• Electronics and Telecommunications Trends
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• v.35 no.3
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• pp.45-54
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• 2020

The cloud computing industry has focused on establishing a cloud-based business environment for enterprises with efforts to convert using their own on-premise computing infrastructures to using cloud services. With these efforts, using cloud services has become natural, especially for the IT industry. The cloud computing industry is moving toward proliferation of the cloud computing environment into various evolving industries. Along with industrial trends, new technical trends such as edge computing and multi-cloud are emerging. These trends are expected to create new business models and develop related service ecosystems, providing new opportunities for service providers and new experiences for users. A mong those emerging technologies, multi-cloud technology is expected to realize unlimited global cloud computing resources by unifying cloud resources from multiple public cloud service providers. In this paper, we introduce the concept and related trends of multi-cloud technology. Subsequently, we analyze the main functionalities and several use cases of multi-cloud technology. Finally, we summarize the effects and usefulness of multi-cloud technology in the domestic cloud industry.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.75-94
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• 2021

The 4th industrial revolution needs a fusion of artificial intelligence, robotics, the Internet of Things (IoT), edge computing, and other technologies. For the fusion of technologies, cloud computing technology can provide flexible and high-performance computing resources so that cloud computing can be the foundation technology of new emerging services. The emerging services become a global-scale, and require much higher performance, availability, and reliability. Public cloud providers already provide global-scale services. However, their services, costs, performance, and policies are different. Enterprises/ developers to come out with a new inter-operable service are experiencing vendor lock-in problems. Therefore, multi-cloud technology that federatively resolves the limitations of single cloud providers is required. We propose a software platform, denoted as Cloud-Barista. Cloud-Barista is a multi-cloud service common platform for federating multiple clouds. It makes multiple cloud services as a single service. We explain the functional architecture of the proposed platform that consists of several frameworks, and then discuss the main design and implementation issues of each framework. To verify the feasibility of our proposal, we show a demonstration which is to create 18 virtual machines on several cloud providers, combine them as a single resource, and manage it.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.1-8
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• 2021

Cloud computing has proven its efficiency, especially after the increasing number of cloud services offered by a wide range of cloud providers, from different domains. Despite, these cloud services are mostly heterogeneous. Consequently, and due to the rising interest of cloud consumers to adhere to a multi-cloud environment instead of being locked-in to one cloud provider, the need for semantically interconnecting different cloud services from different cloud providers is a crucial and important task to ensure. In addition, considerable research efforts proposed interoperability solutions leading to different representation models of cloud services. In this work, we present our solution to overcome this limitation, precisely in the IAAS service model. This solution is a framework permitting the semantic interoperability of different IAAS resources in a multi-cloud environment, in order to assist cloud consumers to retrieve the cloud resource that meets specific requirements.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2849-2864
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• 2021

The emerging of cloud data sharing can create great values, especially in multi-cloud environments. However, "data island" between different cloud service providers (CSPs) has drawn trust problem in data sharing, causing contradictions with the increasing sharing need of cloud data users. And how to ensure the data value for both data owner and data user before sharing, is another challenge limiting massive data sharing in the multi-cloud environments. To solve the problems above, we propose a Uniform Encoding and Parsing Framework (UEPF) with blockchain to support trustworthy and valuable data sharing. We design namespace-based unique identifier pair to support data description corresponding with data in multi-cloud, and build a blockchain-based data encoding protocol to manage the metadata with identifier pair in the blockchain ledger. To share data in multi-cloud, we build a data parsing protocol with smart contract to query and get the sharing cloud data efficiently. We also build identifier updating protocol to satisfy the dynamicity of data, and data check protocol to ensure the validity of data. Theoretical analysis and experiment results show that UEPF is pretty efficient.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1100-1122
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• 2023

Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a large- scale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.798-807
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• 2020

As technologies using digital camera have been developed, 3D images can be constructed from the pictures captured by using multiple cameras. The 3D image data is represented in a form of point cloud which consists of 3D coordinate of the data and the related attributes. Various techniques have been proposed to construct the point cloud data. Among them, Structure-from-Motion (SfM) and Multi-view Stereo (MVS) are examples of the image-based technologies in this field. Based on the conventional research, the point cloud data generated from SfM and MVS may be sparse because the depth information may be incorrect and some data have been removed. In this paper, we propose an efficient algorithm to enhance the point cloud so that the density of the generated point cloud increases. Simulation results show that the proposed algorithm outperforms the conventional algorithms objectively and subjectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3293-3311
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• 2015

Cloud services are required to be composed as a single service to fulfill the workflow applications. Service composition in Cloud raises new challenges caused by the diversity of users with different QoS requirements and vague preferences, as well as the development of cloud computing having geographically distributed characteristics. So the selection of the best service composition is a complex problem and it faces trade-off among various QoS criteria. In this paper, we propose a Cloud service composition approach based on evolutionary algorithms, i.e., NSGA-II and MOPSO. We utilize the combination of multi-objective evolutionary approaches and Decision-Making method (AHP) to solve Cloud service composition optimization problem. The weights generated from AHP are applied to the Crowding Distance calculations of the above two evolutionary algorithms. Our algorithm beats single-objective algorithms on the optimization ability. And compared with general multi-objective algorithms, it is able to precisely capture the users' preferences. The results of the simulation also show that our approach can achieve a better scalability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.18-38
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• 2017

Infrastructure-as-a-Service (IaaS) clouds provide infrastructure as a pool of virtual resources, and the public IaaS clouds, e.g. Amazon Web Service (AWS) and private IaaS cloud toolkits, e.g. OpenStack, CloudStack, etc. provide their own application programming interfaces (APIs) for managing the cloud resources they offer. The heterogeneity of the APIs, however, makes it difficult to access and use the multiple cloud services concurrently and collectively. In this paper, we explore previous efforts to solve this problem and present our own implementation of an integrated cloud API, which can make it possible to access and use multiple clouds collectively in a uniform way. The implemented API provides a RESTful access and hides underlying cloud infrastructures from users or applications. We show the implementation details of the integrated API and performance evaluation of it comparing the proprietary APIs based on our cloud testbed. From the evaluation results, we could conclude that the overhead imposed by our interface is negligibly small and can be successfully used for multi-cloud access.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.327-341
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• 2022

Cloud removal is often required to construct time-series sets of optical images for environmental monitoring. In regression-based cloud removal, the selection of an appropriate regression model and the impact analysis of the input images significantly affect the prediction performance. This study evaluates the potential of Gaussian process (GP) regression for cloud removal and also analyzes the effects of cloud-free optical images and spectral bands on prediction performance. Unlike other machine learning-based regression models, GP regression provides uncertainty information and automatically optimizes hyperparameters. An experiment using Sentinel-2 multi-spectral images was conducted for cloud removal in the two agricultural regions. The prediction performance of GP regression was compared with that of random forest (RF) regression. Various combinations of input images and multi-spectral bands were considered for quantitative evaluations. The experimental results showed that using multi-temporal images with multi-spectral bands as inputs achieved the best prediction accuracy. Highly correlated adjacent multi-spectral bands and temporally correlated multi-temporal images resulted in an improved prediction accuracy. The prediction performance of GP regression was significantly improved in predicting the near-infrared band compared to that of RF regression. Estimating the distribution function of input data in GP regression could reflect the variations in the considered spectral band with a broader range. In particular, GP regression was superior to RF regression for reproducing structural patterns at both sites in terms of structural similarity. In addition, uncertainty information provided by GP regression showed a reasonable similarity to prediction errors for some sub-areas, indicating that uncertainty estimates may be used to measure the prediction result quality. These findings suggest that GP regression could be beneficial for cloud removal and optical image reconstruction. In addition, the impact analysis results of the input images provide guidelines for selecting optimal images for regression-based cloud removal.

• Journal of Communications and Networks
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• v.16 no.6
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• pp.592-599
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• 2014

Cloud computing enables users to easily store their data and simply share data with others. Due to the security threats in an untrusted cloud, users are recommended to compute verification metadata, such as signatures, on their data to protect the integrity. Many mechanisms have been proposed to allow a public verifier to efficiently audit cloud data integrity without receiving the entire data from the cloud. However, to the best of our knowledge, none of them has considered about the efficiency of public verification on multi-owner data, where each block in data is signed by multiple owners. In this paper, we propose a novel public verification mechanism to audit the integrity of multi-owner data in an untrusted cloud by taking the advantage of multisignatures. With our mechanism, the verification time and storage overhead of signatures on multi-owner data in the cloud are independent with the number of owners. In addition, we demonstrate the security of our scheme with rigorous proofs. Compared to the straightforward extension of previous mechanisms, our mechanism shows a better performance in experiments.