CPU-bound

Abstract: This paper describes the parallelization of a mesoscale-cloud-scale numerical weather prediction model and experiments conducted to assess its performance. The model used is the Advanced Regional Prediction System (ARPS), a limited-area nonhydrostatic model suitable for cloud-scale and mesoscale studies. Because models such as ARPS are usually memory and CPU bound, the motivation here is to decrease the computer time required for running the model and/or increase the size of the problem that can be run. A domain decomposition strategy using a network of workstations produced a significant decrease in elapsed time and increase in problem size relative to a single-workstation run. The performance of the resulting program is described by deprived formulas (collectively known as a performance model), which predict the execution time and speedup for different numbers of processors and problem sizes. The interprocessor communication speeds are shown to be the major obstacle to achieving full processor ...

Abstract: MapReduce is a parallel programming model for processing the data-intensive applications in a cloud environment. The scheduler greatly influences the performance of MapReduce model while utilized in heterogeneous cluster environment. The dynamic nature of cluster environment and computing workloads affect the execution time and computational resource usage in the scheduling process. Further, data locality is essential for reducing total job execution time, cross-rack communication, and to improve the throughput. In the present work, a scheduling strategy named efficient locality and replica aware scheduling (ELRAS) integrated with an autonomous replication scheme (ARS) is proposed to enhance the data locality and performs consistently in the heterogeneous environment. ARS autonomously decides the data object to be replicated by considering its popularity and removes the replica as it is idle. The proposed approach is validated in a heterogeneous cluster environment with various realistic applications that are IO bound, CPU bound and mixed workloads. ELRAS improves the throughput by a factor about 2 as compared with the existing FIFO and it also yields near optimal data locality, reduce the execution time, and effective utilization of resources. The simplicity of ELRAS algorithm proves its feasibility to adopt for a wide range of applications.

Abstract: This paper proposes a self-training algorithm for load balancing in cluster computing. It uses load information including CPU load, memory usage and network traffic to decide the load of each node and combines this information with properties of each job, including CPU bound, memory bound and I/O bound features, that are extracted from the previous runs of these jobs. The proposed algorithm is compared to another algorithm that only uses the load information of each node for the purpose of load balancing. The performance evaluation results show that the proposed algorithm performs well.