Resource Utilization Information Research Articles

The Mediating Role of Students' Health Information Literacy Skills: Exploring the Relationship Between Web Resource Utilization and Health Information Evaluation Proficiency.

In an increasingly digital age, students rely heavily on web resources to access health information. However, evaluating the reliability and relevance of such information is crucial for informed decision-making. This study examines the importance of students' health information literacy skills (HILS) as mediators, particularly their ability to utilize web resources and successfully evaluate health information. This research investigates the mediating role of students' HILS in the relationship between their utilization of web resources and their proficiency in evaluating health information. An online survey was distributed to current students at a public university in Bangladesh as part of the data collection process for this study. Using Google Forms, the authors created a structured questionnaire. The survey was distributed through Messenger groups, Facebook pages and email invitations to reach the target audience effectively. The researchers thoroughly analysed the gathered data using structural equation modelling (SEM) techniques and SmartPLS-4 software to look for correlations between the variables. The study revealed that among the 122 participants, a significant number (N = 47) accessed internet health information on an occasional basis, whereas 30 individuals reported using it infrequently. The data revealed that 58 individuals, accounting for 47.5% of the sample, possessed the necessary abilities to access and assess online health information. Additionally, 57 participants, representing 46.7% of the sample, demonstrated proficiency in conducting online health information searches. The measurement model demonstrated good convergent validity, as evidenced by composite reliability (CR)scores and Cronbach's ⍺ values over 0.700 and an average extracted variance (AVE) of 0.500. The structural model demonstrated R2values exceeding 0.1, thus validating its dependable forecasting capability. Notable effects were seen, with f2values of 0.335 and 0.317 for the challenges in accessing and evaluating health information (CAEHI) to health information evaluation (HIE) and CAEHI to HILSrelationships, respectively. The mediation analysis found that HILS act as a mediator between types of web resources (TWRs) and HIE, with TWR having an indirect impact on HIE through HILS. The result supports all hypotheses. Therefore, it is evident that students' HILS mediate the relationship between utilization of web resources and their proficiency in evaluating health information. This study's findings could significantly impact instructional practices meant to raise students' health information literacy. This initiative seeks to enable students to make informed decisions about their health by providing them with the necessary tools to analyse and evaluate health-related information. Research on health information literacy can assist patients and the general public by instructing them on how to assess trustworthy online health resources. Students gave insightful feedback that assisted in shaping the study and guaranteeing its relevancy. If they better comprehend health information literacy, patients and the general public can use web-based resources and critically evaluate health information more accurately.

Machine Learning Model for Predicting Mortality Risk in Patients With Complex Chronic Conditions: Retrospective Analysis.

The health care system is undergoing a shift toward a more patient-centered approach for individuals with chronic and complex conditions, which presents a series of challenges, such as predicting hospital needs and optimizing resources. At the same time, the exponential increase in health data availability has made it possible to apply advanced statistics and artificial intelligence techniques to develop decision-support systems and improve resource planning, diagnosis, and patient screening. These methods are key to automating the analysis of large volumes of medical data and reducing professional workloads. This article aims to present a machine learning model and a case study in a cohort of patients with highly complex conditions. The object was to predict mortality within the following 4 years and early mortality over 6 months following diagnosis. The method used easily accessible variables and health care resource utilization information. A classification algorithm was selected among 6 models implemented and evaluated using a stratified cross-validation strategy with k=10 and a 70/30 train-test split. The evaluation metrics used included accuracy, recall, precision, F1-score, and area under the receiver operating characteristic (AUROC) curve. The model predicted patient death with an 87% accuracy, recall of 87%, precision of 82%, F1-score of 84%, and area under the curve (AUC) of 0.88 using the best model, the Extreme Gradient Boosting (XGBoost) classifier. The results were worse when predicting premature deaths (following 6 months) with an 83% accuracy (recall=55%, precision=64% F1-score=57%, and AUC=0.88) using the Gradient Boosting (GRBoost) classifier. This study showcases encouraging outcomes in forecasting mortality among patients with intricate and persistent health conditions. The employed variables are conveniently accessible, and the incorporation of health care resource utilization information of the patient, which has not been employed by current state-of-the-art approaches, displays promising predictive power. The proposed prediction model is designed to efficiently identify cases that need customized care and proactively anticipate the demand for critical resources by health care providers.

Real-World Cost-Effectiveness of Pulmonary Vein Isolation for Atrial Fibrillation: A Target Trial Approach

ObjectivesRandomized controlled trials of pulmonary vein isolation (PVI) for treating atrial fibrillation (AF) have proven the procedure’s efficacy. Studies assessing its empirical cost-effectiveness outside randomized trial settings are lacking. We aimed to evaluate the effectiveness and cost-effectiveness of PVI versus medical therapy for AF. MethodsWe followed a target trial approach using the Swiss-AF cohort, a prospective observational cohort study that enrolled patients with AF between 2014 and 2017. Resource utilization and cost information were collected through claims data. Quality of life was measured with EQ-5D-3L utilities. We estimated incremental cost-effectiveness ratios (ICERs) from the perspective of the Swiss statutory health insurance system. ResultsPatients undergoing PVI compared with medical therapy had a 5-year overall survival advantage with a hazard ratio of 0.75 (95% CI 0.46-1.21; P = .69) and a 19.8% SD improvement in quality of life (95% CI 15.5-22.9; P < .001), at an incremental cost of 29 604 Swiss francs (CHF) (95% CI 16 354-42 855; P < .001). The estimated ICER was CHF 158 612 per quality-adjusted life-year (QALY) gained within a 5-year time horizon. Assuming similar health effects and costs over 5 additional years changed the ICER to CHF 82 195 per QALY gained. Results were robust to the sensitivity analyses performed. ConclusionsOur results show that PVI might be a cost-effective intervention within the Swiss healthcare context in a 10-year time horizon, but unlikely to be so at 5 years, if a willingness-to-pay threshold of CHF 100 000 per QALY gained is assumed. Given data availability, we find target trial designs are a valuable tool for assessing the cost-effectiveness of healthcare interventions outside of randomized controlled trial settings.

Epidemiology and healthcare resource utilization in atopic dermatitis in Colombia: A retrospective analysis of data from the National Health Registry from 2015 to 2020

Introduction: Atopic dermatitis, also known as eczema or atopic eczema, is a chronic inflammatory skin disorder characterized by the presence of pruritus accompanied by itching. In Colombia, epidemiological and healthcare resource utilization information regarding this pathology is limited. Objective: To describe atopic dermatitis epidemiological characteristics and healthcare resource utilization patterns in Colombia. Material and methods: A retrospective database study using real-world data obtained from the national claims database SISPRO (Sistema de Información para la Protección Social) for the 2015-2020 period was carried out. Sociodemographic (age, and health services delivery), epidemiological (incidence, prevalence, and comorbidities), and healthcare resource utilization data were extracted from the SISPRO database. Results: The epidemiological results showed increased incidence and prevalence of atopic dermatitis in Colombia in the 2018-2019 period compared to 2015-2017. Accordingly, the number of medical consultations (particularly with specialists), the number of procedures, and the number of hospitalizations of patients with atopic dermatitis increased. Topic and systemic corticoids were the most frequently prescribed drugs. Conclusions: Diagnoses of atopic dermatitis in Colombia increased with a concomitant increase in healthcare resource utilization during 2015-2020, which was possibly slowed down by the arrival of the Covid-19. This study may help physicians gaining a better understanding of the disease, improving atopic dermatitis patient management.

The Impact of Depression and Anxiety on Perioperative Outcomes and Patient-Reported Outcomes Measurement Information System Physical Function After Thoracolumbar Surgery.

Depression and anxiety are common within spine patient populations. The demand for surgical management of degenerative spine conditions and the prevalence of mental disorders are expected to increase as the general population ages. Concurrently, there is increasing pressure to demonstrate high-value care through improved perioperative outcome metrics and patient-reported outcome instruments. The purpose of this study was to evaluate the impact of common mental disorders on perioperative markers of high resource utilization and patient-reported outcomes measurement information system physical function (PROMIS-PF) following thoracolumbar (TL) spine surgery. A retrospective review of patients undergoing TL decompression alone or with fusion at a single institution. Data were collected using an administrative database for patient demographics. Outcomes of interest included length of stay, discharge disposition, 90-day return to the emergency department (ED), 90-day hospital readmission, 1-year complication rate, 1-year revision surgery rate, 1-year residual radiculopathy, and PROMIS-PF scores recorded preoperatively, at 0 to 1, 1 to 3, 3 to 6, and 6 to 12 months postoperatively. Univariate analysis and multiple linear regression were utilized to analyze results. A total of 596 patients were included in this study, of whom 205 (34%) had a history of depression or anxiety. Compared with patients with no history of a mental disorder, patients with depression or anxiety who underwent TL decompression alone had higher rates of 90-day ED visits (P = 0.019), 90-day readmissions (P = 0.031), and complications at 1 year (P = 0.012). After risk adjustment, the diagnosis of depression or anxiety had no significant effect on PROMIS-PF improvement from the preoperative to postoperative period. Our study suggests that a history of depression or anxiety is common among patients undergoing spine surgery but has no significant impact on PROMIS-PF improvement. Because some patients with depression or anxiety may be at higher risk of postoperative resource utilization, further study and effort are warranted to support at-risk groups and improve overall care value. Although patients with depression or anxiety are at risk for increased resource utilization after TL decompression or fusion, they can experience similar levels of functional improvement as patients without these conditions. Therefore depression or anxiety should not be considered contraindications to surgery, but additional attention should be paid to this population during the postoperative recovery period.

Habitual sleep duration and its relationship with cardiovascular health, healthcare costs, and resource utilization in a working population

Little is known about the relationship between habitual sleep duration, cardiovascular health (CVH) and their impact on healthcare costs and resource utilization. We describe the relationship between sleep duration and ideal CVH, and the associated burden of healthcare expenditure and utilization in a large South Florida employee population free from known cardiovascular disease. The study used data obtained from a 2014 voluntary Health Risk Assessment among 8629 adult employees of Baptist Health South Florida. Health expenditures and resource utilization information were obtained through medical claims data. Frequencies of the individual and cumulative CVH metrics across sleep duration were computed. Mean and marginal per-capita healthcare expenditures were estimated. The mean age was 43 years, 57% were of Hispanic ethnicity. Persons with 6-8.9hours and ≥9 hours of sleep were significantly more likely to report optimal goals for diet, physical activity, body mass index, and blood pressure when compared to those who slept less than 6 hours. Compared to those who slept less than 6 hours, those sleeping 6-8.9hours and ≥9hours had approximately 2- (odds ratio 2.1, 95% confidence interval: 1.9-3.0) and 3-times (odds ratio 3.0, 95% confidence interval: 1.6-5.6) higher odds of optimal CVH. Both groups with 6 or more hours of sleep had lower total per-capita expenditure (approximately $2000 and $2700 respectively), lower odds of visiting an emergency room, or being hospitalized compared to those who slept < 6 hours. Sleeping 6 or more hours was associated with better CVH, lower healthcare expenditures, and reduced healthcare resource utilization.

POSB238 Disease Burden of Aromatic L-Amino Acid Decarboxylase (AADC) Deficiency: Healthcare Resource Use (HCRU) Overall and by Disease Severity

Aromatic l-amino acid decarboxylase (AADC) deficiency is a rare neurological condition, Healthcare resource utilization (HCRU) information for this rare disease is scarce but required for economic evaluations. Therefore, the aim of this multi-country study was to generate data on the HCRU of patients with AADC deficiency.

POSC384 Aromatic L-Amino Acid Decarboxylase (AADC) Deficiency in UK: Burden of Disease

Aromatic l-amino acid decarboxylase (AADC) deficiency is a rare neurological condition, for which healthcare resource utilization (HCRU) information is scarce, but important to understand the overall burden of disease. Therefore, the aim was to generate data on the HCRU for the treatment patterns of patients with AADC deficiency in England.

Hyperspectral Classification Based on Coupling Multiscale Super-Pixels and Spatial Spectral Features

Hyperspectral remotely sensed images contain not only the spatial information of ground objects, but also their rich spectral information. Effectively improve the classification accuracy of hyperspectral images (HSIs), the purpose is to accurately grasp the current land resource utilization information, which is of great significance to the formulation and implementation of future land and space planning. Existing research on the classification of hyperspectral remotely sensed images uses a single-scale superpixel method for image segmentation. The optimal number of superpixels cannot be determined, image details may get missed, and a single kernel matrix cannot represent the information from multiple features, which results in reduced classification accuracy. Therefore, this study intends to use the superpixel segmentation method to perform multiscale superpixel segmentation on the principal components of HSIs at multiple scales, and to couple the multiscale superpixel spatial spectral kernel (SSK) with the original spectral kernel to form a synthetic kernel using weights for HSI classification. The three HSIs of Pavia University, Pavia Center, and Washington DC Mall are used as the experimental data to test and analyze this method. The experimental results show that the effective classification accuracies for the three datasets obtained by this method are 5.28%, 5.90%, and 7.71% higher than the five comparison methods, at best. The results prove that this method can effectively solve the problem of imprecise image feature extraction and an unknown number of initial superpixels and can significantly improve the classification accuracy of HSIs.

Machine Learning Enables Radio Resource Allocation in the Downlink of Ultra-Low Latency Vehicular Networks

Autonomous driving and intelligent transportation demand ultra-low latency and high reliability communication in future vehicular networks. Proactive wireless communication can facilitate minimal latency by open-loop communication, which discards traditional feedback control mechanisms. However, appropriate radio resource allocation in such proactive mobile networks has not been fully studied due to lacking channel state information (CSI) and the alleviation of multiple access interference (MAI) in multiple virtual cells. This paper aims to ensure the reliability of downlink communication by a novel radio resource allocation scheme in proactive vehicular networks with ultra-low latency. We regard data transmission success rate as the reliability indicator and propose a joint radio resource allocation model based on the “generalized closed-loop”, where anchor node (AN) uses the radio resource utilization information (RRUI) from the vehicle in the immediate past uplink as a guide to assist resource allocation. Subsequently, we study the radio resource allocation model solution on the vehicle side and the network side respectively. On the vehicle side, vehicles use the local or global data transmission experience to select the radio resource with the best quality as the RRUI. On the network side, according to the latest RRUI of vehicle and resource occupancy information, deep reinforcement learning is proposed to make appropriate radio resource allocation decisions. Simulations demonstrate the effectiveness of the intelligent joint radio resource allocation scheme under the cooperation between vehicles and AN. When the resource load rate reaches 40%, the joint radio resource allocation scheme can achieve a data transmission success rate of more than 98%.

Artificial Intelligence based BOT to Manage and Administer Cloud Services

In the era of cloud computing, every company uses cloud technology for its applications and other infrastructure to provide a highly available and easily accessible user experience. While monitoring and managing these assets becomes a hectic work for the IT admins. On which the Level of Effort (LOE) of the resource allocated will be high and the resource must reach different console for different information. Introducing an AI-powered bot which can monitor and manage the cloud assets will reduce the manpower drastically. Most enterprises currently have very rudimentary systems of resource management where someone in the role of an Azure or resource administrator log on to the Admin Portal of their resources and have to apply filters and search through multiple screens to find even the most basic information regarding utilization and cost. This leads to inefficient management of resources and almost leads to overspending in resources that are being underutilized. The implementation of the project will involve creating a cloud services management bot that can be integrated with an enterprise’s collaboration suite as a way to enhance the enterprise’s modern workspace. The bot is to be trained on a set of query data as part of the artificial intelligence process using the natural language processing packages that are included in the Azure Cognitive Services suite. Once queries are processed, the system will connect with the respective endpoints of the Azure Resource Management REST APIs to retrieve relevant resource utilization information and show that to the end-user.

PRO28 Healthcare Resource Use (HCRU) of Patients with Aromatic L-Amino Acid Decarboxylase Deficiency (AADC-D) in France

AADC-d is an ultra-rare autosomal recessive neurometabolic disorder leading to hypotonia, movement disorders, developmental delay and autonomic symptoms from birth. Given the lack of healthcare resource utilization (HCRU) information, the aim of this study, part of a global burden of disease project, was to generate French data to feed a cost-effectiveness model to support HTA submission.

Burden of illness of progressive familial intrahepatic cholestasis in the US, UK, France, and Germany: study rationale and protocol of the PICTURE study

ABSTRACT Background: Progressive familial intrahepatic cholestasis (PFIC) is an ultra-rare disease with a considerable burden on pediatric patients and their caregivers, impacting quality of life (QoL). The mortality rates highlight a significant need for efficacious treatments. Real-world data on associated costs and QoL are needed to gauge the potential impact of new pharmacological treatments. Methods: Clinical and socio-economic burden of PFIC on patients/caregivers, health systems, and society will be assessed. Patient/caregiver- and physician-level retrospective cross-sectional data will be collected from the US, UK, France, and Germany, for PFIC types 1, 2, 3. A representative sample of physicians will provide clinical and resource utilization information using an electronic Case Report Form (eCRF). Patient/caregiver surveys will collect socio-economic and QoL data, enabling assessment of PFIC impact on QoL. Mean costs (direct medical/non-medical, indirect) will be calculated. The study materials were reviewed by medical professionals and patient representatives and received ethical approval from the University of Chester. Discussion: The study aims to reveal the unmet medical need, disease burden, resource utilization, and costs of PFIC, to raise awareness with policymakers and healthcare professionals, and provide support for the patient/caregiver community. As novel PFIC therapies recently emerged, this study will yield quantifiable data for health technology assessments.

Burden of Treatment Resistant Depression (TRD) in patients with major depressive disorder in Ontario using Institute for Clinical Evaluative Sciences (ICES) databases: Economic burden and healthcare resource utilization

IntroductionThe burden of treatment-resistant depression (TRD) in Canada requires empirical characterization to better inform clinicians and policy decision-making in mental health. Towards this aim, this study utilized the Institute for Clinical Evaluative Sciences (ICES) databases to quantify the economic burden and resource utilization of Patients with TRD in Ontario. MethodsTRD, Non-TRD Major Depressive Disorder (Non-TRD MDD) and Non-MDD cohorts were selected from the ICES databases between April 2006-March 2015 and followed-up for at least two years. TRD was defined as a minimum of two treatment failures within one-year of the index MDD diagnosis. Non-TRD and Non-MDD patients were matched with patients with TRD to analyze costs, resource utilization, and demographic information. ResultsOut of 277 patients with TRD identified, the average age was 52 years (SD 16) and 53% were female. Compared to Non-TRD, the patients with TRD had more all-cause visits to outpatient (38.2 vs. 24.2) and emergency units (2.7 vs. 2.0) and more depression-related visits to GPs (3.06 vs. 1.63) and psychiatrists (5.88 vs. 1.95) (all p < 0.05). The average two-year cost for TRD patients was $20,998 (CAD). LimitationsThis study included patients with only public plan coverage; therefore, overall TRD population and cash and private claims were not captured. ConclusionsPatients with TRD exhibit a significantly higher demand on healthcare resources and higher overall payments compared to Non-TRD patients. The findings suggest that there are current challenges in adequately managing this difficult-to-treat patient group and there remains a high unmet need for new therapies.

Economic Burden of End Organ Damage Among Patients with Sickle Cell Disease in the US

Economic Burden of End Organ Damage Among Patients with Sickle Cell Disease in the US

Bundled payment reimbursement for anterior and posterior approaches for cervical spondylotic myelopathy: an analysis of private payer and Medicare databases.

OBJECTIVE Cervical spondylotic myelopathy (CSM) is a progressive spinal condition that often requires surgery. Studies have shown the clinical equivalency of anterior versus posterior approaches for CSM surgery. The purpose of this study was to determine the amount and type of resources used for anterior and posterior surgical treatment of CSM by using large national databases of clinical and financial information from patients. METHODS This study consists of 2 large cohorts of patients who underwent either an anterior or posterior approach for treatment of CSM. These patients were selected from the Medicare 5% National Sample Administrative Database (SAF5) and the Humana orthopedic database (HORTHO), which is a database of patients with private payer health insurance. The outcome measures were the cost of a 90-day episode of care, as well as a breakdown of the cost components for each surgical procedure between 2005 and 2014. RESULTS A total of 16,444 patients were included in this analysis. In HORTHO, there were 10,332 and 1556 patients treated with an anterior or posterior approach for CSM, respectively. In SAF5, there were 3851 and 705 patients who were treated by an anterior or posterior approach for CSM, respectively. The mean ± SD reimbursements for anterior and posterior approaches in the HORTHO database were $20,863 ± $2014 and $23,813 ± $4258, respectively (p = 0.048). The mean ± SD reimbursements for anterior and posterior approaches in the SAF5 database were $18,219 ± $1053 and $25,598 ± $1686, respectively (p < 0.0001). There were also significantly higher reimbursements for a rehabilitation/skilled nursing facility and hospital/inpatient care for patients who underwent a posterior approach in both the private payer and Medicare databases. In all cohorts in this study, the hospital-related reimbursement was more than double the surgeon-related reimbursement. CONCLUSIONS This study provides resource utilization information for a 90-day episode of care for both anterior and posterior approaches for CSM surgery. There is a statistically significant higher resource utilization for patients undergoing the posterior approach for CSM, which is consistent with the literature. Understanding the reimbursement patterns for anterior versus posterior approaches for CSM will help providers design a bundled payment for patients requiring surgery for CSM, and this study suggests that a subset of patients who require the posterior approach for treatment also require greater resources. The data also suggest that hospital-related reimbursement is the major driver of payments.

Deconstructing the Energy Consumption of the Mobile Page Load

Mobile Web page performance is critical to content providers, service providers, and users, as Web browsers are one of the most popular apps on phones. Slow Web pages are known to adversely affect profits and lead to user abandonment. While improving mobile web performance has drawn increasing attention, most optimizations tend to overlook an important factor, energy. Given the importance of battery life for mobile users, we argue that web page optimizations should be evaluated for their impact on energy consumption. However, examining the energy effects of a web optimization is challenging, even if one has access to power monitors, for several reasons. First, the page load process is relatively short-lived, ranging from several milliseconds to a few seconds. Fine-grained resource monitoring on such short timescales to model energy consumption is known to incur substantial overhead. Second, Web pages are complex. A Web enhancement can have widely varying effects on different page load activities. Thus, studying the energy impact of a Web enhancement on page loads requires understanding its effects on each page load activity. Existing approaches to analyzing mobile energy typically focus on profiling and modeling the resource consumption of the device during execution. Such approaches consider long-running services and apps such as games, audio, and video streaming, for which low-overhead, coarse-grained resource monitoring suffices. For page loads, however, coarse-grained resource monitoring is not sufficient to analyze the energy consumption of individual, short-lived, page load activities. We present RECON (REsource- and COmpoNent-based modeling), a modeling approach that addresses the above challenges to estimate the energy consumption of any Web page load. The key intuition behind RECON is to go beyond resource-level information and exploit application-level semantics to capture the individual Web page load activities. Instead of modeling the energy consumption at the full page load level, which is too coarse grained, RECON models at a much finer component level granularity. Components are individual page load activities such as loading objects, parsing the page, or evaluating JavaScript. To do this, RECON combines coarse-grained resource utilization and component-level Web page load information available from existing tools. During the initial training stage, RECON uses a power monitor to measure the energy consumption during a set of page load processes and juxtaposes this power consumption with coarse-grained resource and component information. RECON uses both simple linear regression and more complex neural networks to build a model of the power consumption as a function of the resources used and the individual page load components, thus providing benefits over individual models. Using the model, RECON can estimate the energy consumption of any Web page loaded as-is or upon applying any enhancement, without the monitor. We experimentally evaluate RECON on the Samsung Galaxy S4, S5, and Nexus devices using 80 Web pages. Comparisons with actual power measurements from a fine-grained power meter show that, using the linear regression model, RECON can estimate the energy consumption of the entire page load with a mean error of 6.3% and that of individual page load activity segments with a mean error of 16.4%. When trained as a neural network, RECON's mean error for page energy estimation reduces to 5.4% and the mean segment error is 16.5%. We show that RECON can accurately estimate the energy consumption of a Web page under different network conditions, such as lower bandwidth or higher RTT, even when the model is trained under a default network condition. RECON also accurately estimates the energy consumption of a Web page after applying popular Web enhancements including ad blocking, inlining, compression, and caching.

A Radio Resource Virtualization-Based RAT Selection Scheme in Heterogeneous Networks

In the future heterogeneous wireless networks, heterogeneity of radio resources from different radio access technologies (RATs) still exists. The heterogeneity, especially for networks with the coexistence of non-orthogonal and orthogonal resources, makes the radio resources difficult to be uniformly measured, and thus hinders the efficient utilization of radio resources. To overcome this limitation, this letter firstly proposes a radio resource virtualization approach in heterogeneous networks. Based on the accumulated historical data of resource utilization information, heterogeneous radio resources are virtualized into normalized resources using deep learning method. Secondly, the consumption difference of virtualized resources under different situations of network load and user demand is modeled. Moreover, aiming at efficiently utilizing radio resources and reducing access blocking rate, a RAT selection scheme based on the radio resource virtualization is proposed. Through simulation, the validity of the proposed scheme is evaluated.

Tolhit – A Scheduling Algorithm for Hadoop Cluster

With the accretion in use of Internet in everything, a prodigious influx of data is being observed. Use of MapReduce as a programming model has become pervasive for processing such wide range of Big Data Applications in cloud computing environment. Apache Hadoop is the most prominent implementation of MapReduce, which is used for processing and analyses of such large scale data intensive applications in a highly scalable and fault tolerant manner. Several scheduling algorithms have been proposed for Hadoop considering various performance goals. In this work, a new scheme is introduced to aid the scheduler in identifying the nodes on which stragglers can be executed. The proposed scheme makes use of resource utilization and network information of cluster nodes in finding the most optimal node for scheduling the speculative copy of a slow task. The performance evaluation of the proposed scheme has been done by series of experiments. From the performance analysis 27% improvement in terms of the overall execution time has been observed over Hadoop Fair Scheduler (HFS).

A Simulation Tool for Security of Hardware Resource Utilization Information in Cloud Environment with EnDeCloudReports Tool

A cloud storage mechanism repressed a storage device design and cloud based protocols. Cloud storage architecture can be exposed design for remote method access through cloud storage device. Cloud storage is a centralized data distribution center meant for authorized clients, among them cloud data will share towards different clients as apiece their authority in addition to access permissions. Based on this assortment of researches are being proceeding. However, within this article it focuses the storing data in the midst of fully secure in cloud report also. The swift progression of the cloud has formed it as a desirable ambition of attack intended for both foreign attackers and malicious insiders. Files, blocks, data sets and objects are common logic units of data storage within cloud storage device mechanism. In this research it pioneer with a new set of methods or new unit of methods handled by means of the AES algorithm meant for creating strong encryption and decryption.