Performance Analysis of Novel Offloading Mechanism in Fog

Performance Analysis of Novel Offloading Mechanism in Fog Environment

Implementation Plan:
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Step 1: Initially, we create a network topology consisting of a Fog computing environment designed in iFogSim with multiple Iot devices, single smart gateway(SWG), single fog manager, multiple Fog clusters and single cloud.

Step 2: Next we calculate the state could include information about the current task, fog nodes’ status, network conditions, etc. Actions represent which fog node to offload the task to.

Step3: Next we perform the state as input and output Q-values for different actions (offloading decisions) using the DQN algorithm.

Step 4: Then we store and manage the agent’s experiences (state, action, reward, next state) in a replay buffer. This helps in breaking the temporal correlations between experiences and improves the stability of learning.

Step 5: Next we perform The training process involves updating the Q-network parameters to minimize the difference between predicted Q-values and target Q-values using the Bellman equation.

Step 6: Finally, we evaluate the following performance metrics

6.1 Number of tasks vs. Average Latency
6.2 Number of tasks vs. Energy Consumption
6.3 Number of tasks vs. Resource Utilization
6.4 Number of tasks vs. Throughput
6.5 Number of tasks vs. Offloading Rate
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Software Requirement:
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1. NetBeans-12.3 [But We had Implemented in NetBeans 18]
2. IFOGSIM
3. JDK 1.8 [But We had Implemented in JDK 20.0.2]
4. Windows 10 – [64-bits]
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Note :-
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1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.

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