**3. Performance analysis**

*Telecommunication Systems – Principles and Applications of Wireless-Optical Technologies*

waiting time is used to predict the congestion, delivery cost can be considered as an exponentially increasing function. Hence, the delivery cost to *c* via *n* is computed as:

> (*t*).[1 − Θ*<sup>n</sup> c*

Thus, the shortest path with least congestion is identified by pulling packets toward the neighbors that have the smallest queue. It helps the SDN controller to set

In order to deploy gaming modules, an algorithm is proposed that utilizes the Edge-Fog layer and Optical-Fog layer in the Optical-Fog network. The proposed algorithm places gaming modules using the SDN topology and iterates over all paths. Here, it places modules on the devices in incremental fashion starts from edge devices *dEdge* to the optical devices *dOptical* and to the cloud data-centers. The modules that can be placed for each fog device in the path ε*dEdge* ∪ *dOptical* are identified by computing the processing requirement against the available capacity of fog devices. A module *M* is placed on a fog device *dEdge* or *dOptical* only if all other modules are

(*t*)] + *Cn c*

*<sup>c</sup>* is the arrival time of packets *i*. Since the queue

(*t* − 1) (3)

Here, *τ* id the present time and *an*,*<sup>i</sup>*

the threshold value for decision making.

already placed in the bottom-up path.

*c* (*t*) = Ω*<sup>n</sup> c*

*2.3.2 Modules placement strategy in the Optical-Fog network*

*Cn*

**178**

The real-time gaming applications and CPS systems require ultralow latency, minimum energy consumption, and optimum bandwidth. The proposed Optical-Fog layer provides the desired QoE by evaluating the following parameters such as latency measure, energy consumption and bandwidth usage in contrast to the traditional cloud computing.
