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. Front Matter ....Pages i-xiii Introduction (Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu)....Pages 1-9 Background (Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu)....Pages 11-33 Benchmarking (Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu)....Pages 35-45 Computation of Large Datasets (Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu)....Pages 47-64 Optimized Online Scheduling Algorithms (Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu)....Pages 65-81 Performance Evaluation (Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu)....Pages 83-89 Conclusion and Future Works (Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu)....Pages 91-92 Back Matter ....Pages 93-99