Billing Model Research Articles

I-MPaFS: enhancing EDoS attack detection in cloud computing through a data-driven approach

Cloud computing offers cost-effective IT solutions but is susceptible to security threats, particularly the Economic Denial of Sustainability (EDoS) attack. EDoS exploits cloud elasticity and the pay-per-use billing model, forcing users to incur unnecessary costs. This research introduces the Integrated Model Prediction and Feature Selection (I-MPaFS) framework to address EDoS attacks. I-MPaFS framework enhances an existing dataset to improve performance, using the generated data to build a Random Forest model for EDoS detection. Our investigation employs the UNSW-NB15, CSE-CIC-IDS18 and NSL-KDD datasets, demonstrating the proposed method’s superiority over existing techniques. The model achieved recall scores of 99.45% on the UNSW-NB15 dataset, 98.19% on the CSE-CIC-IDS18 dataset, and 99.82% on the NSL-KDD dataset, highlighting its reliability and efficacy in safeguarding cloud users from financial exploitation. This study contributes to the field by evaluating current EDoS detection methods, introducing the I-MPaFS framework, validating its performance with benchmark datasets, and comparing its effectiveness against state-of-the-art techniques. The findings affirm the significant potential of I-MPaFS in enhancing cloud security and protecting users from EDoS attacks.

Expanding the Availability of Scalp Cooling to All Patients at Risk of Chemotherapy-Induced Alopecia.

Alopecia is an undesirable side effect of cancer chemotherapy. The mitigation of alopecia is a desirable adjunct treatment for patients with cancer. FDA-cleared scalp cooling (SC) devices have been successfully used to prevent or reduce chemotherapy-induced alopecia (CIA). This paper provides an understanding of the implementation and value of the new Insurance-Based Billing Model used in the USA for SC and its benefits compared with the original self-pay model. This improved compensation change will result in all patients in need, including underserved and disadvantaged populations, receiving equitable healthcare by allowing access to this valuable supportive care technology.

Impact and intervention of postoperative complications after gastric cancer surgery from a health economics perspective

Gastric cancer ranks as the third most prevalent malignant tumor in our nation, imposing a substantial health and economic burden. The occurrence of postoperative complications in gastric cancer not only hinders patient recovery but also significantly increases the medical expenditures of patients, contributing supplementary health economic challenges to both society and healthcare institutions. Conducting health economic analysis on postoperative complications in gastric cancer provides evidence for the formulation of health policies, offers guidance for hospital cost control, and furnishes economic insights for the development of new technologies in the prevention and treatment of complications. This paper, through a thorough review of domestic and international literature, comprehensively examines the impact of complication severity on healthcare expenses, delineates the principal contributors to healthcare costs in patients with postoperative complications, and proposes practical strategies to alleviate the health economic burden resulting from such complications. Furthermore, this study delves into and analyzes the health economic considerations associated with postoperative complications within the framework of the Diagnosis Related Groups (DRG) billing model.

Analysis of the Economic Soundness and Viability of Migrating from Net Billing to Net Metering Using Energy Cooperatives

In the European Union, increasing attention is already being paid not only to the development of renewable energy sources, but also to the establishment of solutions to achieve local energy self-sufficiency while increasing the role of citizens in managing the energy they generate. This approach is expected both to have a positive impact on the environment and the reduction of greenhouse gas emissions, and to enhance energy security—both in economic and civic terms by, i.a., combating energy poverty. The development of local energy communities promoted in the EU is supported i.a. by energy cooperatives. These contribute to the efficient harnessing of renewable energy potential in rural and urban-rural areas, and have been developing in Poland for several years now. In their previous studies, the authors of this research paper attempted to verify the generation (number, type and capacity of installed sources) and consumption (energy demand) configurations in which an energy cooperative would be a viable solution for prosumers who might establish it. However, over the past few years, the conditions for prosumers and the method of their accounting with the electricity seller have changed radically in Poland (shift from net metering to net billing). This situation has opened up space for further research and encouraged the authors to revisit the problem of analyzing the viability of establishing energy cooperatives in relation to the rules of operation of individual prosumers. This research was carried out for three scenarios, and the horizon of the analyses conducted and described extends to 2045. The comparative analysis included energy consumers without their own generation sources, prosumers with a photovoltaic generation installation covered by the net billing model, as well as a scenario involving prosumers’ cooperation within an energy cooperative, which by law is settled in the net metering model. Conclusions from the research and simulations made it possible to confirm the claim that, despite changes in the rules of prosumer billing, developing energy independence in the energy community formula results in a significant reduction in the cost of purchasing electricity (even several times lower purchase costs in the timeframe analyzed) and can lead to a reduction in the payback time of investments in generation sources even by a factor of two. The results presented in this research paper open up space for further research. The outcomes allow us to assume that energy cooperatives—in the organizational and institutional model in Poland—are a good tool for reducing the phenomenon of energy poverty on a local scale.

Implementation of Billing System at Electrical Vehicle Charging Stations in Elektroprivreda BiH

Abstract Electromobility is strongly encouraged in the European Union. Elektroprivreda of Bosnia and Herzegovina (EP BiH) is the only company in BiH that uses Charge Point Management Software. To ensure a functional process of calculation and billing of charging consumption, it is necessary to implement appropriate software solutions. This paper provides a comparative analysis of possible models of ad-hoc charging at electric vehicle charging stations. As a result of a comparative analysis of the advantages and disadvantages of each of the observed variants, a more detailed description of one selected model was given, which, due to its characteristics, as well as readiness for quick implementation in existing environment of EP BiH, proved to be the most adequate solution for business requirements of the company. The paper describes the economic effects of the introduction of the selected billing model, as well as the tariff model for charging.

Estimated energy consumption and billing modelling using power availability recorder

Accurate estimation and billing of unmetered facilities and homes by the energy providers in Nigeria has been a serious nightmare to the rural and other consumers. The unjust billing due to inaccurate and wrong estimations have resulted to rancor, facilities damage and economic hardship among the people due to poor equipment and unreliable consumption data. This research work presents accurate energy consumption and billing model using power availability recorder (PAR) which keeps track of how long electricity lasts in a building or facility at discrete time scale. Two residential buildings situated at the University of Nigeria, Nsukka, and one facility each in two chosen communities in Nsukka, were used as commercial and residential electricity consumers respectively for this study. The readings from the PAR were obtained in discrete scale (mini-seconds), and were later converted to seconds and hours. Similarly, the readings from the Energy Consumption meter were taken in kWh. These daily results taken simultaneously at 6 am and 6 pm were used to calculate the Availability Factor (AF), Load Use Factor (LUF) and Average Load (AL) in kW. With PAR as energy meter in significant number of houses within the chosen communities, AF, LUF, and AL for different facilities in the community were obtained and used for the energy billing model development. A relationship, which depends on availability and load use factor, has been suggested in this study with a proportionality constant value of 14.78 kWh/day/customer for use in obtaining better estimates of the bills chargeable to unmetered customers. This will control the black-out relay period usually encountered by consumers instead of the grossly unjust estimated bills currently being used by Nigeria's electricity distribution companies.

MLLess: Achieving cost efficiency in serverless machine learning training

Function-as-a-Service (FaaS) has raised a growing interest in how to “tame” serverless computing to enable domain-specific use cases such as data-intensive applications and machine learning (ML), to name a few. Recently, several systems have been implemented for training ML models. Certainly, these research articles are significant steps in the correct direction. However, they do not completely answer the nagging question of when serverless ML training can be more cost-effective compared to traditional “serverful” computing. To help in this endeavor, we propose MLLess, a FaaS-based ML training prototype built atop IBM Cloud Functions. To boost cost-efficiency, MLLess implements two innovative optimizations tailored to the traits of serverless computing: on one hand, a significance filter, to make indirect communication more effective, and on the other hand, a scale-in auto-tuner, to reduce cost by benefiting from the FaaS sub-second billing model (often per 100 ms). Our results certify that MLLess can be 15X faster than serverful ML systems [27] at a lower cost for sparse ML models that exhibit fast convergence such as sparse logistic regression and matrix factorization. Furthermore, our results show that MLLess can easily scale out to increasingly large fleets of serverless workers.

What is Cloud computing Benfits and challenges of Cloud

Cloud computing provides on-demand delivery of computer resources ranging from, compute, data storage, networking, software, and other IT services over the internet in exchange for pay as you go billing model. The study discusses various types of cloud computing available, cloud computing advantages, cloud computing implementation models, cost-effective IT solution, IT service agility, challenges, and features of cloud computing in this growing digital world.
 Keywords: cloud, data storage, compute, digital, on-demand billing model.

Time Controlled Expressive Predicate Query With Accountable Anonymity

Many existing searchable encryption schemes are inflexible in retrieval patterns. The data usage authorization is almost permanent valid as long as the user is not revoked. This “all-or-nothing” authorization mode is not compatible with the “pay-as-you-use” commercial billing model. In this article, we propose a new notion called time controlled expressive predicate query with accountable anonymity. It realizes time controlled data query, where a time server issues time token to authorize search privilege in designated time period. The data users can anonymously query on encrypted data and the anonymity is accountable in a way that the trusted authority is able to deanonymize data users if they misbehave in the system. The underlying techniques are anonymous credential, Pederson commitment and non-interactive zero-knowledge proof. We firstly design an efficient expressive predicate query (EPQ) scheme, which is proved secure to protect the privacy of expressive search predicate. Based on EPQ, we present a concrete system instantiation, which realizes key-escrow free and time token nontransferability. The formal definition and security models are given out. The system is formally proved indistinguishable against chosen keyword-set attacks, unforgeable of time tokens and accountable of anonymous users. The comparison and experiment results demonstrate its scalability and efficiency.

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.

Online Lending Business And Its Criminal Aspect Of Collectibility

Online lending business opportunities or what is often referred to as fintech applications grow very fertile in today's digital era. On the one hand, it can be assumed as the growth of the economy, but on the other hand, the collectibility side for these business actors reflects a concerning reality. There are events where they are actually disturbing when collecting using harsh methods, with words that tend to be threatening and inhumane. This is an unlawful reality. This paper will discuss the criminal aspects of these activities from the perspective of the Electronic Information and Transaction Law. This study is conducted using a descriptive method with a normative approach. The data sources analyzed are secondary data, which are then interpreted qualitatively. The results of the study show how the billing model can be seen as an unlawful act in Article 27 Paragraph (1) of the Electronic Information and Transactions Law. Thus, the individual collectors of the online loan company can be held criminally liable.

IMPACT OF COVID 19 PANDEMIC ON THE PERFORMANCE OF MEDICAL AID SOCIETIES IN ZIMBABWE

This research study was inspirational to know and understand the impact of COCOVID-19andemic on the performance of Medical Aid Societies in Zimbabwe. The objectives of the study were to determine the impact of COVID-19 on membership uptake, examine the impact of COVID-19 on the profitability of medical aid and assess the impact of COVID-19 on claims management. A literature review provided the foundation and knowledge, understanding and scholarly debates of the impact of COVID-19 medical aid societies. The study adopted the quantitative research methodology and constructed a questionnaire and conducted interviews distributing them to 80 correspondents. Data collected was analysed using IBM SPSS statistics version 20 and the findings of the data were represented on tables. The hypothesis of the study H0 was accepted as COVID19 had an impact on the membership uptake, profitability, and claims of medical AID Schemes in Zimbabwe. The sample size used was 80. Recommendations on how to manage medical aid societies’ performance in a crisis is according to the findings of the study are proposed. The recommendations include engaged membership, blockchain technology, and hybrid billing model. The study proposed that future researchers can compare the use of telehealth services in other countries during pandemics of this nature and its role in Zimbabwean Medical Aid societies for the sustenance of their performance.

Accurate metering and billing of ambient loop systems

• Accurate billing of ambient loop systems requires complex metering and billing strategy. • Tenants exchanging energy via an ambient loop offers potential energy savings. • Transparency of energy usage gives tenants control to reduce energy consumption. In order to reduce the operational energy consumption in the heating and cooling of buildings, the building industry is exploring methods of combining district heating and cooling networks where tenants’ premises are equipped with individual water source heat pumps. This has the potential to create an ambient loop where tenants can exchange heat with each other, and a main source of heating or cooling can top up the energy demand of the loop. A challenge that arises in such a system is how the landlord can accurately bill tenants when energy is shared between tenants via the landlord's services loop. This study takes a theoretical approach on one potential solution where the billing model is developed to use data from energy meters and electricity meters, analyze the operational modes of the components in the system and proportion the energy usage accordingly.

Network Services Anomalies in NFV: Survey, Taxonomy, and Verification Methods

Network Function Virtualization (NFV) has emerged as a disruptive networking architecture whose galloping evolution is prompting enterprises to outsource network functions to the cloud and ultimately harvest the fruits of cloud computing, including elasticity, pay-as-you-go billing model, and on-demand services provisioning. However, many reluctant enterprises oppose the benefits of this outsourcing to their critical and pressing concerns about security, trust, and compliance. The latter anticipate possible security and QoS policy violations stemming from dishonest behaviors by cloud providers, attacks by co-resident competitors, misconfiguration by cloud administrators, or implementations flaws by NFV developers. As a result, migrating sensitive workloads to the cloud requires enterprises to first assess risks by gaining knowledge of possible network services’ anomalies and second, to build trust in the cloud by designing effective mechanisms to detect such anomalies. This survey provides scrutiny of network services anomalies that may occur in the NFV environments. We first present a taxonomy of network service anomalies and analyze their negative impacts on critical service attributes, including security and performance. Second, we compare and classify the existing anomalies’ verification mechanisms from the literature. Finally, we point out the literature gap and identify future research directions for anomalies verification in NFV.

Student Data Privacy and Automatic Textbook Billing

The textbook market in U.S. higher education is changing. In recent years, publishers have developed an automatic billing model, in which colleges and universities negotiate deals with publishers to provide ebooks and courseware to students, folding the cost into student fees. This model is commonly known as "inclusive access." Because it offers students first-day access to course materials - important to student success - as well as some savings over full-priced standard textbooks, it is becoming popular with faculty and administrators. But textbook publishers are promoting these plans for another reason: The data they can collect with digital materials opens a lucrative new market, allowing them to diversify into analytics services.

Optimality and PAR Reduction in Autonomous Demand Response: Evaluation and Billing Mechanisms

The main objective of this paper is to propose a more efficient, distributed, and multi-objective billing model that can be implemented in every smart meter in the grid for achieving optimality and fairness. First, we develop a new evaluation index to evaluate such as billing model not only in addressing fairness but also to minimize the cost and reduce the Peak-to-Average Ratio (PAR) in the load demand and subsequently the bill of each customer. Then, we study some of the billing models that exist in the literature and evaluate them with our evaluation index. Simulations are performed to test the performance of our model in terms of optimality, PAR reduction and fairness.

SERVERLESS COMPUTATIONS RESOURCE SCHEDULING BASED ON DATA DEPENDENCY

The serverless computing model is becoming quite widespread. This model allows developers to create flexible and fault tolerant applications with an attractive billing model. The increasing complexity of serverless functions has led to the necessity to use serverless workflows – serverless functions invoking other serverless functions. However, such concept imposes certain requirements on the serverless functions that make distributed computations. The overhead of transferring data between serverless functions can significantly increase the execution time of a program using this approach. One way to reduce overhead is to improve serverless scheduling techniques. This paper discusses an approach to scheduling serverless computations based on data dependency analysis. We propose to divide the problem of planning of the computation of a composite serverless function into three stages. For each stage we provide a description by a mathematical model. We carried out a review of algorithms used to schedule resources by compilers and in parallel computing in multiprocessor systems to determine the best algorithm to implement in a prototype scheduler. For each algorithm, it was specified how it could be used for resource scheduling in serverless platforms. We provide a description of the developed prototype based on the Fission serverless platform. The prototype implements the critical path heuristic. It is shown that the improvements can significantly reduce the execution time up to two times for some types of serverless functions.

Implementation of a pharmacist-provided pharmacogenomics service in an executive health program.

We describe the implementation of a pharmacist-provided pharmacogenomics (PGx) service in an executive health program (EHP) at an academic medical center. As interest in genomic testing grows, pharmacists have the opportunity to advance the use of PGx in EHPs, in collaboration with other healthcare professionals. In November 2018, a pharmacist-provided PGx service was established in the EHP at the University of Colorado Hospital. The team members included 3 physicians, a pharmacist trained in PGx, a registered dietitian/exercise physiologist, a nurse, and 2 medical assistants. We conducted 4 preimplementation steps: (1) assessment of the patient population, (2) selection of a PGx test, (3) establishment of a visit structure, and (4) selection of a billing model. The PGx consultations involved two 1-hour visits. The first visit encompassed pretest PGx education, review of the patient's current medications and previous medication intolerances, and DNA sample collection for genotyping. After this visit, the pharmacist developed a therapeutic plan based on the PGx test results, discussed the results and plan with the physician, and created a personalized PGx report. At the second visit, the pharmacist reviewed the PGx test results, personalized the PGx report, and discussed the PGx-guided therapeutic plan with the patient. Overall, the strategy worked well; minor challenges included evaluation of gene-drug pairs with limited PGx evidence, communication of information to non-EHP providers, scheduling issues, and reimbursement. The addition of a PGx service within an EHP was feasible and provided pharmacists the opportunity to lead PGx efforts and collaborate with physicians to expand the precision medicine footprint at an academic medical center.

Reimbursement outcomes of a pharmacist‐physician co‐visit model in a Federally Qualified Health Center

AbstractBackgroundPharmacists have demonstrated a positive impact in providing patient care in ambulatory settings. Pharmacists have faced multiple barriers to reimbursement, especially in a Federally Qualified Health Center (FQHC) setting. A commonly cited barrier is lack of recognition as providers under Medicare.ObjectivesThe objective of this study was to describe the reimbursement potential, physician appointment capacity, and billing levels with the use of a pharmacist‐physician co‐visit model in an urban FQHC.MethodsThis was a retrospective, observational cohort study. Patient visits were included in the co‐visit model cohort if they were completed with the pharmacist and/or collaborating physician on the same clinic half‐day from May‐September 2019. Comparator group patient visits were included in the physician‐only cohort if they were with one of five physicians practicing at the FQHC on a day without the pharmacist. Patient visits were excluded if the visit was nonbillable.ResultsRealized reimbursement per half‐day in clinic [mean (SD)], was significantly less with the physician‐only visits compared to the co‐visit billing model [$558 (242) vs $850 (410), P = .001]. The addition of a pharmacist through the co‐visit billing model increased the median (IQR) billing per half‐day in clinic from $1613 (1173, 1788) to $2108 (1832,2620) (P < .001), and the median (IQR) number of patients seen per half‐day from 9 (7, 10) to 12 (10, 14) (P < .001). Unpaid patient bills ($39 599) were excluded from reimbursement analysis in both groups.ConclusionsThe pharmacist‐physician co‐visit model in an FQHC increased revenue to justify the addition of one full‐time equivalent position within our facility, increased the number of patients seen by the physician, allowed for higher‐complexity billing, and provided physicians with variety in responsibilities. The benefits of team‐based care extend beyond the financial implications of a co‐visit model. Without direct pharmacist‐billing mechanisms, this model justifies the expansion of pharmacist‐managed services.

Modeling and Optimization of Performance and Cost of Serverless Applications

Function-as-a-Service (FaaS) and serverless applications have proliferated significantly in recent years because of their high scalability, ease of resource management, and pay-as-you-go pricing model. However, cloud users are facing practical problems when they migrate their applications to the serverless pattern, which are the lack of analytical performance and billing model and the trade-off between limited budget and the desired quality of service of serverless applications. In this article, we fill this gap by proposing and answering two research questions regarding the prediction and optimization of performance and cost of serverless applications. We propose a new construct to formally define a serverless application workflow, and then implement analytical models to predict the average end-to-end response time and the cost of the workflow. Consequently, we propose a heuristic algorithm named Probability Refined Critical Path Greedy algorithm (PRCP) with four greedy strategies to answer two fundamental optimization questions regarding the performance and the cost. We extensively evaluate the proposed models by conducting experimentation on AWS Lambda and Step Functions. Our analytical models can predict the performance and cost of serverless applications with more than 98 percent accuracy. The PRCP algorithms can achieve the optimal configurations of serverless applications with 97 percent accuracy on average.