Resource Allocation Algorithms for Multicast Streaming in Elastic Cloud-Based Media Collaboration Services

2016 ◽  
Author(s):  
Rafael Xavier ◽  
Hendrik Moens ◽  
Bruno Volckaert ◽  
Filip De Turck
Keyword(s):  
Resource Allocation ◽  
Allocation Algorithms ◽  
Multicast Streaming ◽  
Media Collaboration

Cloud Resource Allocation Algorithms for Elastic Media Collaboration Flows

2016 ◽  
Author(s):  
Rafael Xavier ◽  
Hendrik Moens ◽  
Jurgen Slowack ◽  
Wim Sandra ◽  
Steven Delputte ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Elastic Media ◽  
Cloud Resource ◽  
Allocation Algorithms ◽  
Media Collaboration

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.

Adaptive Resource Allocation Algorithms for Multi-user MIMO-OFDM Systems

2014 ◽  
Vol 80 (1) ◽  
pp. 51-69 ◽  
Author(s):  
Najib A. Odhah ◽  
Emad S. Hassan ◽  
Mohamad Abdelnaby ◽  
Waleed E. Al-Hanafy ◽  
Moawad I. Dessouky ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Adaptive Resource Allocation ◽  
Ofdm Systems ◽  
Mimo Ofdm ◽  
Adaptive Resource ◽  
Allocation Algorithms
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