Lightweight Virtualization in Cloud Computing for Research

Kacamarga, Muhamad Fitra; Pardamean, Bens; Wijaya, Hari

doi:10.1007/978-3-662-46742-8_40

Cited by 18 publications

(6 citation statements)

References 5 publications

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“…36) and the Rstudio-0.99.902 (ref. 37) web interface in addition to a custom R library for regression studies in a Docker container 38 . The packaged environment is publically available on Docker hub (https://hub.docker.com/r/chiragjp/nhanes_scidata/) and can be consistently deployed across local or cloud-based environments.…”

Section: Usage Notesmentioning

confidence: 99%

A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey

et al. 2016

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The National Health and Nutrition Examination Survey (NHANES) is a population survey implemented by the Centers for Disease Control and Prevention (CDC) to monitor the health of the United States whose data is publicly available in hundreds of files. This Data Descriptor describes a single unified and universally accessible data file, merging across 255 separate files and stitching data across 4 surveys, encompassing 41,474 individuals and 1,191 variables. The variables consist of phenotype and environmental exposure information on each individual, specifically (1) demographic information, physical exam results (e.g., height, body mass index), laboratory results (e.g., cholesterol, glucose, and environmental exposures), and (4) questionnaire items. Second, the data descriptor describes a dictionary to enable analysts find variables by category and human-readable description. The datasets are available on DataDryad and a hands-on analytics tutorial is available on GitHub. Through a new big data platform, BD2K Patient Centered Information Commons (http://pic-sure.org), we provide a new way to browse the dataset via a web browser (https://nhanes.hms.harvard.edu) and provide application programming interface for programmatic access.

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Section: Usage Notesmentioning

confidence: 99%

A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey

et al. 2016

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“…Many organizations encounter difficulties in acquiring and maintaining the vast information technology (IT) infrastructure needed for their daily business operations. Some of these organizations are in public and some of them are in private sectors, such as schools, hospitals, retailers and government agencies (Kacamarga et al , 2015; Oduh et al , 2018). The organizations experience difficulties mainly in acquiring, hosting, maintaining and supporting the required IT systems, applications and infrastructure that drive the alignment of their IT objectives with business objectives that leads to efficient business operations (Wilkin et al , 2016; Islam et al , 2017).…”

Section: Introductionmentioning

confidence: 99%

A multifaceted framework for adoption of cloud computing in Malaysian SMEs

Asiaei

Rahim

2019

JSTPM

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Purpose The purpose of this study is to develop a model to understand the relationships among technology, organizational and environmental (TOE) contexts, intention to adopt cloud computing (IACC) and actual usage of cloud computing (AUCC) in small and medium enterprises (SMEs) in Malaysia as a developing country. More specifically, this paper seeks to explore the mediation effect of IACC on the relationship between TOE context and AUCC. Design/methodology/approach A positivist research approach was selected for this study. Drawing largely upon the TOE framework, this study uses survey data from 209 Malaysian SMEs. Structural equation modelling (SEM) based on partial least squares (PLS) was used to assess the structural relations of the research model. Findings The results of the structural model show that data security, technology readiness, top management support, competitive pressure and innovativeness are the most significant factors in predicting the adoption of cloud computing in Malaysian SMEs. Further, the results indicate that intention to adopt cloud computing can play a mediating role between TOE factors and the actual usage of cloud computing. Research limitations/implications The focus upon Malaysian SMEs may diminish the generalizability of the findings. This study provides profound insight into the management and foundation of cloud computing, different types of cloud services and deployment models that could facilitate the management of enterprise strategic resources and contribute to the performance improvement. This study also provides another important implication for practitioners regarding the absolute necessity of value drivers’ identification within enterprise and understand the causal relationships, which are vital in driving those values. Practical implications This study provides several practical guidance for practitioners in deploying cloud services which are most suitable option for their specific technology requirement in their enterprise to enjoy the full benefits of their intangible assets. Another significant implication of this study lies in the fact that it may require a different emphasis on nature and adoption design when there is a higher level of stress on technology-related and cloud computing resources. Originality/value This study contributes to the extant literature by developing an integrative model to identify how a wide set of contextual factors can determine the intention to adopt cloud computing and, in turn, influence the actual usage of cloud computing in SMEs in Malaysia as a developing country.

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“…If the next population size is equal to n, it will be returned as the actual next population. However, if the size of combined fronts is greater than n, the first front up to (i − 1)th front are combined, and the remaining individuals are picked from the ith front by selecting the most elite ones as shown in lines [11][12][13][14][15][16][17][18][19]. Selecting the remaining individuals is done after going through three operators: normalization, association, and niching.…”

Section: Constraint Violation Valuementioning

confidence: 99%

“…However, if the size of combined fronts is greater than n, the first front up to (i − 1)th front are combined, and the remaining individuals are picked from the ith front by selecting the most elite ones as shown in lines[11][12][13][14][15][16][17][18][19]. However, if the size of combined fronts is greater than n, the first front up to (i − 1)th front are combined, and the remaining individuals are picked from the ith front by selecting the most elite ones as shown in lines[11][12][13][14][15][16][17][18][19].…”

mentioning

confidence: 99%

Optimizing scheduling decisions of container management tool using many‐objective genetic algorithm

Imdoukh

Ahmad

Alfailakawi

2019

Concurrency and Computation

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Container virtualization is an emerging technology in cloud computing mainly due to its portability and lightweight features. Scheduling is a key task, performed by container management tool, which indirectly affects the characteristics of distributed software system in terms of availability, realizability, scalability, resources utilization, as well as power consumption. However, current schedulers only focus on some of the aforementioned aspects but not all. In this paper, a Many-Objective Genetic Algorithm Scheduler (MOGAS) is proposed to handle all such objectives to realize solutions with better characteristics. The proposed scheduler is compared with the Ant Colony Optimization (ACO)-based scheduler. Based on the proposed objective functions, simulation results show that MOGAS is better than the ACO scheduler in equally distributing tasks by 50%, assigning unique set of tasks per node by 40%, and reducing power consumption by 7%, on average.

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Lightweight Virtualization in Cloud Computing for Research

Cited by 18 publications

References 5 publications

A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey

A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey

A multifaceted framework for adoption of cloud computing in Malaysian SMEs

Optimizing scheduling decisions of container management tool using many‐objective genetic algorithm

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