Year: 2017
Type: dissertation
Abstract: Over the past few years, cloud computing has been more and more attractive as a new \ncomputing paradigm due to high flexibility for provisioning on-demand computing \nresources that are used as services through the Internet. The issues around cloud service \ndiscovery have considered by many researchers in the recent years. However, \nin cloud computing, with the highly dynamic, distributed, the lack of standardized \ndescription languages, diverse services offered at different levels and non-transparent \nnature of cloud services, this research area has gained a significant attention. Robust \ncloud service discovery approaches will assist the promotion and growth of cloud \nservice customers and providers, but will also provide a meaningful contribution to \nthe acceptance and development of cloud computing. In this dissertation, we have \nproposed an automated cloud service discovery approach of cloud services. We have \nalso conducted extensive experiments to validate our proposed approach. The results \ndemonstrate the applicability of our approach and its capability of effectively identifying \nand categorizing cloud services on the Internet. Firstly, we develop a novel \napproach to build cloud service ontology. Cloud service ontology initially is built \nbased on the National Institute of Standards and Technology (NIST) cloud computing \nstandard. Then, we add new concepts to ontology by automatically analyzing real \ncloud services based on cloud service ontology Algorithm. We also propose cloud \nservice categorization that use Term Frequency to weigh cloud service ontology concepts \nand calculate cosine similarity to measure the similarity between cloud services. \nThe cloud service categorization algorithm is able to categorize cloud services to clusters for effective categorization of cloud services. In addition, we use Machine \nLearning techniques to identify cloud service in real environment. Our cloud service \nidentifier is built by utilizing cloud service features extracted from the real cloud service \nproviders. We determine several features such as similarity function, semantic \nontology, cloud service description and cloud services components, to be used effectively \nin identifying cloud service on the Web. Also, we build a unified model to \nexpose the cloud service’s features to a cloud service search user to ease the process of \nsearching and comparison among a large amount of cloud services by building cloud \nservice’s profile. Furthermore, we particularly develop a cloud service discovery Engine \nthat has capability to crawl the Web automatically and collect cloud services. \nThe collected datasets include meta-data of nearly 7,500 real-world cloud services \nproviders and nearly 15,000 services (2.45GB). The experimental results show that \nour approach i) is able to effectively build automatic cloud service ontology, ii) is \nrobust in identifying cloud service in real environment and iii) is more scalable in \nproviding more details about cloud services.
Author: Abdullah Alfazi
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2944023662