Integration of heterogeneous resources through updatable views

2005 ◽  
Author(s):  
H. Kozankiewicz ◽  
K. Stencel ◽  
K. Subieta
Keyword(s):  
Heterogeneous Resources

scholarly journals Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds

2020 ◽  
Vol 11 (1) ◽  
pp. 149
Author(s):  
Wu-Chun Chung ◽  
Tsung-Lin Wu ◽  
Yi-Hsuan Lee ◽  
Kuo-Chan Huang ◽  
Hung-Chang Hsiao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Data Stream ◽  
Limited Attention ◽  
Heterogeneous Servers ◽  
Resource Allocation Algorithm ◽  
System Utilization ◽  
Allocation Algorithms ◽  
Considerable Resource ◽  
Heterogeneous Resources

Resource allocation is vital for improving system performance in big data processing. The resource demand for various applications can be heterogeneous in cloud computing. Therefore, a resource gap occurs while some resource capacities are exhausted and other resource capacities on the same server are still available. This phenomenon is more apparent when the computing resources are more heterogeneous. Previous resource-allocation algorithms paid limited attention to this situation. When such an algorithm is applied to a server with heterogeneous resources, resource allocation may result in considerable resource wastage for the available but unused resources. To reduce resource wastage, a resource-allocation algorithm, called the minimizing resource gap (MRG) algorithm, for heterogeneous resources is proposed in this study. In MRG, the gap between resource usages for each server in cloud computing and the resource demands among various applications are considered. When an application is launched, MRG calculates resource usage and allocates resources to the server with the minimized usage gap to reduce the amount of available but unused resources. To demonstrate MRG performance, the MRG algorithm was implemented in Apache Spark. CPU- and memory-intensive applications were applied as benchmarks with different resource demands. Experimental results proved the superiority of the proposed MRG approach for improving the system utilization to reduce the overall completion time by up to 24.7% for heterogeneous servers in cloud computing.

scholarly journals Modularization of the LHCb software environment and preparation for heterogeneous resources

2020 ◽  
Vol 245 ◽  
pp. 05029
Author(s):  
Marco Clemencic ◽  
Ben Couturier
Keyword(s):  
Software Environment ◽  
Computing Grid ◽  
Computing Environments ◽  
Heterogeneous Resources

LHCb software runs in very different computing environments: the trigger farm at CERN, on the LHC Computing Grid (LCG), on shared clusters or on software developer’s desktops. . . The old model assumes the availability of CVMFS and relies on custom scripts (a.k.a LbScripts) to configure the environment to build and run the software. It lacks flexibility and does not allow, for example running in container and it is very difficult to extend them to configure and run on new environments. This paper describes the steps taken to modularize those tools to allow for easier development and deployment (as standard Python packages), but also added integration with container technology to better support non standard environments.

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