Response Time Reliability in Cloud Environments: An Empirical Study of n-Tier Applications at High Resource Utilization

2012 ◽  
Author(s):  
Qingyang Wang ◽  
Yasuhiko Kanemasa ◽  
Jack Li ◽  
Deepal Jayasinghe ◽  
Motoyuki Kawaba ◽  
...  
Keyword(s):  
Empirical Study ◽  
Response Time ◽  
Resource Utilization ◽  
High Resource ◽  
Cloud Environments

P053 VARIATIONS IN HEALTHCARE UTILIZATION PATTERNS AMONG HIGH RISK INFLAMMATORY BOWEL DISEASE PATIENTS ENROLLED IN HIGH DEDUCTIBLE HEALTH PLANS

2020 ◽  
Vol 26 (Supplement_1) ◽  
pp. S67-S68
Author(s):  
Jeffrey Berinstein ◽  
Shirley Cohen-Mekelburg ◽  
Calen Steiner ◽  
Megan Mcleod ◽  
Mohamed Noureldin ◽  
...  
Keyword(s):  
At Risk ◽  
Resource Utilization ◽  
Time Trends ◽  
Healthcare Costs ◽  
Insurance Plan ◽  
High Resource ◽  
Patients At Risk ◽  
Inflammatory Bowel ◽  
Ed Visits ◽  
Out Of Pocket Costs

Abstract Background High-deductible health plan (HDHP) enrollment has increased rapidly over the last decade. Patients with HDHPs are incentivized to delay or avoid necessary medical care. We aimed to quantify the out-of-pocket costs of Inflammatory Bowel Disease (IBD) patients at risk for high healthcare resource utilization and to evaluate for differences in medical service utilization according to time in insurance period between HDHP and traditional health plan (THP) enrollees. Variations in healthcare utilization according to time may suggest that these patients are delaying or foregoing necessary medical care due to healthcare costs. Methods IBD patients at risk for high resource utilization (defined as recent corticosteroid and narcotic use) continuously enrolled in an HDHP or THP from 2009–2016 were identified using the Truven Health MarketScan database. Median annual financial information was calculated. Time trends in office visits, colonoscopies, emergency department (ED) visits, and hospitalizations were evaluated using additive decomposition time series analysis. Financial information and time trends were compared between the two insurance plan groups. Results Of 605,862 with a diagnosis of IBD, we identified 13,052 patients at risk for high resource utilization with continuous insurance plan enrollment. The median annual out-of-pocket costs were higher in the HDHP group (n=524) than in the THP group (n=12,458) ($1,920 vs. $1,205, p<0.001), as was the median deductible amount ($1,015 vs $289, p<0.001), without any difference in the median annual total healthcare expenses (Figure 1). Time in insurance period had a greater influence on utilization of colonoscopies, ED visits, and hospitalization in IBD patients enrolled in HDHPs compared to THPs (Figure 2). Colonoscopies peaked in the 4th quarter, ED visits peaked in the 1st quarter, and hospitalizations peaked in the 3rd and 4th quarter. Conclusion Among IBD patients at high risk for IBD-related utilization, HDHP enrollment does not change the cost of care, but shifts healthcare costs onto patients. This may be a result of HDHPs incentivizing delays with a potential for both worse disease outcomes and financial toxicity and needs to be further examined using prospective studies.

A Promising Hardware Accelerator with PAST Adder

2021 ◽  
Vol 105 ◽  
pp. 241-248
Author(s):  
Abhishek Choubey ◽  
Shruti Bhargava Choubey
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Resource Utilization ◽  
Network Inference ◽  
Hardware Accelerator ◽  
High Complexity ◽  
High Resource ◽  
The Neural Network ◽  
And Storage ◽  
Dedicated Hardware

Recent neural network research has demonstrated a significant benefit in machine learning compared to conventional algorithms based on handcrafted models and features. In regions such as video, speech and image recognition, the neural network is now widely adopted. But the high complexity of neural network inference in computation and storage poses great differences on its application. These networks are computer-intensive algorithms that currently require the execution of dedicated hardware. In this case, we point out the difficulty of Adders (MOAs) and their high-resource utilization in a CNN implementation of FPGA .to address these challenge a parallel self-time adder is implemented which mainly aims at minimizing the amount of transistors and estimating different factors for PASTA, i.e. field, power, delay.

Abstract 16763: Frailty, as Measured by the Essential Frailty Toolset, is Associated With Higher Resource Utilization in Patients Undergoing Coronary Artery Bypass Grafting

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Fady K Soliman ◽  
Lindsay Volk ◽  
Rajath Kenath ◽  
Alexis K Okoh ◽  
Joshua C Chao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Coronary Artery ◽  
Coronary Artery Bypass ◽  
Resource Utilization ◽  
Artery Bypass ◽  
Direct Costs ◽  
High Resource ◽  
Frail Patients ◽  
Multivariable Logistic Regression ◽  
Prolonged Ventilation

Introduction: Frailty is an important predictor of clinical outcomes, but its contribution to resource utilization remains understudied. This study investigates the impact of frailty on high resource utilization (HRU) in patients undergoing Coronary Artery Bypass Graft Surgery (CABG). Methods: We reviewed data on patients who underwent CABG at a single center between 04/2018 and 12/2019. A Frailty score (FS) was calculated using the Essential Frailty Toolset (EFT). Patients were divided into two groups: Frail (FS ≥ 3/5) & Non-Frail (FS <3/5). Baseline clinical characteristics and outcomes were compared in both groups. The primary outcome was HRU (post-operative length of stay > 7 days or readmission within 30-days). Secondary outcomes included operative time, prolonged ventilation, & direct procedure costs. Multivariable logistic regression was used to assess the effect of frailty on HRU. Results: The study included 740 patients of whom 18% (n=132) were frail. Compared to Non-Frail patients, Frail patients were older (66 vs. 70 yrs. P<0.001) and more likely to be high risk for operative mortality (1.3% vs. 14%, p<0.001). The incidence of HRU was 28% vs. 53%, p<0.001, in Non-Frail vs. Frail patients. Frail patients had longer operative times (272 vs. 247 mins; p<0.001), and a higher incidence of prolonged ventilation (9.9% vs. 4%; p<0.001). Median direct costs were also higher in Frail subjects ($33,434 vs. $22, 207; p<0.001). On multivariable logistic regression analysis, independent predictors of HRU were (OR: 95% C.I.) Frailty: 2.19(1.44, 3.33; p=0.003), Sex (Female): 1.66 (1.14, 2.40; p=0.008), and history of COPD: 2.32(1.53, 3.54; p<0.001). Conclusions: About one out of every five patients undergoing CABG was classified as frail by the EFT. Frailty was associated with higher direct costs and found to be an independent predictor of high resource utilization. Further attention is required to optimize outcomes and resource use in this vulnerable population.

scholarly journals Multi-criteria Checkpointing Strategies: Response-Time versus Resource Utilization

2013 ◽  
pp. 420-431 ◽  
Author(s):  
Aurelien Bouteiller ◽  
Franck Cappello ◽  
Jack Dongarra ◽  
Amina Guermouche ◽  
Thomas Hérault ◽  
...  
Keyword(s):  
Response Time ◽  
Resource Utilization

A Proficient Approach for Load Balancing in Cloud Computing-Join Minimum Loaded Queue

2020 ◽  
Vol 11 (1) ◽  
pp. 12-36
Author(s):  
Minakshi Sharma ◽  
Rajneesh Kumar ◽  
Anurag Jain
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Resource Utilization ◽  
Cloud Service ◽  
Cloud Environment ◽  
Cloud Service Provider ◽  
Qos Parameters ◽  
Load Balancing Algorithm

Cloud load balancing is done to persist the services in the cloud environment along with quality of service (QoS) parameters. An efficient load balancing algorithm should be based on better optimization of these QoS parameters which results in efficient scheduling. Most of the load balancing algorithms which exist consider response time or resource utilization constraints but an efficient algorithm must consider both perspectives from the user side and cloud service provider side. This article presents a load balancing strategy that efficiently allocates tasks to virtualized resources to get maximum resource utilization in minimum response time. The proposed approach, join minimum loaded queue (JMLQ), is based on the existing join idle queue (JIQ) model that has been modified by replacing idle servers in the I-queues with servers having one task in execution list. The results of simulation in CloudSim verify that the proposed approach efficiently maximizes resource utilization by reducing the response time in comparison to its other variants.

scholarly journals HIGH RESOURCE UTILIZATION FOR NON-CARDIAC HOSPITAL ADMISSIONS FOR ADULTS WITH CONGENITAL HEART DISEASE

2015 ◽  
Vol 65 (10) ◽  
pp. A543 ◽  
Author(s):  
Michael Seckeler ◽  
Ian D. Thomas ◽  
Jennifer Andrews ◽  
Omar Meziab ◽  
Elissa Heller ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Resource Utilization ◽  
Congenital Heart ◽  
Hospital Admissions ◽  
High Resource

scholarly journals Toward a Unified Framework for the Heterogeneous Cloud Utilizing Bytecode Containers

2021 ◽  
Author(s):  
David Andrew Lloyd Tenty
Keyword(s):  
Cloud Computing ◽  
Resource Utilization ◽  
Heterogeneous Systems ◽  
Public Cloud ◽  
Unified Framework ◽  
The Public ◽  
Cloud Environments ◽  
Heterogeneous Cloud ◽  
Systems Framework ◽  
Computing Landscape

As we approach the limits of Moore’s law the Cloud computing landscape is becoming ever more heterogeneous in order to extract more performance from available resources. Meanwhile, the container-based cloud is of growing importance as a lightweight way to deploy applications. A unified heterogeneous systems framework for use with container-based applications in the heterogeneous cloud is required. We present a bytecode-based framework and it’s implementation called Man O’ War, which allows for the creation of novel, portable LLVM bitcode-based containers for use in the heterogeneous cloud. Containers in Man O’ War enabled systems can be efficiently specialized for the available hardware within the Cloud and expand the frontiers for optimization in heterogeneous cloud environments. We demonstrate that a framework utilizing portable bytecode-based containers eases optimizations such as heterogeneous scaling which have the potential to improve resource utilization and significantly lower costs for users of the public cloud.

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