• Cloud Computing Operations: Cloud computing refers to centralized data centers that provide scalable, on-demand services over the internet. Operations in cloud computing generally include:

 Resource Provisioning: Datacenters, hosts, and VM allocation.

 Task Scheduling: Cloudlet submission, load balancing, and workflow management.

 VM Management: VM allocation, migration, and scaling.

 Energy Efficiency: Power models, energy-aware scheduling, and DVFS(Dynamic Voltage and Frequency Scaling).

 Network Management: Bandwidth allocation, latency, and QoS management.

 Cost Modeling: Simulating pricing and billing for cloud resources.

 Fault Tolerance: Simulating failures and recovery mechanisms.

 Simulation Control: Event management, logging, and simulation configuration.

 Dynamic Resource Allocation: Elastic and QoS-aware resource management.

• Fog Computing Operations: Fog computing acts as an intermediate layer between edge devices and the cloud, helping to process and store data closer to where it is generated, but not at the source. Some key operations in fog computing include:

 Local Processing: Processing data locally to reduce the latency and bandwidth.

 Task Scheduling: Allocating tasks between edge nodes and fog nodes based on resources.

 Real-time Data Processing: Fog nodes process data locally, reducing the need to send data to the cloud.

 Preprocessing of Data: It aggregate the data coming from multiple edge devices to reduce the volume of data.

 Energy-aware Computing: It enhances energy efficiency in IoT networks.

• Edge Computing Operations: Edge computing operations focus on processing data near the source (at the network's edge) rather than relying solely on cloud data centers. These operations are essential for improving latency, bandwidth efficiency, and real-time decision-making. Some key operations in fog computing include:

  Real-time Data Processing: perform computations locally, enabling real-time data analysis and reducing the latency.

  Data Filtering and Preprocessing: can filter and preprocess raw data before sending only relevant information to the cloud.

 Data Offloading: can be offloaded between edge devices and the cloud depending on the complexity of the task

 Network Optimization: It optimize network traffic by reducing the amount of data that needs to traverse the network.

 Device and Resource Management: This enables seamless operation even in environments with limited connectivity.