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Item type:Publication, Efficient fault tolerance on cloud environments(IGI Global, 2021-01-01)With mission critical web applications and resources being hosted on cloud environments, and cloud services growing fast, the need for having greater level of service assurance regarding fault tolerance for availability and reliability has increased. The high priority now is ensuring a fault tolerant environment that can keep the systems up and running. To minimize the impact of downtime or accessibility failure due to systems, network devices or hardware, the expectations are that such failures need to be anticipated and handled proactively in fast, intelligent way. This article discusses the fault tolerance system for cloud computing environments, analyzes whether this is effective for Cloud environments. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2(Center for Open Science, 2022-09-24)Accurately characterizing clouds and their shadows is a long-standing problem in the Earth Observation community. Recent works showcase the necessity to improve cloud detection methods for imagery acquired by the Sentinel-2 satellites. However, the lack of consensus and transparency in existing reference datasets hampers the benchmarking of current cloud detection methods. Exploiting the analysis-ready data offered by the Copernicus program, we created CloudSEN12, a new multi-temporal global dataset to foster research in cloud and cloud shadow detection. CloudSEN12 has 49,400 image patches, including (1) Sentinel-2 level-1C and level-2A multi-spectral data, (2) Sentinel-1 synthetic aperture radar data, (3) auxiliary remote sensing products, (4) different hand-crafted annotations to label the presence of thick and thin clouds and cloud shadows, and (5) the results from eight state-of-the-art cloud detection algorithms. At present, CloudSEN12 exceeds all previous efforts in terms of annotation richness, scene variability, geographic distribution, metadata complexity, quality control, and number of samples. The dataset is made publicly available at https://cloudsen12.github.io/. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A distributed N-FINDR cloud computing-based solution for endmembers extraction on large-scale hyperspectral remote sensing data(MDPI, 2022-05-01)In this work, we introduce a novel, distributed version of the N-FINDR endmember extraction algorithm, which is able to exploit computer cluster resources in order to efficiently process large volumes of hyperspectral data. The implementation of the distributed algorithm was done by extending the InterCloud Data Mining Package, originally adopted for land cover classification, through the HyperCloud-RS framework, here adapted for endmember extraction, which can be executed on cloud computing environments, allowing users to elastically administer processing power and storage space for adequately handling very large datasets. The framework supports distributed execution, network communication, and fault tolerance, transparently and efficiently to the user. The experimental analysis addresses the performance issues, evaluating both accuracy and execution time, over the processing of different synthetic versions of the AVIRIS Cuprite hyperspectral dataset, with 3.1 Gb, 6.2 Gb, and 15.1Gb respectively, thus addressing the issue of dealing with large-scale hyperspectral data. As a further contribution of this work, we describe in detail how to extend the HyperCloud-RS framework by integrating other endmember extraction algorithms, thus enabling researchers to implement algorithms specifically designed for their own assessment. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Application of cloud computing technology in computer secure storage(Hindawi Limited, 2022-01-01)To investigate the use of cloud computing technologies in safe computer storage, firstly, it is proposed to complete the central control function by building a cloud computing data center, collect multiple platforms and network safety technologies, and then connect computers in unlike sites to confirm computer information security. Then, based on the implementation advantages of cloud computing technique in computer network safe storage, specific applications are analyzed. Finally, a cloud computing secure modeling and analysis idea based on multiqueue and multiserver is proposed. The proposed cloud security approach ensures that both data and applications are easily accessible to authorized users. One always has a consistent way to access your cloud data and applications, allowing you to address any potential security issues as soon as they arise. It has greatly improved computer security storage convenience while also greatly improving computer network storage security. After verification, with the cloud computing technology platform to carry out relevant businesses at any time, the operation effectiveness has been meaningfully enhanced by 80%. At the same time, it promotes the construction of information sharing and gives full performance to the benefits of hardware, accelerates the process of resource integration, and provides information support for the formulation of enterprise strategic plans. Combined with the actual situation, the current study discusses the development and application direction of cloud computing, so as to add new impetus to the economic growth of enterprises. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Advances in resource management through the integration of distributed computing approaches(Wiley, 2024-03-08)With a focus on grid computing, cloud computing, and edge computing (EC), this chapter analyzes the integration of distributed computing systems for resource management. These methods present a number of advantages, including increased flexibility, scalability, and analysis and processing of data capabilities, but they also present difficulties, including efficient resource scheduling, management, and monitoring, as well as issues with data security and management. To address these challenges, researchers are developing new techniques and tools for managing and allocating resources across distributed computing environments. These include resource allocation and scheduling algorithms, monitoring and management tools, and security and data management techniques. The chapter also discusses emerging trends and future research directions in the field, including the increasing use of EC and the need for more efficient and effective resource allocation and scheduling algorithms. The integration of distributed computing approaches for resource management has the potential to transform computing infrastructure for organizations. By leveraging the resources of multiple systems and networks, organizations can scale their computing infrastructure, improve data processing and analysis capabilities, and improve overall efficiency and cost savings. Although there are difficulties to be overcome, the advantages of these technologies make them a crucial field for future research and development. the requirement for ongoing study and development in this field to solve problems and enhance methods and tools for resource management in distributed computing settings.