{"title":"未来动态计算的疾病驱动模型","authors":"Dephney Mathebula","doi":"10.1109/icABCD59051.2023.10220487","DOIUrl":null,"url":null,"abstract":"Future computing entities should be capable of accessing computing resources for data-intensive algorithm execution. This should be realizable in operational contexts where internet accessibility to cloud contexts becomes challenging. Such a scenario describes developing contexts. In addition, future computing entities also make use of multiple operating systems in a context where the computing resources are reduced due to the use of partitions. The use of partitions is recognized to reduce the number of accessible computing resources and increase the overhead associated with computing resource allocation. The presented research proposes an architecture where an operating system is logically stowed and selectively activated without involving the use of partition. This frees up the number of computing resources previously locked in different partition systems and reduces the computing resource overhead. Analysis shows that the proposed framework increases the accessible computing resources by 14.6% on average. In addition, the computing resource overhead is reduced by 21 % on average.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Disease Motivated Model for Future Dynamic Computing\",\"authors\":\"Dephney Mathebula\",\"doi\":\"10.1109/icABCD59051.2023.10220487\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Future computing entities should be capable of accessing computing resources for data-intensive algorithm execution. This should be realizable in operational contexts where internet accessibility to cloud contexts becomes challenging. Such a scenario describes developing contexts. In addition, future computing entities also make use of multiple operating systems in a context where the computing resources are reduced due to the use of partitions. The use of partitions is recognized to reduce the number of accessible computing resources and increase the overhead associated with computing resource allocation. The presented research proposes an architecture where an operating system is logically stowed and selectively activated without involving the use of partition. This frees up the number of computing resources previously locked in different partition systems and reduces the computing resource overhead. Analysis shows that the proposed framework increases the accessible computing resources by 14.6% on average. In addition, the computing resource overhead is reduced by 21 % on average.\",\"PeriodicalId\":51314,\"journal\":{\"name\":\"Big Data\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-08-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Big Data\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1109/icABCD59051.2023.10220487\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/icABCD59051.2023.10220487","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Disease Motivated Model for Future Dynamic Computing
Future computing entities should be capable of accessing computing resources for data-intensive algorithm execution. This should be realizable in operational contexts where internet accessibility to cloud contexts becomes challenging. Such a scenario describes developing contexts. In addition, future computing entities also make use of multiple operating systems in a context where the computing resources are reduced due to the use of partitions. The use of partitions is recognized to reduce the number of accessible computing resources and increase the overhead associated with computing resource allocation. The presented research proposes an architecture where an operating system is logically stowed and selectively activated without involving the use of partition. This frees up the number of computing resources previously locked in different partition systems and reduces the computing resource overhead. Analysis shows that the proposed framework increases the accessible computing resources by 14.6% on average. In addition, the computing resource overhead is reduced by 21 % on average.
Big DataCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍:
Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions.
Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government.
Big Data coverage includes:
Big data industry standards,
New technologies being developed specifically for big data,
Data acquisition, cleaning, distribution, and best practices,
Data protection, privacy, and policy,
Business interests from research to product,
The changing role of business intelligence,
Visualization and design principles of big data infrastructures,
Physical interfaces and robotics,
Social networking advantages for Facebook, Twitter, Amazon, Google, etc,
Opportunities around big data and how companies can harness it to their advantage.