Double-State-Temporal Difference Learning for Resource Provisioning in Uncertain Fog Computing Environment

Author(s):  
Bhargavi Krishna Murthy ◽  
Sajjan G Shiva
2020 ◽  
Vol 11 (4) ◽  
pp. 17-30
Author(s):  
Shefali Varshney ◽  
Rajinder Sandhu ◽  
P. K. Gupta

Application placement in the fog environment is becoming one of the major challenges because of its distributed, hierarchical, and heterogeneous nature. Also, user expectations and various features of IoT devices further increase the complexity of the problem for the placement of applications in the fog computing environment. Therefore, to improve the QoE of various end-users for the use of various system services, proper placement of applications in the fog computing environment plays an important role. In this paper, the authors have proposed a service placement methodology for the fog computing environment. For a better selection of application services, AHP technique has been used which provides results in the form of ranks. The performance evaluation of the proposed technique has been done by using a customized testbed that considers the parameters like CPU cycle, storage, maximum latency, processing speed, and network bandwidth. Experimental results obtained for the proposed methodology improved the efficiency of the fog network.


2020 ◽  
Vol 161 ◽  
pp. 109-131 ◽  
Author(s):  
Masoumeh Etemadi ◽  
Mostafa Ghobaei-Arani ◽  
Ali Shahidinejad

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