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Cloud computing–based big data processing and intelligent analytics
Author(s) -
Dong Fang,
Wu Chenshu,
Gao Shangce
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5531
Subject(s) - cloud computing , computer science , big data , edge computing , distributed computing , server , software deployment , analytics , service provider , data processing , latency (audio) , data science , service (business) , computer network , operating system , telecommunications , economy , economics
Cloud and big data have become the big things today in many systems especially regarding of information processing and intelligent analytics. Big data analytics is the use of advanced analytic techniques against very large, diverse data sets, and it allows analysts, researchers to make better decisions using data that was previously inaccessible or unusable. Due to the urgent demand on high capacity of computation and storage resources, cloud computing has been acknowledged as the primary computing paradigm for massive data storage, processing under various circumstances and different requirement.1 Moreover, edge computing pushes the cloud frontier to the edge of the network and extends cloud computing to be able to address more application scenarios. This special track plans to solicit novel and original manuscripts in the above topics with an emphasize on ‘‘Cloud Computing–based Big Data Processing and Intelligent Analytics.’’ From those submitted papers for the 6th International Conference on Advanced Cloud and Big Data (CBD 2018) held in Lanzhou, China on August 12 to August 14, 2018, nine papers are selected that target the following research issues in cloud computing and big data: • Service deployment and task scheduling in cloud computing and edge computing. • Cloud storage system design and optimization. • Case studies of big data in cloud-based system. • Network performance optimization in data centers. • Approximate big data analysis and processing. • Security threats and solutions in cloud computing and big data processing.