scholarly journals Software-Defined Multi-cloud Computing: A Vision, Architectural Elements, and Future Directions

2018 ◽  
pp. 3-18 ◽  
Author(s):  
Rajkumar Buyya ◽  
Jungmin Son
Keyword(s):  
Cloud Computing ◽  
Future Directions ◽  
Architectural Elements ◽  
Multi Cloud

scholarly journals Ontologies in Cloud Computing—Review and Future Directions

2021 ◽  
Vol 13 (12) ◽  
pp. 302
Author(s):  
JohnBosco Agbaegbu ◽  
Oluwasefunmi Tale Arogundade ◽  
Sanjay Misra ◽  
Robertas Damaševičius
Keyword(s):  
Cloud Computing ◽  
Selection Criteria ◽  
Semantic Analysis ◽  
Cloud Services ◽  
Computing Environment ◽  
Service Description ◽  
Future Directions ◽  
Business Operations ◽  
Cloud Interoperability ◽  
Multi Cloud

Cloud computing as a technology has the capacity to enhance cooperation, scalability, accessibility, and offers discount prospects using improved and effective computing, and this capability helps organizations to stay focused. Ontologies are used to model knowledge. Once knowledge is modeled, knowledge management systems can be used to search, match, visualize knowledge, and also infer new knowledge. Ontologies use semantic analysis to define information within an environment with interconnecting relationships between heterogeneous sets. This paper aims to provide a comprehensive review of the existing literature on ontology in cloud computing and defines the state of the art. We applied the systematic literature review (SLR) approach and identified 400 articles; 58 of the articles were selected after further selection based on set selection criteria, and 35 articles were considered relevant to the study. The study shows that four predominant areas of cloud computing—cloud security, cloud interoperability, cloud resources and service description, and cloud services discovery and selection—have attracted the attention of researchers as dominant areas where cloud ontologies have made great impact. The proposed methods in the literature applied 30 ontologies in the cloud domain, and five of the methods are still practiced in the legacy computing environment. From the analysis, it was found that several challenges exist, including those related to the application of ontologies to enhance business operations in the cloud and multi-cloud. Based on this review, the study summarizes some unresolved challenges and possible future directions for cloud ontology researchers.

Task Scheduling for Multi-Cloud Computing Subject to Security and Reliability Constraints

2021 ◽  
Vol 8 (4) ◽  
pp. 848-865
Author(s):  
Qing-Hua Zhu ◽  
Huan Tang ◽  
Jia-Jie Huang ◽  
Yan Hou
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Security And Reliability ◽  
Multi Cloud

PrEstoCloud

2021 ◽  
Vol 34 (1) ◽  
pp. 66-85
Author(s):  
Yiannis Verginadis ◽  
Dimitris Apostolou ◽  
Salman Taherizadeh ◽  
Ioannis Ledakis ◽  
Gregoris Mentzas ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Software Engineering ◽  
Response Time ◽  
Fog Computing ◽  
Data Sources ◽  
Data Intensive ◽  
Service Response Time ◽  
Enabling Services ◽  
Data Intensive Applications ◽  
Multi Cloud

Fog computing extends multi-cloud computing by enabling services or application functions to be hosted close to their data sources. To take advantage of the capabilities of fog computing, serverless and the function-as-a-service (FaaS) software engineering paradigms allow for the flexible deployment of applications on multi-cloud, fog, and edge resources. This article reviews prominent fog computing frameworks and discusses some of the challenges and requirements of FaaS-enabled applications. Moreover, it proposes a novel framework able to dynamically manage multi-cloud, fog, and edge resources and to deploy data-intensive applications developed using the FaaS paradigm. The proposed framework leverages the FaaS paradigm in a way that improves the average service response time of data-intensive applications by a factor of three regardless of the underlying multi-cloud, fog, and edge resource infrastructure.

Integration of Cognitive Radio Sensor Networks and Cloud Computing

2014 ◽  
pp. 288-312 ◽  
Author(s):  
Yasir Saleem ◽  
Farrukh Salim ◽  
Mubashir Husain Rehmani
Keyword(s):  
Cloud Computing ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Resource Limitation ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Future Directions ◽  
Cognitive Radio Sensor Networks ◽  
Computational Speed ◽  
Is Integration

Cognitive Radio Sensor Networks (CRSNs) are composed of sensor nodes equipped with Cognitive Radio (CR) technology with limited resources (e.g., storage, computational speed, bandwidth, security, etc.). In order to overcome resource limitation, cognitive radio sensor nodes are integrated with cloud computing, which provides computing resources (e.g., storage, computation, security, etc.) to sensor nodes. Therefore, the focus of this chapter is integration of cognitive radio sensor networks with cloud computing. In this chapter, the authors first provide background on cloud computing, cognitive radio networks, wireless sensor networks, and cognitive radio sensor networks. This chapter also describes benefits of this integration to both cognitive radio sensor networks and cloud computing, followed by advantages of using cloud computing in cognitive radio sensor networks. Furthermore, it provides applications of cloud-based cognitive radio sensor networks. In the end, the authors provide some issues, challenges, and future directions for such integration.

Integration of Cognitive Radio Sensor Networks and Cloud Computing

2015 ◽  
pp. 1025-1048
Author(s):  
Yasir Saleem ◽  
Farrukh Salim ◽  
Mubashir Husain Rehmani
Keyword(s):  
Cloud Computing ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Resource Limitation ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Future Directions ◽  
Cognitive Radio Sensor Networks ◽  
Computational Speed ◽  
Is Integration

Cognitive Radio Sensor Networks (CRSNs) are composed of sensor nodes equipped with Cognitive Radio (CR) technology with limited resources (e.g., storage, computational speed, bandwidth, security, etc.). In order to overcome resource limitation, cognitive radio sensor nodes are integrated with cloud computing, which provides computing resources (e.g., storage, computation, security, etc.) to sensor nodes. Therefore, the focus of this chapter is integration of cognitive radio sensor networks with cloud computing. In this chapter, the authors first provide background on cloud computing, cognitive radio networks, wireless sensor networks, and cognitive radio sensor networks. This chapter also describes benefits of this integration to both cognitive radio sensor networks and cloud computing, followed by advantages of using cloud computing in cognitive radio sensor networks. Furthermore, it provides applications of cloud-based cognitive radio sensor networks. In the end, the authors provide some issues, challenges, and future directions for such integration.

Multi-Cloud Data Management Using Shamir's Secret Sharing and Quantum Byzantine Agreement Schemes

2016 ◽  
pp. 1205-1222
Author(s):  
Mohammed A. AlZain ◽  
Alice S. Li ◽  
Ben Soh ◽  
Eric Pardede
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Data Management ◽  
Data Storage ◽  
Secret Sharing ◽  
Byzantine Agreement ◽  
Management Model ◽  
Cloud Data ◽  
Cloud Data Management ◽  
Multi Cloud

Cloud computing is a phenomenal distributed computing paradigm that provides flexible, low-cost on-demand data management to businesses. However, this so-called outsourcing of computing resources causes business data security and privacy concerns. Although various methods have been proposed to deal with these concerns, none of these relates to multi-clouds. This paper presents a practical data management model in a public and private multi-cloud environment. The proposed model BFT-MCDB incorporates Shamir's Secret Sharing approach and Quantum Byzantine Agreement protocol to improve trustworthiness and security of business data storage, without compromising performance. The performance evaluation is carried out using a cloud computing simulator called CloudSim. The experimental results show significantly better performance in terms of data storage and data retrieval compared to other common cloud cryptographic based models. The performance evaluation based on CloudSim experiments demonstrates the feasibility of the proposed multi-cloud data management model.

Optimal Resource Usage in Multi-Cloud Computing Environment

2013 ◽  
Vol 3 (1) ◽  
pp. 44-57 ◽  
Author(s):  
Veena Goswami ◽  
Choudhury Nishkanta Sahoo
Keyword(s):  
Cloud Computing ◽  
Service Providers ◽  
Queueing Network ◽  
Computing Environment ◽  
New Paradigm ◽  
Cloud Computing Environment ◽  
Optimal Resource ◽  
Optimal Resource Management ◽  
And Performance ◽  
Multi Cloud

Cloud computing has emerged as a new paradigm for accessing distributed computing resources such as infrastructure, hardware platform, and software applications on-demand over the internet as services. This paper presents an optimal resource management framework for multi-cloud computing environment. The authors model the behavior and performance of applications to integrate different service-providers for end-to-end-requirements. Each service model caters to specific type of requirements and there are already number of players with own customized products/services offered. Intercloud Federation and Service delegation models are part of Multi-Cloud environment where the broader target is to achieve infinite pool of resources. They propose an analytical queueing network model to improve the efficiency of the system. Numerical results indicate that the proposed provisioning technique detects changes in arrival pattern, resource demands that occur over time and allocates multiple virtualized IT resources accordingly to achieve application Quality of Service targets.

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