Volume 15, Issue 4 (10-2023)                   2023, 15(4): 41-52 | Back to browse issues page

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sheikhi nejad S, Khadem Zadeh A, Rahmani A M, Broumandnia A. Resource Allocation optimization in fog Architecture Based Software-Defined Networks. International Journal of Information and Communication Technology Research 2023; 15 (4) : 42
URL: http://ijict.itrc.ac.ir/article-1-543-en.html
Resource Allocation optimization in fog Architecture Based Software-Defined Networks
Sepideh Sheikhi nejad1 , Ahmad Khadem Zadeh 2, Amir Masoud Rahmani3 , Ali Broumandnia4
1- Department of Computer Engineering, Islamic Azad University South Branch, Tehran, Iran s.sheikhynejad9834@gmail.com
2- Department of Computer Engineering, Research Center ITRC, Tehran, Iran , a.khademzadeh@itrc.ac.ir
3- Future Technology Research Center, National Yunlin University of Science and Technology, Taiwan
4- Department of Computer Engineering, Islamic Azad University South Branch, Tehran, Iran
Abstract:   (776 Views)
As a growing of IoT devices, new computing paradigms such as fog computing are emerging. Fog computing is more suitable for real-time processing due to the proximity of resources to IoT layer devices. Service providers must dynamically update the hardware and software parameters of the network infrastructure. Software defined network (SDN) proposed as a new network paradigm, whose separate control layer from data layer and provides flexible network management. This paper presents a software-defined fog platform to host real-time applications in IoT. Then, we propose a novel resource allocation method. This method involves scheduling multi-node real-time task graphs over the fog to minimize task execution latency. The proposed method is designed to benefit the centralized structure of SDN. The simulation results show that the proposed method can find near to optimal solutions in a very lower execution time than the brute force method.
Article number: 42
Keywords: Software-defined network, fog computing, Multi-nodes weighted directed task graph, Task assigning, task offloading
Full-Text [PDF 1015 kb]   (321 Downloads)    
Type of Study: Research | Subject: Network
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