To simulate an Edge Computing projects using OPNET, we will require configuring a network including edge devices, cloud servers, and clients to design calculation close data sources and minimize latency. Edge Computing concentrates on processing data nearer to where it is made thus we offer stepwise approach to configuring and replicating Edge Computing projects in OPNET:
Steps to Simulate Edge Computing Projects in OPNET
- Set Up the Network Topology
- In OPNET’s Object Palette, choose modules like IoT devices, edge servers, cloud servers, and client devices.
- Organize the nodes within a hierarchical topology in which:
- Client nodes (sensors, IoT devices) are nearer to edge servers.
- Edge servers are placed among client nodes and the cloud servers, which performing like intermediate data processors.
- From the clients Cloud servers are positioned further that denoting a central data processing facility.
- Associate the devices utilizing IP or wireless links to replicate the network infrastructure.
- Configure Edge Servers
- Describe it such as a compute and storage node within the Attributes for each edge server:
- Allow application processing capabilities to replicate the missions such as data preprocessing, analytics, or storage for certain applications like data caching, image recognition.
- We can set the processing parameters like CPU capacity, memory size, and storage according to the hardware profile of edge server.
- Insert certain applications for each edge server like data filtering, aggregation, or analysis tasks which offload the primary cloud processing.
- Define Client Device Traffic
- In Application Configuration, describe the applications which replicate the client-generated data such as video streams, sensor data.
- Allocate these applications to the client nodes like IoT devices, sensors within the network.
- Configure the data generation rate, frequency, and volume for each application using Profile Configuration. For instance, set up the video cameras to transmit continuous streams to neighbouring edge servers, whereas sensors could transmit periodic updates.
- Set Up Cloud Server for Central Processing
- Set up the cloud server to perform as a centralized processor for tasks, which cannot be handled by edge servers.
- Indicate the applications on the cloud server, which can manage the heavy computation or storage tasks that the edge servers can offload periodically.
- Describe a pathway for data transfer amongst edge servers and the cloud, which replicating situations in which edge servers are upload processed data to the cloud for advance analysis.
- Configure Network Latency and Bandwidth Constraints
- Configure realistic network latency and bandwidth limitations on the links among clients, edge servers, and the cloud.
- This stage is significant since Edge Computing targets to minimize the latency by locally processing data at the edge, and these limitations will support to illustrate the advantages of edge-based processing.
- Simulate Task Offloading and Load Balancing
- Set up edge servers to manage the local processing feasible and offload tasks to the cloud only when required.
- Replicate the load balancing through edge and cloud servers in which edge servers manage main processing and then relieve of overflow tasks to the cloud.
- Define Simulation Parameters
- Describe the simulation duration and allow data collection for related performance parameters that containing:
- End-to-End Delay: Assess the latency reduction attained by edge processing.
- Throughput and Network Utilization: Measure how much data is processed locally against transmitted to the cloud.
- Edge Server Load: Monitor CPU and memory usage of the edge servers to see its role in minimizing cloud load.
- Task Offloading Rate: Observe how frequently tasks are offloaded from the edge servers to the cloud.
- Run the Simulation
- Implement the simulation and then monitor how data flows from client devices to edge servers and selectively to the cloud.
- Monitor real-time performance to observe in which processing happens and how load is distributed amongst edge and cloud resources.
- Analyze Results
- Utilize OPNET’s Analysis Tools to estimate:
- Latency: Equate the latency of processing at the edge against within the cloud.
- Data Transfer Reduction: Assess the reduction in data transferred to the cloud by reason of edge processing.
- Edge and Cloud Utilization: Verify memory usage and CPU on both edge servers and cloud servers.
- Task Completion Time: Find out how rapidly tasks are accomplished while processed at the edge equated to transmitting them to the cloud.
We had furnished brief outline of the simulation process that helps you to understand the concept and how to perform and simulate the Edge Computing projects using OPNET’s tools. Furthermore, we will share detailed insights in another guide.
