Due to the increasing number of cloud applications,the amount of data in the cloud shows signs of growing faster than ever *** nature of cloud computing requires cloud data processing systems that can handle huge volu...
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Due to the increasing number of cloud applications,the amount of data in the cloud shows signs of growing faster than ever *** nature of cloud computing requires cloud data processing systems that can handle huge volumes of data and have high ***,most cloud storage systems currently adopt a hash-like approach to retrieving data that only supports simple keyword-based enquiries,but lacks various forms of information ***,a scalable and efficient indexing scheme is clearly *** this paper,we present a skip list-based cloud index,called SLC-index,which is a novel,scalable skip list-based indexing for clouddata *** SLC-index offers a two-layered architecture for extending indexing scope and facilitating better *** load-balancing for the SLC-index is achieved by online migration of index nodes between ***,it is a flexible system due to its dynamic addition and removal of *** SLC-index is efficient for both point and range *** results show the efficiency of the SLC-index and its usefulness as an alternative approach for cloud-suitable data structures.
In this article, the author describes the history of the development, modern state, and future considerations of cloud (diffused) computing as one of the modern innovative technologies. The models of cloud computing a...
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In this article, the author describes the history of the development, modern state, and future considerations of cloud (diffused) computing as one of the modern innovative technologies. The models of cloud computing and its advantages and disadvantages are analyzed. A number of cloud operating systems, cloud computing vendors, and the capabilities of their platforms are considered.
With the increased push to promote data-driven methods in modern healthcare, there is a tremendous need for fast access to clinical datasets in order to pursue medical breakthroughs in the areas of personalized medici...
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ISBN:
(纸本)9781665404242
With the increased push to promote data-driven methods in modern healthcare, there is a tremendous need for fast access to clinical datasets in order to pursue medical breakthroughs in the areas of personalized medicine and big data knowledge discovery. However, the inherent lack of trust between the data custodians and data consumers/users has resulted in a fully manual honest broker approach to access and process protected healthcare data. Such a manual approach leads to slow data handling, and adds to overheads needed to address data auditability and assurance needed for compliance with healthcare data security standards. In this paper, we address these challenges by proposing a trust model to enable semi-automation of the honest broker process to increase its efficiency. The trust model is based on multi-dimensional risk management principles and considers risk associated with data identifiers, as well as requestor profile and reputation. We implement and evaluate a semi-automated honest broker that uses our trust model in a community cloud testbed using the SynPUF synthetic dataset. Our experiment results show that our multidimensional risk management approach consistently identifies the lower confidentiality risk configuration in the semi-automation in comparison with a one-dimensional strategy. Thus, our semi-automated honest brokering approach improves efficiency for data custodians and data consumers by facilitation of fast and secure data access, while also ensuring compliance in the processing of the protected datasets.
In cloud event processing, data generated at the edge is processed in real-time by cloud resources. Both distributed stream processing (DSP) and Function-as-a-Service (FaaS) have been proposed to implement such event ...
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ISBN:
(数字)9781665491150
ISBN:
(纸本)9781665491150
In cloud event processing, data generated at the edge is processed in real-time by cloud resources. Both distributed stream processing (DSP) and Function-as-a-Service (FaaS) have been proposed to implement such event processing applications. FaaS emphasizes fast development and easy operation, while DSP emphasizes efficient handling of large data volumes. Despite their architectural differences, both can be used to model and implement loosely-coupled job graphs. In this paper, we consider the selection of FaaS and DSP from a cost perspective. We implement stateless and stateful workflows from the Theodolite benchmarking suite using cloud FaaS and DSP. In an extensive evaluation, we show how application type, cloud service provider, and runtime environment can influence the cost of application deployments and derive decision guidelines for cloud engineers.
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