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As computing infrastructure expands, resource management in a large, heterogeneous, and distributed environment becomes a challenging task. In a cloud environment, with uncertainty and dispersion of resources, one encounters problems of allocation of resources, which is caused by things such as heterogeneity, dynamism, and failures. Unfortunately, existing resource management techniques, frameworks, and mechanisms are insufficient to handle these environments, applications, and resource behaviors. To provide efficient performance of workloads and applications, the aforementioned characteristics should be addressed effectively. This research depicts a broad methodical literature analysis of autonomic resource management in the area of the cloud in general and QoS (Quality of Service)-aware autonomic resource management specifically. The current status of autonomic resource management in cloud computing is distributed into various categories. Methodical analysis of autonomic resource management in cloud computing and its techniques are described as developed by various industry and academic groups. Further, taxonomy of autonomic resource management in the cloud has been presented. This research work will help researchers find the important characteristics of autonomic resource management and will also help to select the most suitable technique for autonomic resource management in a specific application along with significant future research directions. [ABSTRACT FROM AUTHOR]

Cloud computing is a form of distributed computing, which promises to deliver reliable services through next-generation data centers that are built on virtualized compute and storage technologies. It is becoming truly ubiquitous and with cloud infrastructures becoming essential components for providing Internet services, there is an increase in energy-hungry data centers deployed by cloud providers. As cloud providers often rely on large data centers to offer the resources required by the users, the energy consumed by cloud infrastructures has become a key environmental and economical concern. Much energy is wasted in these data centers because of under-utilized resources hence contributing to global warming. To conserve energy, these under-utilized resources need to be efficiently utilized and to achieve this, jobs need to be allocated to the cloud resources in such a way so that the resources are used efficiently and there is a gain in performance and energy efficiency. In this paper, a model for energy-aware resource utilization technique has been proposed to efficiently manage cloud resources and enhance their utilization. It further helps in reducing the energy consumption of clouds by using server consolidation through virtualization without degrading the performance of users' applications. An artificial bee colony based energy-aware resource utilization technique corresponding to the model has been designed to allocate jobs to the resources in a cloud environment. The performance of the proposed algorithm has been evaluated with the existing algorithms through the CloudSim toolkit. The experimental results demonstrate that the proposed technique outperforms the existing techniques by minimizing energy consumption and execution time of applications submitted to the cloud. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing utilizes heterogeneous resources that are located in various datacenters to provide an efficient performance on a pay‐per‐use basis. However, existing mechanisms, frameworks, and techniques for management of resources are inadequate to manage these applications, environments, and the behavior of resources. There is a requirement of a Quality of Service (QoS) based autonomic resource management technique to execute workloads and deliver cost‐efficient and reliable cloud services automatically. In this paper, we present an intelligent and autonomic resource management technique named RADAR. RADAR focuses on two properties of self‐management: firstly, self‐healing that handles unexpected failures and, secondly, self‐configuration of resources and applications. The performance of RADAR is evaluated in the cloud simulation environment and the experimental results show that RADAR delivers better outcomes in terms of execution cost, resource contention, execution time, and SLA violation while it delivers reliable services. [ABSTRACT FROM AUTHOR]

Languages help to unite the world socially, culturally and technologically. Different natives communicate in different languages; there is a tremendous requirement for inter-language information translation process to transfer and share information and ideas. Though Sanskrit is an ancient Indo-European language, a significant amount of work for processing the information is required to explore the full potential of this language to open vistas in computational linguistics and computer science domain. In this paper, we have proposed and presented the machine translation system for translating Sanskrit to the Hindi language. The developed technique uses linguistic features from rule-based feed to train neural machine translation system. The work is novel and applicable to any low-resource language with rich morphology. It is a generic system covering various domains with minimal human intervention. The performance analysis of work is performed on automatic and linguistic measures. The results show that proposed and developed approach outperforms earlier work for this language pair. [ABSTRACT FROM AUTHOR]

Machine translation system (MTS) constitutes of functionally heterogeneous modules for processing source language to a given target language. Deploying such an application on a stand-alone system requires much time, knowledge and complications. It even becomes more challenging for a common user to utilize such a complex application. This paper presents a MTS that has been developed using a combination of linguistic rich, rule-based and prominent neural-based approach. The proposed MTS is deployed on the cloud to offer translation as a cloud service and improve the quality of service (QoS) from a stand-alone system. It is developed on TensorFlow and deployed under the cluster of virtual machines in the Amazon web server (EC2). The significance of this paper is to demonstrate management of recurrent changes in term of corpus, domain, algorithm and rules. Further, the paper also compares the MTS as deployed on stand-alone machine and on cloud for different QoS parameters like response time, server load, CPU utilization and throughput. The experimental results assert that in the translation task, with the availability of elastic computing resources in the cloud environment, the job completion time irrespective of its size can be assured to be within a fixed time limit with high accuracy. [ABSTRACT FROM AUTHOR]

Abstract Cloud Computing has become prime infrastructure for scientists to deploy scientific applications as it offers parallel and distributed environment for large-scale computations. During deployment, the significant prediction of resource usage is essential to achieve optimal scheduling for scientific applications. The existing resource prediction models fall short in providing reasonable accuracy because of high variances of cloud metrics. Therefore, to handle the varying cloud resource demands, it is necessary to accurately predict the future resource requirements for automatically provisioning the resources. In this paper, an Intelligent Regressive Ensemble Approach for Prediction (REAP) has been proposed which integrates feature selection and resource usage prediction techniques to achieve high performance. The effectiveness of proposed approach is evaluated in a real cloud environment by conducting a series of experiments. The experimental results show that the proposed approach outperforms the existing models by significantly improving the accuracy rate and reducing the execution time. The results are further validated by comparing the existing Learning Automata (LA) based ensemble approach with the proposed approach on the basis of error rate. Highlights • Regressive Ensemble Approach for Predicting (REAP) CPU usage of a scientific application is proposed. • Dataset is generated by simulating Cybershake on workflow management system. • A meta-heuristic, Genetic Algorithm is used for selecting the relevant features. • REAP improves the accuracy rate, reduces the execution time and error rate. [ABSTRACT FROM AUTHOR]

High-throughput technologies and machine learning (ML), when applied to a huge pool of medical data such as omics data, result in efficient analysis. Recent research aims to apply and develop ML models to predict a disease well in time using available omics datasets. The present work proposed a framework, 'OmicPredict', deploying a hybrid feature selection method and deep neural network (DNN) model to predict multiple diseases using omics data. The hybrid feature selection method is developed using the Analysis of Variance (ANOVA) technique and firefly algorithm. The OmicPredict framework is applied to three case studies, Alzheimer's disease, Breast cancer, and Coronavirus disease 2019 (COVID-19). In the case study of Alzheimer's disease, the framework predicts patients using GSE33000 and GSE44770 dataset. In the case study of Breast cancer, the framework predicts human epidermal growth factor receptor 2 (HER2) subtype status using Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset. In the case study of COVID-19, the framework performs patients' classification using GSE157103 dataset. The experimental results show that DNN model achieved an Area Under Curve (AUC) score of 0.949 for the Alzheimer's (GSE33000 and GSE44770) dataset. Furthermore, it achieved an AUC score of 0.987 and 0.989 for breast cancer (METABRIC) and COVID-19 (GSE157103) datasets, respectively, outperforming Random Forest, Naïve Bayes models, and the existing research.

The current global pandemic, Covid-19, is a severe threat to human health and existence especially when it is mutating very frequently. Being a novel disease, Covid-19 is impacting the patients with comorbidities and is predicted to have long-term consequences, even for those who have recovered from it. To clearly recognize its impact, it is important to comprehend the complex relationship between Covid-19 and other diseases. It is also being observed that people with good immune system are less susceptible to the disease. It is perceived that if a correlation between Covid-19, other diseases, and diet is realized, then caregivers would be able to enhance their further course of medical action and recommendations. Network Analysis is one such technique that can bring forth such complex interdependencies and associations. In this paper, a Network Analysis-based approach has been proposed for analyzing the interplay of diets/foods along with Covid-19 and other diseases. Relationships between Covid-19, diabetes mellitus type 2 (T2DM), non-alcoholic fatty liver disease (NAFLD), and diets have been curated, visualized, and further analyzed in this study so as to predict unknown associations. Network algorithms including Louvain graph algorithm (LA), K nearest neighbors (KNN), and Page rank algorithms (PR) have been employed for predicting a total of 60 disease-diet associations, out of which 46 have been found to be either significant in disease risk prevention/mitigation or in its progression as validated using PubMed literature. A precision of 76.7% has been achieved which is significant considering the involvement of a novel disease like Covid-19. The generated interdependencies can be further explored by medical professionals and caregivers in order to plan healthy eating patterns for Covid-19 patients. The proposed approach can also be utilized for finding beneficial diets for different combinations of comorbidities with Covid-19 as per the underlying health conditions of a patient. Graphical abstract., (© 2022. International Federation for Medical and Biological Engineering.)