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This work develops a cloud-fog computing architecture for information-centric Internet-of-Things (IoT) applications with job classification and resource scheduling functions. The two designed functions support quality of service (QoS) by classifying IoT applications and scheduling computing resources. An innovative scheduling mechanism is developed to optimize the dispatch of cloud and fog resources...
With rapid growth of demand on big data storage, MongoDB has been a prevalent choice to store unstructured data in recent years. MongoDB evenly distributes data across shard servers to ensure that all the shard servers hold approximately same amount of data and the data access workload will be balanced across these servers. This approach, however, can hardly guarantee the performance of data access...
The rapid growth of computational power demand from scientific, business, and Web applications has led to the emergence of cloud-oriented data centers. These centers use pay-as-you-go execution environments that scale transparently to the user. Load prediction is a significant cost-optimal resource allocation and energy saving approach for a cloud computing environment. Traditional linear or nonlinear...
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