge computing resources. However, clinching auctions have the drawback that they can only allocate homogeneous finitely divisible goods, meaning that they cannot be directly applied for resource allocation in edge computing. This article presents two new auction mechanisms that improve on the clinching auction. Specifically, based on the principle of increasing global prices and local competition, two mechanisms are designed, one from the perspective of resource providers (MDCAM‐ECS) and the other from the perspective of users (MDCAM‐User), to solve the problem of edge computing resource allocation and pricing with deployment constraints and user budget constraints. The mechanisms proposed in this article have the properties of individual rationality, truthfulness, and computational efficiency. In the experiments, in terms of social welfare and revenue, our algorithms can achieve a 20% improvement over existing algorithms, such as fixed‐price, Vickery–Clarke–Groves (VCG), and monotonic critical‐price mechanisms. Additionally, in most experiments, our algorithm can ensure resource utilization greater than 80%.

A Cooperative Game Approach for Optimal Design of Shared Energy Storage System

The energy sector’s long-term sustainability increasingly relies on widespread renewable energy generation. Shared energy storage embodies sharing economy principles within the storage industry. This approach allows storage facilities to monetize unused capacity by offering it to users, generating additional revenue for providers, and supporting renewable energy prosumers’ growth. However, high investment costs and long payback periods often hinder the development of battery storage. To address this challenge, we propose a shared storage investment framework. In this framework, a storage investor virtualizes physical storage equipment, enabling prosumers to access storage services as though they owned the batteries themselves. We adopt a cooperative game approach to incorporate storage sharing into the design phase of energy systems. To ensure a fair distribution of cooperative benefits, we introduce a benefit allocation mechanism based on contributions to energy storage sharing. Utilizing realistic data from three buildings, our simulations demonstrate that the shared storage mechanism creates a win–win situation for all participants. It also enhances the self-sufficiency and self-consumption of renewable energy. This paper provides valuable insights for shared storage investors regarding optimal design and benefit allocation among multiple stakeholders.

A proximal policy optimization approach for food delivery problem with reassignment due to order cancellation

Unexpected cancellation of food delivery orders poses significant challenges to resource allocation planning and could lead to reduced revenue for service providers. This paper addresses this issue by developing an optimization framework that can reassign canceled orders to alternative customers to maximize net revenue and minimize resource wastage. The problem is formulated as a route-based Markov decision process, named the Dynamic Routing and Pricing Problem with Cancellation (DRPPC). A solution approach based on the proximal policy optimization strategy is introduced as a computationally effective way of solving the optimization problem using reinforcement learning techniques. Experimental results demonstrate that the proposed computational method outperforms selected benchmark approaches with higher revenue from various kinds of scenarios with uncertainties. This research advances the intersection of urban logistics and reinforcement learning, offering actionable strategies for enhanced operational resilience in food delivery service providers.

Multi-objective task offloading for highly dynamic heterogeneous Vehicular Edge Computing: An efficient reinforcement learning approach

Vehicular Edge Computing (VEC) provides a flexible distributed computing paradigm for offloading computations to the vehicular network, which can effectively solve the problem of limited vehicle computing resources and meet the on-vehicle computing requests of users. However, the conflict of interest between vehicle users and service providers leads to the need to consider a variety of conflict optimization goals for computing offloading, and the dynamic nature of vehicle networks, such as vehicle mobility and time-varying network conditions, make the offloading effectiveness of vehicle computing requests and the adaptability to complex VEC scenarios challenging. To address these challenges, this paper proposes a multi-objective optimization model suitable for computational offloading of dynamic heterogeneous VEC networks. By formulating the dynamic multi-objective computational offloading problem as a multi-objective Markov Decision Process (MOMDP), this paper designs a novel multi-objective reinforcement learning algorithm EMOTO, which aims to minimize the average task execution delay and average vehicle energy consumption, and maximize the revenue of service providers. In this paper, a preference priority sampling module is proposed, and a model-augmented environment estimator is introduced to learn the environmental model for multi-objective optimization, so as to solve the problem that the agent is difficult to learn steadily caused by the highly dynamic change of VEC environment, thus to effectively realize the joint optimization of multiple objectives and improve the decision-making accuracy and efficiency of the algorithm. Experiments show that EMOTO has superior performance on multiple optimization objectives compared with advanced multi-objective reinforcement learning algorithms. In addition, the algorithm shows robustness when applied to different environmental settings and better adapting to highly dynamic environments, and balancing the conflict of interest between vehicle users and service providers.

The role of Video on Demand in stimulating broadband adoption

In this paper we study the role of Video on Demand services (VOD) as drivers of broadband adoption. While the research literature has highlighted the importance of digital content, such as applications and Internet platforms, as a driver of broadband adoption, no studies have yet explored the specific contribution of VOD. We developed a worldwide database of VOD services launch by country since 2012 (including Netflix, Amazon Prime, Apple TV+, Disney+, Paramount+, and HBO Max) and explored their contribution as contributors to broadband adoption during the period. To do so, we relied on several empirical specifications including instrumental variables to control for reverse causality between broadband adoption and VOD services availability. Our results confirm that VOD services have been a key contributor to the increase of broadband connectivity, also helping to narrow down the digital divide especially in developing nations. In addition, VOD service offers have been associated with the gradual increase in the purchasing of broadband higher speed plans. Finally, there is a significant and positive increase in consumer surplus associated with the launch of VOD offers. This evidence points to the impact of VOD services on the digital economy as a stimulus for consumer acquisition of broadband services and therefore as a contributor to the revenues of broadband service providers.

Resource sharing for collaborative edge learning: A privacy-aware incentive mechanism combined with demand prediction

Edge intelligence benefits from ubiquitous resource capabilities by numerous edge devices. However, interest concerns and privacy issues caused by the openness of the edge network make smart devices reluctant to participate in collaborative edge learning. This work proposes a privacy-aware incentive mechanism based on combinatorial double auction (PIMCDA) to facilitate idle resource sharing of diversified edge devices. First, we establish an auction model based on blockchain to form a trusted sharing market in a multi-resource binding manner. Then, we propose a two-stage auction solution combined with differential privacy: The privacy-aware winner selection stage matches the optimal winner candidate to the task that is the closest in probability to maximize the revenue of resource providers with the obfuscated location information. The probabilistic pricing decision stage is designed based on a uniform pricing method and exponential mechanism, which can ensure bid privacy from inference attacks. Furthermore, to ensure the effectiveness of PIMCDA for long-term participation, we design a supply and demand balance mechanism with a learning-based resource prediction method. Theoretical and simulation analysis demonstrate that the proposed mechanism achieves privacy-preserving in location and bid information while ensuring effective incentive properties. Our approach effectively motivates the sharing of multiple resources in edge computing.

Older adult consumers and local competition in the healthcare service

AbstractPopulation aging is a global phenomenon observed in many developed economies. On the one hand, this trend suggests significant opportunities for healthcare providers due to increasing service demand. On the other hand, it also implies that the industry requires new managerial knowledge to accommodate older adult consumers. In this research, we propose and examine the impact of older adult consumers on healthcare provider performance and identify boundary conditions related to two types of competition: intra‐ and inter‐competition. Using longitudinal data on healthcare provider revenues, we find that an increase in the older adult population increases the revenues of generalists but decreases those of specialists. Second, the positive effect of elderly consumers on generalists is amplified when intra‐competition (from same‐type physicians) increases and diminishes when inter‐competition (from different‐type physicians) intensifies. Third, for specialist healthcare providers, the negative impact of older adult population is mitigated by intra‐competition but exacerbated when inter‐competition becomes strong. Our results offer theoretical implications for the healthcare service marketing literature and managerial insights into decisions related to healthcare service locations. Specifically, our findings suggest that healthcare providers need to carefully consider the proportion of older adult consumers, the presence of local competitors, and their own specialties, as these factors all have a meaningful impact on performance.

Job-Deadline-Guarantee-Based Joint Flow Scheduling and Routing Scheme in Data Center Networks.

Many emerging Internet of Things (IoT) applications deployed on cloud platforms have strict latency requirements or deadline constraints, and thus meeting the deadlines is crucial to ensure the quality of service for users and the revenue for service providers in these delay-stringent IoT applications. Efficient flow scheduling in data center networks (DCNs) plays a major role in reducing the execution time of jobs and has garnered significant attention in recent years. However, only few studies have attempted to combine job-level flow scheduling and routing to guarantee meeting the deadlines of multi-stage jobs. In this paper, an efficient heuristic joint flow scheduling and routing (JFSR) scheme is proposed. First, targeting maximizing the number of jobs for which the deadlines have been met, we formulate the joint flow scheduling and routing optimization problem for multiple multi-stage jobs. Second, due to its mathematical intractability, this problem is decomposed into two sub-problems: inter-coflow scheduling and intra-coflow scheduling. In the first sub-problem, coflows from different jobs are scheduled according to their relative remaining times; in the second sub-problem, an iterative coflow scheduling and routing (ICSR) algorithm is designed to alternately optimize the routing path and bandwidth allocation for each scheduled coflow. Finally, simulation results demonstrate that the proposed JFSR scheme can significantly increase the number of jobs for which the deadlines have been met in DCNs.

Growing administrative complexity in the 340B program and the rise of third-party administrators.

The 1992 340B drug discount program lowers drug costs and provides a source of revenue for safety-net providers by entitling them to discounts on drugs for all patients, including those with insurance. As 340B has grown so has the administrative complexity of the program, creating a market for third-party administrators (TPAs) to manage both formal and informal requirements in exchange for fees. Although TPAs are playing an important role in the 340B ecosystem, the TPA market is poorly understood, along several dimensions including its size, specialization, competitiveness, and scope of services offered. In this brief report, we report the results of original market research on TPA firms to address unanswered questions and identify gaps in knowledge. We identify 48 TPA firms, including 8 owned by private equity firms, 2 owned by software companies, and 6 owned by a variety of vertically integrated health care stakeholders such as pharmacies, pharmaceutical benefit managers, and pharmaceutical wholesalers. We identified 7 services offered by TPAs including patient discount cards (13), enrollment (31), auditing (45), revenue capture (42), referral management (9), inventory management (19), and 340B ESP assistance (9). Our results suggest that TPAs are involved in many diverse functions related to 340B.

Cleaning up the stack: Evaluating a clean cooking fuel stacking intervention in urban Kenya

There are a growing number of market-based providers of clean cooking solutions in sub-Saharan Africa that rely on use customer fuel sales to subsidise upfront cost of equipment. These business models can widen access to clean cooking but are undermined by the continued use of polluting fuels, known as “fuel stacking”, which limits provider revenues whilst perpetuating the negative impacts of cooking with traditional fuels.This study aimed to design and test a fuel stacking intervention with commercial pay-as-you-go LPG customers in Kisumu, Kenya. It consisted of three main phases: developing and testing a survey tool for diagnosing drivers of stacking (n = 99); using an intervention design framework (the Behaviour Change Wheel) to design an intervention consisting of the bundled provision of a pressure cooker, chapati pan and training; and testing the intervention in a small (n = 19) pre-post study.There was high uptake of the intervention, with the pans being used by all participants on roughly two-fifths of days. Target foods were cooked more frequently on LPG in the intervention phase, resulting in a significant increase in PAYG LPG use and an insignificant decrease in charcoal use. A third of participants stopped cooking with charcoal altogether, but some residual charcoal usage continued amongst the remainder.The results show that targeted stacking interventions can simultaneously promote sustained use of clean fuels and dis-adoption of polluting ones, resulting in commercial gains for clean cooking fuel providers. This could address the wicked problem of fuel stacking and accelerate progress towards Sustainable Development Goal 7.

Economics of Semantic Communication System: An Auction Approach

Semantic communication technologies enable wireless edge devices to communicate effectively by transmitting semantic meanings of data. Edge components, such as vehicles in next-generation intelligent transport systems, use well-trained semantic models to encode and decode semantic information extracted from raw and sensor data. However, the limitation in computing resources makes it difficult to support the training process of accurate semantic models on edge devices. As such, edge devices can buy the pretrained semantic models from semantic model providers, which is called “semantic model trading”. Upon collecting semantic information with the semantic models, the edge devices can then sell the extracted semantic information, e.g., information about urban road conditions or traffic signs, to the interested buyers for profit, which is called “semantic information trading”. To facilitate both types of the trades, effective incentive mechanisms should be designed. Thus, in this paper, we propose a hierarchical trading system to support both semantic model trading and semantic information trading jointly. The proposed incentive mechanism helps to maximize the revenue of semantic model providers in the semantic model trading, and effectively incentivizes model providers to participate in the development of semantic communication systems. For semantic information trading, our designed auction approach can support the trading between multiple semantic information sellers and buyers, while ensuring individual rationality, incentive compatibility, and budget balance, and moreover, allowing them to achieve higher utilities than the baseline method.

Evolutionary Game Analysis of Risk in Third-Party Environmental Governance

Focusing on the moral hazard of third-party environmental service providers in monitoring and controlling the emission of pollutants by enterprises, this paper takes the third-party governance of environmental pollution under the incentive-and-constraint mechanism as its research object. It also constructs a game model involving emission-producing enterprises producing emissions, third-party environmental service providers, and local governments. Adopting this evolutionary game model, this paper analyzes the mechanism of local government’s role in effectively resolving the moral hazard between emission-producing enterprises producing emissions and third-party environmental service providers by exploring the conditions of spontaneous cooperation between emission-producing enterprises producing emissions and third-party environmental service providers. This paper provides a possible solution to the problem of emission-producing enterprises or third-party environmental service providers stealing and leaking emissions, as well as collusion between the two. The study presents two major findings. (1) There are three possible scenarios of breach of contract: unilateral breach by third-party environmental service providers, unilateral breach by emissions-producing enterprises, and collusion between the two. When a third-party environmental service provider unilaterally breaches a contract, emission-producing enterprises have regulatory responsibilities toward them. In such cases, local governments should reduce the penalties imposed on emission-producing enterprises. This measure would decrease the willingness of these enterprises to allocate a higher proportion of collusion payments to third-party environmental service providers. However, it would simultaneously provide a new avenue through which third-party environmental service providers would gain benefits, thereby increasing their expected gains from collusion. This would create a new game between the two parties, leading to the failure of collusion negotiations. (2) The efficacy of incentive-constraint mechanisms is influenced by the severity of contractual breaches, represented by the magnitude of stealing and leaking emissions. When false emissions reduction is at a high level, increasing the incentives for emission-producing enterprises and third-party environmental service providers cannot effectively prevent collusion; when the level is moderate, incentives for third-party environmental service providers can effectively prevent collusion, but incentives for emission-producing enterprises cannot; when the level is low, increasing the incentives for emission-producing enterprises and third-party environmental service providers can help prevent collusion. (3) When emission-producing enterprises engage in unilateral discharge, if a local government’s incentive for third-party environmental service providers exceeds the benefits it can obtain from regulating the discharged amount, third-party environmental service providers tacitly approve the company’s discharge behavior. However, with the strengthening of local government regulations, emission-producing enterprises tend to engage in more clandestine discharging of pollutants to obtain greater rewards. This practice infringes upon the revenue of third-party environmental service providers, as their earnings are positively correlated with the amount of pollution abated. Third-party environmental service providers no longer acquiesce to corporate emissions theft, resulting in an increase in the probability of the detection of emission-producing enterprises’ illicit discharges; in this way, the behavior of these enterprises is regulated.

Evaluation of a Telehealth-Based Pharmacist Led Chronic Care Management Program

Background: Clinical pharmacy services improve several patient chronic disease outcomes. This review evaluates a pharmacist-led chronic care management (CCM) program partnered with a health system for patient outcomes and sustainability. Methods: A mixed methods evaluation based on the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework was completed. Patient A1c and blood pressure readings were retrospectively collected from the electronic health record from August 2018-April 2022. Patients that completed >4 CCM visits with a diagnosis of diabetes and/or hypertension were included. Results: 557 patients enrolled, 53 had uncontrolled systolic blood pressure (SBP), SBP >130 mmHg. Average SBP at baseline was 141.0 mmHg and average SBP at 6 months was 130.2 mmHg, (P < .001). 76 patients had uncontrolled diabetes, A1c > 7%. Average A1c at baseline = 9.1% and average A1c at 6 months = 8.3%, (P < .001). 4464 CCM visits with 247 disease-state targeted patients were completed over 44-month with a 100% adoption rate across clinic locations. Implementation facilitators included patient medication cost concerns, disease burden, provider revenue generation, CCM dedicated software, streamlined call process, and remote EMR access. Implementation barriers included provider discomfort “selling the program,” potential patient costs, unclear need from patient, pharmacists not considered providers, pharmacist cost, multi-platform software, reprioritized stakeholder support, and lack of partner site diversification. Program maintenance showed revenue generation was $5925.31-$8879.89 from August 2021-May 2022 and profitability was $3385.61-$1614.23. Conclusion: This study provides lessons learned, strategies for implementation, and ideas for process efficiencies leading to maintenance of a telehealth pharmacist-led CCM service.

The state of art and review on video streaming

Nowadays, video streaming is very popular around the world, users use video streaming to watch online movies, education, and do office work. Video streaming is referred to as the transmission of video content, live or recorded from server/cloud to end-users. Video and music files are prearranged and transmitted in sequential packets of data so they can be streamed instantaneously, only the User required a high-speed network for access, and a subscription to streaming via an application. In this paper, we survey and analyze the previous development in video streaming such as 2D, and 3D video streaming, compression technologies, protocols for streaming, cloud video processing, 4K/8K, and challenges, and limitations and offer aspects of future development, which will help to provide quality of service of video streaming and increase the revenue for service providers.

Big data and machine learning and the impact on education

In this paper we mention deep learning. Deep learning can be used to improve computing service provider revenue in the mobile blockchain network or to use a machine to study standard mobile detection tasks (e.g., activity recognition). Using deep neural networks can compare results with learning techniques in more common use. Also, we see the deep learning for massive amounts of data and deep learning for a wide variety of data and high-velocity data. Beyond all applications Big Data and Machine learning can be used to improve education, and its practices and procedures.

Problem gambling in Germany: results of a mixed-mode population survey in 2021

ABSTRACT Since 2006, representative telephone surveys among the German general population have not detected any significant changes with respect to gambling-related problems. This is remarkable, as in the meantime not only the availability of gambling services and the turnover and gross gambling revenues of gambling providers have increased, but also the demand for treatment from people with gambling problems has grown. Not only in the gambling field have population surveys of this type recently been associated with certain methodological problems. These include a decreasing willingness to participate among and limited access to specific subgroups. The prevalence study at hand is based on a mixed-mode design which combines both a telephone and an online survey. The weighting of both samples in a ratio of 2 to 1 was checked against plausibility data. The sample consists of 12,303 complete interviews (telephone: 61%, online: 39%). The results show that 2.3% of the German population aged 18–70 years are identified as having a ‘gambling disorder’ according to DSM-5 (mild disorder: 1.1%, moderate disorder: 0.7%, and severe disorder: 0.5%). The prevalence detected in this study is significantly higher than that of previous surveys. Finally, we discuss the results primarily within the context of the new survey methodology.

A QoE-Aware Nonlinear Fuzzy Radio Resource Management Approach for Revenue Enhancement

In this article, the problem of radio resource management is addressed by adjusting the tradeoff between two key quality of experience (QoE) influencing factors, namely transmission rate, and service price. To this end, a piecewise utility function is employed to assess the user QoE in three different quality classes. It comprises the combination of two utility functions corresponding to the transmission rate and the service price. A low-complexity fuzzy-based approach is then introduced to reallocate additional resources to dissatisfied users to increase the number of satisfied users. The simulation results indicate the efficiency and applicability of the novel approach in terms of increasing the service provider revenue while providing approximately the same overall QoE and power consumption. The results also show proposed approach achieves an improvement of at least 15% in total service provider revenue on average compared to other methods.

Revenue Analysis of Travel Agent in Palangka Raya City at the Time of Covid-19 Pandemic

This study aims purpose of this study is to quantify and evaluate how the Covid-19 epidemic has affected the revenue generated by travel-related transportation services in Palangka Raya City. A qualitative descriptive technique is used in the research procedure. This study uses the Community as its analytical unit. Observational methods, interviews, and questionnaires were utilized to collect primary data for this study on travel services in Palangka Raya City for 30 populations who served as samples. Using the program SPSS 25, a non-parametric technique was used to examine the data that had been gathered. The study's findings show that the number of travelers significantly affects the revenue earned by travel service companies. Tariffs have a negative and negligible effect on the revenue of travel service providers. The revenue of travel service providers in Palangka Raya is significantly impacted by operational costs.

Toward resilient cloud warehousing via a blockchain-enabled auction approach

Cloud warehousing service (CWS) has emerged as a promising third-party logistics service paradigm driven by the widespread use of e-commerce. The current CWS billing method is typically based on a fixed rate in a coarse-grained manner. This method cannot reflect the true service value under the fluctuating e-commerce logistics demand and is not conducive to CWS resilience management. Accordingly, a floating mechanism can be considered to introduce more flexible billing. A CWS provider lacks sufficient credibility to implement floating mechanisms because it has vested interests in terms of fictitious demand. To address this concern, this report proposes a blockchain-enabled floating billing management system as an overall solution for CWS providers to enhance the security, credibility, and transparency of CWS. A one-sided Vickrey–Clarke–Groves (O-VCG) auction mechanism model is designed as the underlying floating billing mechanism to reflect the real-time market value of fine-grained CWS resources. A blockchain-based floating billing prototype system is built as an experimental environment. Our results show that the O-VCG mechanism can effectively reflect the real-time market value of CWSs and increase the revenue of CWS providers. When the supply of CWS providers remains unchanged, allocation efficiency increases when demand increases. By analyzing the performance of the O-VCG auction and comparing it with that of the fixed-rate billing model, the proposed mechanism has more advantages. Moreover, our work provides novel managerial insights for CWS market stakeholders in terms of practical applications.

Domain-independent real-time service provisioning in digital platforms: Featuring bundling and customer time-preference

Digital platforms have emerged as an important technology underpinning the new economy. A key problem in such platforms concerns provisioning decisions for customer-service requests, in order to maximize the provider's revenue subject to resource availability. Provisioning is important to meet customer needs, and in turn, for customer retention. The service provisioning problem, in addition to being NP-hard, is further compounded by the fact that the timeframe to make provisioning decisions is short, and more importantly, varies considerably across different platform domains. Digital-platform customers often request bundled services and are sensitive to when tasks get completed. We describe a behavioral-economic model of this problem and develop a domain-independent solution featuring a judicious combination of optimization-backed rule-based approach. Through extensive computational results, we show how our approach has the potential for generalizability and tailorability across different domains in a seamless manner, thus offering the advantages proposed in the software-reusability literature. Implications to platform sustainability, reusable platform solutions and to practical approaches to real-time platforms are also provided.