Parallel acceleration of CPU and GPU range queries over large data sets.

 Data management systems commonly use bitmap indices to increase the efficiency of querying scientific data. Bitmaps are usually highly compressible and can be queried directly using fast hardware-supported bit...

With the development of cloud computing, edge computing has been proposed to provide real-time and low-delay services to users. Current research usually integrates cloud computing and edge computing as cloud-e...

Because of their portability, electric motorcycles are usually pushed into elevators by residents and charged in the home, which has serious safety risks. Traditional manual-based methods to manage this behaviour.

With the application and comprehensive development of big data technology, the need for effective research on cloud workflow management and scheduling is becoming increasingly urgent. However, there are currently suitable methods for effective analysis. To determine how to effectively manage and schedule smart cloud workflows, this article studies big data from various aspects and draws the following conclusions: Compared with the original JStorm system, the response time is shortened by a maximum of 58.26% and an average of 23.18%, CPU resource utilization is increased by a maximum of 17.96% and an average of 11.39%, and memory utilization increased by a maximum of 88.7% and an average of 71.16%. In terms of optimizing the dynamic combination of web services, the overall performance of both the MOACO and CCA algorithms is better than that of the GA algorithm, and the average performance of the MOACO algorithm is better than that of the CCA algorithm. This paper also proposes a cloud workflow scheduling strategy based on an intelligent algorithm and realizes the two-tier scheduling of cloud workflow tasks by adjusting the combination strategy for cloud service resources. We have studied three representative intelligent algorithms (ACO, PSO and GA) and improved them for scheduling optimization. It can be clearly seen that in the same scenario, the optimal values of the different algorithms vary greatly for different test cases. However, the optimal solution curve is substantially consistent with the trend of the mean curve.


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