Distributed Computing Projects examples using omnet++

Distributed Computing Projects examples using omnet++ are shared below as it has to include the use of multiple associated computers to resolve complex tasks in which each node in the network donates to the computation process. While the OMNeT++ can be used to emulate the numerous contexts of distributed computing, like resource allocation, task scheduling, and fault tolerance in distributed systems.  The given below are the samples of distributed computing projects that can be implemented using OMNeT++:

1. Simulation of Distributed Task Scheduling Algorithms

• Objective: Mimic and measure numerous task scheduling algorithms in a distributed computing environment to enhance the resource usage and reduce the task completion time.
• Implementation: Develop a distributed network in which the multiple nodes can receive and process tasks. Execute the various scheduling techniques, like Round-Robin, Earliest Deadline First (EDF), or Load Balancing, to allocate tasks to nodes.
• Extension: To emulate the scenarios with changing task arrival rates, network loads and node capacities and evaluate the performance of each scheduling techniques in terms of task completion time, resource utilization, and load distribution.

2. Fault Tolerance in Distributed Computing Systems

• Objective: To emulate fault tolerance mechanisms in a distributed computing environment to make sure the system reliability and continuity in the presence of node failures.
• Implementation: Generate a distributed network in which nodes work together to finish the tasks. Execute fault tolerance algorithm like checkpointing, replication, or task reallocation to manage the node failures.
• Extension: To emulate the various kinds of failures like crash failures, network partitions and measure the efficiency of the fault tolerance mechanisms in maintaining system performance. Evaluate the trade-offs among the fault tolerance, overhead, and system complexity.

3. Resource Allocation in Distributed Cloud Computing

• Objective: To emulate the resource allocation strategies in a distributed cloud computing environment to enhance the usage of computing resources and make sure the QoS for users.
• Implementation: Develop a cloud computing network with multiple data centers and users and execute resource allocation techniques that allocate tasks to virtual machines (VMs) according the conditions like CPU, memory, and bandwidth requirements.
• Extension: To mimic the changing workloads, user demands, and network conditions and assess the performance of the resource allocation strategies in terms of resource utilization, task completion time, and cost efficiency and test with dynamic resource allocation based on real-time monitoring.

4. Distributed Consensus in Blockchain Networks

• Objective: To mimic distributed consensus algorithms used in blockchain networks to make sure agreement between nodes on the state of the distributed ledger.
• Implementation: Generate a blockchain network with multiple nodes participating in the consensus process. Execute and compare numerous consensus techniques like Proof of Work (PoW), Proof of Stake (PoS), or Practical Byzantine Fault Tolerance (PBFT).
• Extension: Evaluate the performance, security, and energy consumption of each consensus techniques in various network conditions. Mimic attacks like 51% attacks or double spending and assess the robustness of the consensus mechanisms.

5. Simulation of Distributed File Systems

• Objective: mimic a distributed file system (DFS) to investigate data storage, retrieval, and replication across multiple nodes in a network.
• Implementation: To develop a DFS in which the files are dispersed across multiple nodes. Execute file distribution, replication, and retrieval mechanisms to make sure data availability and fault tolerance.
• Extension: To mimic the various access patterns like read-intensive, write-intensive and measure the performance of the DFS in terms of data retrieval time, load balancing, and fault tolerance. Verify with numerous replication strategies to enhance data availability and storage efficiency.

6. Distributed Computing for Big Data Processing

• Objective: Emulate a distributed computing framework for processing big data tasks via the multiple nodes, like in a MapReduce or Apache Spark environment.
• Implementation: Generate a network where large datasets are processed in parallel by sharing the tasks to multiple nodes and execute the MapReduce programming model or related frameworks to split, process, and aggregate data.
• Extension: Assess the performance of the distributed computing framework in changing data sizes, node capacities, and network conditions. To mimic the fault tolerance mechanisms, like task re-execution or data replication, to make sure the reliable data processing.

7. Simulation of Peer-to-Peer Distributed Networks

• Objective: Emulate a peer-to-peer (P2P) distributed network to examine the decentralized resource sharing, data distribution, and network robustness.
• Implementation: Developed a P2P network in which the nodes act as both clients and servers that deliberately share resources like files or computing power. Execute P2P protocols such as BitTorrent or Chord for resource discovery and data exchange.
• Extension: To execute network scenarios with changing levels of node participation, churn (nodes joining and leaving), and malicious behaviour like free-riding, Sybil attacks. Evaluate the effect of these factors on data availability, search efficiency, and overall network resilience.

8. Distributed Machine Learning (ML) Model Training

• Objective: mimic distributed training of machine learning models via multiple nodes to enhance the training times and manage the large datasets.
• Implementation: generate a distributed network in which a large ML model is split into smaller tasks and distributed between the nodes for parallel training. Execute approaches like data parallelism or model parallelism to divide the workload.
• Extension: Assess the effect of various training strategies on model convergence, accuracy, and training time. To emulate scenarios with changing data distribution, node capacities, and network conditions to measure the trade-offs among the training speed and model performance.

9. Load Balancing in Distributed Computing Environments

• Objective: Emulate load balancing algorithm in a distributed computing environment to make sure even distribution of tasks across nodes and mitigate overloads.
• Implementation: Develop a network in which the tasks are shared among nodes with changing capacities. Execute load balancing techniques like round-robin, least connections, or dynamic load balancing based on real-time monitoring.
• Extension: To mimic the various workload patterns like bursty, steady-state and measure the efficiency of the load balancing techniques in maintaining system performance. Evaluate the trade-offs among the load balancing overhead, system responsiveness, and task completion time.

10. Distributed Ledger Technology (DLT) Simulation

• Objective: Mimic a Distributed Ledger Technology (DLT) network to examine the decentralized management and synchronization of distributed ledgers through multiple nodes.
• Implementation: Generate a DLT network in which transactions are recorded in a decentralized ledger shared among nodes. Execute the consensus techniques to make sure the reliability and integrity of the ledger via the network.
• Extension: Emulate scenarios with changing transaction rates, network latencies, and node failures. Assess the performance, security, and scalability of the DLT under various conditions. Test with numerous ledger structures like blockchain, directed acyclic graph to enhance performance.

11. Simulation of Distributed Databases

• Objective: To emulate a distributed database system to examine the data consistency, availability, and partition tolerance in a distributed environment.
• Implementation: To developed a distributed database with multiple nodes storing various parts of the database. Execute the consistency models like eventual consistency, strong consistency, or CAP theorem trade-offs to handle the data across nodes.
• Extension: To emulate scenarios with concurrent transactions, network partitions, and node failures and measure the effect of various consistency models on data integrity, query latency, and system availability.

12. Distributed Autonomous Systems Simulation

• Objective: To emulate a distributed autonomous system like a fleet of drones or robots in which nodes collaborate to realize a common aim without centralized control.
• Implementation: Generate a network in which the autonomous nodes interact and cooperate to comprehensive tasks like exploration, surveillance, or delivery. Execute distributed algorithms for coordination, task allocation, and consensus between the nodes.
• Extension: Emulate scenarios with dynamic environments, node failures, or communication delays. Assess the performance of the distributed autonomous system in terms of task completion time, resilient, and scalability.

13. Simulation of Distributed Hash Tables (DHT)

• Objective: Emulate a Distributed Hash Table (DHT) to study the decentralized data storage, retrieval, and scalability in a distributed network.
• Implementation: Develop a network in which nodes participate in a DHT, storing and retrieving key-value pairs based on a dispersed hash function. Execute protocols such as Chord, Kademlia, or Pastry for efficient data lookup and routing.
• Extension: Mimic the scenarios with changing levels of node churn, data distribution, and lookup requests. Evaluate the performance of the DHT in terms of lookup latency, data availability, and network overhead.

14. Edge Computing in Distributed Networks

• Objective: To mimic edge computing scenarios wherever the computational tasks are distributed among the centralized cloud servers and edge devices closer to the data source.
• Implementation: Generate a dispersed network with edge devices and cloud servers and execute task offloading schemes that regulate whether tasks should be handled locally at the edge or offloaded to the cloud based on factors such as latency, energy consumption, and task complexity.
• Extension: To emulate various edge computing scenarios like IoT applications, real-time analytics, or mobile gaming. Measure the performance trade-offs among the edge and cloud computing in terms of latency, resource utilization, and energy efficiency.

15. Simulation of Distributed Collaborative Filtering for Recommender Systems

• Objective: To emulate a distributed collaborative filtering technique to power a recommender system via multiple nodes in a network.
• Implementation: Develop a network in which the user data like preferences, ratings is dispersed via the multiple nodes. Execute a collaborative filtering technique that collects data from various nodes to make personalized recommendations.
• Extension: Assess the performance of the distributed recommender system in various data distribution scenarios, user loads, and network conditions. Evaluate the effect of data sparsity, node failures, and privacy-preserving approaches on recommendation accuracy and system performance.

In the end, we had learned about the examples of Distributed computing using OMNeT++ that provides how to implement, simulate and their extensions were given. Also, we offer the detailed information regarding the Distributed computing. On Distributed Computing Projects using omnet++tool we share high quality project performance contact omnet-manual.com to get best simulation outcome