• Description:
  In EdgeCloudSim, a simulation framework designed for edge and cloud computing environments, the 3-tier architecture typically refers to various configurations of the system design that determine how data and tasks flow between the Cloud, Edge, and Devices. In this context, there are several configurations based on the number of tiers and the inclusion of Edge Orchestration (EO) for computational tasks.

• A Single-Tier Architecture consists of only one tier (either the Edge or Cloud), where all processing, storage, and decision-making take place within that tier.
• Functions:
  Centralized Processing: In this architecture, all tasks and computations are handled in a single tier (either at the edge or in the cloud). There is no intermediate tier between devices and the processing layer.
  
  No Offloading: There is no mechanism for offloading tasks to a secondary tier. All processing either happens at the edge (for low-latency tasks) or at the cloud (for resource-intensive tasks).
  
  Simplified Design: This architecture is easier to design and deploy, as it avoids the complexity of managing multiple tiers.
  
  Limited Flexibility: The lack of multiple processing layers limits the flexibility and scalability. There is no load balancing or offloading between the edge and cloud, which may increase latency or computational overload.
• Example: In a Single-Tier Edge Architecture, data generated by IoT devices might be processed locally at the edge. In contrast, in a Single-Tier Cloud Architecture, all tasks are sent to the cloud for processing.

• A Two-Tier Architecture involves both the Edge and the Cloud as two distinct tiers in the system. It provides a more flexible and scalable design compared to the single-tier architecture by distributing the tasks between the edge and the cloud.
• Functions:
  Edge Layer: This tier handles tasks requiring low latency, such as data preprocessing, real-time analytics, and local decision-making. Edge devices process data generated by IoT devices (e.g., sensors, cameras) and perform local computations or filtering before sending relevant data to the cloud.
  
  Cloud Layer: The cloud handles more resource-intensive tasks that require significant computational power, such as complex analytics, machine learning model training, and large-scale data storage. It provides scalability and flexibility for handling large datasets and computations.
  
  Task Offloading: Tasks are offloaded from the edge to the cloud based on their computational complexity. Tasks that require high computational resources (e.g., AI model training) are sent to the cloud, while simpler tasks (e.g., sensor data aggregation) are handled at the edge.
  
  Scalability: The cloud tier offers elasticity to accommodate varying workloads, while the edge tier ensures that real-time tasks are processed with minimal latency.
• Example: A Two-Tier Edge-Cloud Architecture could be used for smart traffic management, where real-time traffic data is processed at the edge for immediate decision-making (e.g., adjusting traffic lights), while more comprehensive traffic analytics and forecasting are handled in the cloud.

• In the Two-Tier Architecture with Edge Orchestration (EO), an additional layer of Orchestration is introduced between the Edge and Cloud. This layer helps dynamically manage the distribution of tasks between the edge and cloud, optimizing performance and resource utilization.
• Functions of Two-Tier Architecture with Edge Orchestration (EO):
  Dynamic Task Scheduling: The orchestration layer dynamically decides where tasks should be processed based on real-time data such as the load on edge devices, available resources, task deadlines, and network conditions.
    Edge: Tasks that are latency-sensitive or can be processed with available resources are handled at the edge.
    Cloud: Tasks that require more computational resources or do not have stringent latency requirements are offloaded to the cloud.
  
  Load Balancing with Edge Intelligence: The orchestration layer not only manages offloading but also ensures that resources at the edge are balanced and utilized efficiently. It can decide whether to process tasks locally or offload based on the current load at the edge and cloud.
  
  Energy-Aware Task Offloading: The orchestration layer ensures energy-efficient processing by deciding whether to offload tasks to the cloud to save power or handle them at the edge.
  
  QoS and QoE Optimization: The orchestration layer works to ensure that the Quality of Service (QoS) and Quality of Experience (QoE) for applications are optimized, considering factors like latency, bandwidth, energy consumption, and resource availability.
  
  Real-Time Monitoring: The orchestration layer continuously monitors edge devices and cloud resources, adjusting task distribution based on real-time performance and resource availability.
• Example:
  Smart Traffic Management: Edge devices at traffic lights or intersections may process real-time traffic data locally to reduce latency. However, more advanced traffic prediction algorithms or long-term data analysis could be offloaded to the cloud, all managed dynamically by the orchestration layer.