Flexible-Matching Data-Comparison Tool with the Cloud Applications

2010 ◽  
Author(s):  
Wei-Tek Tsai ◽  
Xin Sun ◽  
Qihong Shao
Keyword(s):  
Data Comparison ◽  
Cloud Applications ◽  
Flexible Matching

scholarly journals Self-healing trans-cloud applications

Computing ◽  
2021 ◽  
Author(s):  
Antonio Brogi ◽  
Jose Carrasco ◽  
Francisco Durán ◽  
Ernesto Pimentel ◽  
Jacopo Soldani
Keyword(s):  
Open Source ◽  
Cloud Services ◽  
Self Healing ◽  
Controlled Experiments ◽  
Fault Handling ◽  
Extensive Evaluation ◽  
Cloud Providers ◽  
Heterogeneous Cloud ◽  
Cloud Applications ◽  
Unstable States

AbstractTrans-cloud applications consist of multiple interacting components deployed across different cloud providers and at different service layers (IaaS and PaaS). In such complex deployment scenarios, fault handling and recovery need to deal with heterogeneous cloud offerings and to take into account inter-component dependencies. We propose a methodology for self-healing trans-cloud applications from failures occurring in application components or in the cloud services hosting them, both during deployment and while they are being operated. The proposed methodology enables reducing the time application components rely on faulted services, hence residing in “unstable” states where they can suddenly fail in cascade or exhibit erroneous behaviour. We also present an open-source prototype illustrating the feasibility of our proposal, which we have exploited to carry out an extensive evaluation based on controlled experiments and monkey testing.

scholarly journals Measurements of the time-dependent cosmic-ray Sun shadow with seven years of IceCube data: Comparison with the Solar cycle and magnetic field models

2021 ◽  
Vol 103 (4) ◽  
Author(s):  
M. G. Aartsen ◽  
R. Abbasi ◽  
M. Ackermann ◽  
J. Adams ◽  
J. A. Aguilar ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Solar Cycle ◽  
Cosmic Ray ◽  
Time Dependent ◽  
Data Comparison

scholarly journals Selective caching: a persistent memory approach for multi-dimensional index structures

2021 ◽  
Author(s):  
Muhammad Attahir Jibril ◽  
Philipp Götze ◽  
David Broneske ◽  
Kai-Uwe Sattler
Keyword(s):  
Main Memory ◽  
Index Structure ◽  
Index Structures ◽  
Cloud Infrastructure ◽  
General Technique ◽  
Persistent Memory ◽  
The Cost ◽  
Cloud Applications ◽  
Memory Layout ◽  
Analytical Index

AbstractAfter the introduction of Persistent Memory in the form of Intel’s Optane DC Persistent Memory on the market in 2019, it has found its way into manifold applications and systems. As Google and other cloud infrastructure providers are starting to incorporate Persistent Memory into their portfolio, it is only logical that cloud applications have to exploit its inherent properties. Persistent Memory can serve as a DRAM substitute, but guarantees persistence at the cost of compromised read/write performance compared to standard DRAM. These properties particularly affect the performance of index structures, since they are subject to frequent updates and queries. However, adapting each and every index structure to exploit the properties of Persistent Memory is tedious. Hence, we require a general technique that hides this access gap, e.g., by using DRAM caching strategies. To exploit Persistent Memory properties for analytical index structures, we propose selective caching. It is based on a mixture of dynamic and static caching of tree nodes in DRAM to reach near-DRAM access speeds for index structures. In this paper, we evaluate selective caching on the OLAP-optimized main-memory index structure Elf, because its memory layout allows for an easy caching. Our experiments show that if configured well, selective caching with a suitable replacement strategy can keep pace with pure DRAM storage of Elf while guaranteeing persistence. These results are also reflected when selective caching is used for parallel workloads.

scholarly journals Targeting Mitochondria in Diabetes

2021 ◽  
Vol 22 (12) ◽  
pp. 6642
Author(s):  
Nina Krako Jakovljevic ◽  
Kasja Pavlovic ◽  
Aleksandra Jotic ◽  
Katarina Lalic ◽  
Milica Stoiljkovic ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Mitochondrial Function ◽  
Whole Body ◽  
Noncommunicable Diseases ◽  
Pharmacological Interventions ◽  
Cellular Energy ◽  
Cellular Energy Metabolism ◽  
Data Comparison ◽  
Selection Of

Type 2 diabetes (T2D), one of the most prevalent noncommunicable diseases, is often preceded by insulin resistance (IR), which underlies the inability of tissues to respond to insulin and leads to disturbed metabolic homeostasis. Mitochondria, as a central player in the cellular energy metabolism, are involved in the mechanisms of IR and T2D. Mitochondrial function is affected by insulin resistance in different tissues, among which skeletal muscle and liver have the highest impact on whole-body glucose homeostasis. This review focuses on human studies that assess mitochondrial function in liver, muscle and blood cells in the context of T2D. Furthermore, different interventions targeting mitochondria in IR and T2D are listed, with a selection of studies using respirometry as a measure of mitochondrial function, for better data comparison. Altogether, mitochondrial respiratory capacity appears to be a metabolic indicator since it decreases as the disease progresses but increases after lifestyle (exercise) and pharmacological interventions, together with the improvement in metabolic health. Finally, novel therapeutics developed to target mitochondria have potential for a more integrative therapeutic approach, treating both causative and secondary defects of diabetes.

scholarly journals An Enterprise Time Series Forecasting System for Cloud Applications Using Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1590
Author(s):  
Arnak Poghosyan ◽  
Ashot Harutyunyan ◽  
Naira Grigoryan ◽  
Clement Pang ◽  
George Oganesyan ◽  
...  
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Transfer Learning ◽  
Performance Monitoring ◽  
Nonlinear Problems ◽  
Time Series Forecasting ◽  
Stationary Time Series ◽  
Learning Gain ◽  
Application Performance ◽  
Cloud Applications

The main purpose of an application performance monitoring/management (APM) software is to ensure the highest availability, efficiency and security of applications. An APM software accomplishes the main goals through automation, measurements, analysis and diagnostics. Gartner specifies the three crucial capabilities of APM softwares. The first is an end-user experience monitoring for revealing the interactions of users with application and infrastructure components. The second is application discovery, diagnostics and tracing. The third key component is machine learning (ML) and artificial intelligence (AI) powered data analytics for predictions, anomaly detection, event correlations and root cause analysis. Time series metrics, logs and traces are the three pillars of observability and the valuable source of information for IT operations. Accurate, scalable and robust time series forecasting and anomaly detection are the requested capabilities of the analytics. Approaches based on neural networks (NN) and deep learning gain an increasing popularity due to their flexibility and ability to tackle complex nonlinear problems. However, some of the disadvantages of NN-based models for distributed cloud applications mitigate expectations and require specific approaches. We demonstrate how NN-models, pretrained on a global time series database, can be applied to customer specific data using transfer learning. In general, NN-models adequately operate only on stationary time series. Application to nonstationary time series requires multilayer data processing including hypothesis testing for data categorization, category specific transformations into stationary data, forecasting and backward transformations. We present the mathematical background of this approach and discuss experimental results based on implementation for Wavefront by VMware (an APM software) while monitoring real customer cloud environments.

Corneal densitometry in patients with keratoconus undergoing intrastromal Ferrara ring implantation

2021 ◽  
pp. 112067212110206
Author(s):  
Pablo Felipe Rodrigues ◽  
Bernardo Kaplan Moscovici ◽  
Guilherme Ferrara ◽  
Luciano Lamazales ◽  
Marcela Mara Silva Freitas ◽  
...  
Keyword(s):  
Visual Acuity ◽  
Medical Records ◽  
Spherical Equivalent ◽  
Interventional Study ◽  
Data Comparison ◽  
Corneal Densitometry ◽  
Corrected Visual Acuity ◽  
Ring Segment ◽  
The Mean

Objective: Evaluation of central corneal densitometry changes following Ferrara corneal ring segment implantation in patients with keratoconus, especially the correlation between corneal densitometry and keratometry. Methods: Retrospective, non-comparative, interventional study based on the review of medical records of patients diagnosed with keratoconus who underwent Ferrara corneal ring segment implantation. Pre and post-operative corneal densitometry measurements obtained with Pentacam HR (Oculus, Wetzlar, Germany) were analyzed. The follow-up time was 3 months, and data comparison was made, using specific statistical analysis, with the data of 3 months postoperatively. Results: The study sample consisted of 43 eyes of 36 patients. The mean corrected visual acuity improved from 0.82 LogMAR preoperatively (SD ± 0.33) to 0.19 LogMAR (SD ± 0.13) postoperatively. The mean spherical equivalent varied from −4.63 (SD ± 3.94) preoperatively to −2.16 (SD ± 2.63) postoperatively. Asphericity varied from −0.69 (SD ± 0.32) preoperatively to −0.27 (SD ± 0.31) postoperatively. The mean maximum K was 54.01D (SD ± 3.38) preoperatively and 51.50D (SD ± 2.90) postoperatively. The mean anterior densitometric value was 18.26 (SD ± 2.03) preoperatively and 17.66 (SD ± 1.84) postoperatively. Conclusion: Corneal densitometry is an interesting technology that should be studied in keratoconus patients. Our results suggest that the corneal densitometry in the cornea’s anterior layer reduces after ICRS implantation and correlates with corneal keratometry. Further studies should be performed to increase the knowledge in this field.

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