scholarly journals Improving Fairness for Distributed Interactive Applications in Software-Defined Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ran Xu ◽  
Weiqiang Zhang

With the popularization of distributed interactive applications (DIAs), for getting good interactive experience among participants, efficient and fair allocation of network resource should be considered. In software-defined networks, the presence of central controllers provides novel solution to deploy customizable routing for interactive applications, which allows fine-grained resource allocation for DIAs to achieve fairness among participants. But opportunities always come with challenges, the wide spread user locations often require distribution of controllers to meet the requirements of applications. Hence, the latency involved among participants is directly affected by the processing time of controllers. In this context, we address the DIAs’ fair resource provisioning problems on computing and links load with the objective of balancing the achievable request rate and fairness among multiple flows in SDN networks. We firstly formulate the problems as a combination of controller loading and routing optimization. Then, we propose proactive assignment controller algorithm based on deep learning and fairness path allocation algorithm to share the bottleneck links. Compared with the state-of-the-art greedy assignment algorithm and priority order allocating algorithm, the final result is proven to get better fairness on controller and link load among DIAs’ participants by trace driven simulation.

2020 ◽  
Vol 107 ◽  
pp. 485-497
Author(s):  
Jianwei Hou ◽  
Minjian Zhang ◽  
Ziqi Zhang ◽  
Wenchang Shi ◽  
Bo Qin ◽  
...  

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 40512-40535 ◽  
Author(s):  
Alaitz Mendiola ◽  
Jasone Astorga ◽  
Eduardo Jacob ◽  
Juanjo Unzilla

2011 ◽  
Vol 331 (1) ◽  
pp. 012002 ◽  
Author(s):  
Junmin Gu ◽  
Dimitrios Katramatos ◽  
Xin Liu ◽  
Vijaya Natarajan ◽  
Arie Shoshani ◽  
...  

Author(s):  
Minjian Zhang ◽  
Jianwei Hou ◽  
Ziqi Zhang ◽  
Wenchang Shi ◽  
Bo Qin ◽  
...  

2015 ◽  
Vol 25 (03) ◽  
pp. 1541003 ◽  
Author(s):  
Rafael Ferreira da Silva ◽  
Gideon Juve ◽  
Mats Rynge ◽  
Ewa Deelman ◽  
Miron Livny

Estimates of task runtime, disk space usage, and memory consumption, are commonly used by scheduling and resource provisioning algorithms to support efficient and reliable workflow executions. Such algorithms often assume that accurate estimates are available, but such estimates are difficult to generate in practice. In this work, we first profile five real scientific workflows, collecting fine-grained information such as process I/O, runtime, memory usage, and CPU utilization. We then propose a method to automatically characterize workflow task requirements based on these profiles. Our method estimates task runtime, disk space, and peak memory consumption based on the size of the tasks’ input data. It looks for correlations between the parameters of a dataset, and if no correlation is found, the dataset is divided into smaller subsets using a clustering technique. Task estimates are generated based on the ratio parameter/input data size if they are correlated, or based on the probability distribution function of the parameter. We then propose an online estimation process based on the MAPE-K loop, where task executions are monitored and estimates are updated as more information becomes available. Experimental results show that our online estimation process results in much more accurate predictions than an offline approach, where all task requirements are estimated prior to workflow execution.


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