Optimized Cloud Based Scheduling
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Optimized Cloud Based Scheduling
This book presents an improved design for service provisioning and allocation models that are validated through running genome sequence assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and showcases new algorithms for hybrid cloud scheduling. Scientific sectors such as bioinformatics, astronomy, high-energy physics, and Earth science are generating a tremendous flow of data, commonly known as big data. In the context of growing demand for big data analytics, cloud computing offers an ideal platform for processing big data tasks due to its flexible scalability and adaptability. However, there are numerous problems associated with the current service provisioning and allocation models, such as inefficient scheduling algorithms, overloaded memory overheads, excessive node delays and improper error handling of tasks, all of which need to be addressed to enhance the performance of big data analytics.
Optimized Cloud Resource Management and Scheduling
Optimized Cloud Resource Management and Scheduling identifies research directions and technologies that will facilitate efficient management and scheduling of computing resources in cloud data centers supporting scientific, industrial, business, and consumer applications. It serves as a valuable reference for systems architects, practitioners, developers, researchers and graduate level students. Explains how to optimally model and schedule computing resources in cloud computing Provides in depth quality analysis of different load-balance and energy-efficient scheduling algorithms for cloud data centers and Hadoop clusters Introduces real-world applications, including business, scientific and related case studies Discusses different cloud platforms with real test-bed and simulation tools
Intelligent Resource Scheduling with Neutrosophic Knowledge and Optimized Cache Management Using Cuckoo Search Method in Cloud Computing
The cloud environment comprised of distributed resources in a dynamic fashion, so this necessitates the need for developing optimal scheduling in cloud environment with the satisfaction of Quality of Service necessitated by the cloud consumer with the maximum profit to cloud providers. But the presence of impreciseness while scheduling cloud resources is the challenging issue of traditional scheduling policies.