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GPU-based clusters are widely chosen for accelerating a variety of scientific applications in high-end cloud environments. With their growing popularity, there is a necessity for improving the system throughput and decreasing the turnaround time for co-executing applications on the same GPU device. However, resource contention among multiple applications on a multi-tasked GPU leads to the performance...
With the popularization and development of cloud computing, lots of scientific computing applications are conducted in cloud environments. However, current application scenario of scientific computing is also becoming increasingly dynamic and complicated, such as unpredictable submission times of jobs, different priorities of jobs, deadlines and budget constraints of executing jobs. Thus, how to perform...
Currently in large-scale scientific experiments, scientists often submit scientific workflow jobs at different time. From the view of system, the entire workload is a stream of jobs submitted at an unpredictable time and different job has different priority and deadline. Moreover the cost of performing these jobs cannot exceed a certain budget constraint. Therefore how to perform scientific workflow...
Large scale data processing is increasingly common in cloud computing systems like MapReduce, Hadoop, and Dryad in recent years. In these systems, files are split into many small blocks and all blocks are replicated over several servers. To process files efficiently, each job is divided into many tasks and each task is allocated to a server to deals with a file block. Because network bandwidth is...
The dynamic feature is one of the most important differences between Grid and traditional heterogeneous distributed systems, thus the most significant challenge for task scheduling in Grid environment is how to relieve the resource performance dynamism effectively. However, the existing schedule algorithms usually suppose that computation or communication times are deterministic and static, thus they...
With more and more research carried on in the QoS of Grid, QoS-based Grid task scheduling algorithm has become a hot research aspect. In this paper, various existing QoS-based Grid scheduling algorithms are analyzed firstly. And by introducing the conception of QoS matching offset between tasks and resources in Grid scheduling, resources and tasks can be clustering upon their offset in order that...
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