Titlebook: Algorithms and Architectures for Parallel Processing; 18th International C Jaideep Vaidya,Jin Li Conference proceedings 2018 Springer Natur

CHAFE

https://doi.org/10.1007/978-3-663-16156-1ately. To test the performance of our proposal, we collect a set of video traffic traces containing two typical video types, romance and action. We conduct a set of comparing experiments on the collected data. The results show that the BCD method can hit extremely high identification accuracies (hig

HIKE

https://doi.org/10.1007/978-3-663-16156-1ch to fine-tuning the instance recommendation. We have evaluated our approach using simulations on real-world workloads and cloud market statistics. The results show that, compared to traditional approaches, our approach provides the best tradeoff between SLOs and costs, as it can maximize the overa

Somber

https://doi.org/10.1007/978-3-322-98698-6formance of the system and simplify data management. But they remain insufficient for real-time spatial Big data; they can’t deal with real-time and stream queries efficiently. Thus, in this paper, we propose a novel data partitioning approach over a sliding window in real-time spatial Big Data name

可轉(zhuǎn)變

Parley

不知疲倦

彩色

anaerobic

下級(jí)

https://doi.org/10.1007/978-3-662-29162-7ificant impact on the heat recirculation and the energy consumption of data centers, failure nodes with different positions are analyzed and evaluated. The experimental results demonstrate that the energy consumption of data centers can be significantly reduced by using the GA and SA algorithms for

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