Distributed Computing Projects Examples using NS2

Distributed computing projects examples using Network Simulator 2 (NS2) tool ideas are shared here so drop us all your details we will guide you back. Here we concentrate on different properties like resource allocation, load balancing, fault tolerance, and communication efficiency inside a distributed system. While NS2 is mainly a network simulator, it can still be able to replicate the communication amongst distributed nodes in various computing scenarios. Here are some distributed computing project examples that can be simulated using NS2:

  1. Resource Allocation in Distributed Computing Networks
  • Project Focus: Mimic dynamic resource allocation features in distributed networks to balance computing loads over numerous servers or data centers.
  • Objective: Execute algorithms like round-robin, least connections, or adaptive load balancing to improve resource consumption in a distributed computing environment.
  • Metrics: Resource usage, load balancing efficiency, communication overhead, and reaction time.
  1. Fault Tolerance in Distributed Systems
  • Project Focus: Imitate fault-tolerant functionalities where distributed nodes can identify and recuperate from failures in the network or system without impacting entire performance.
  • Objective: Establish replication and redundancy techniques to learn how distributed systems can tolerate node failures and uphold service availability.
  • Metrics: Fault detection time, recovery time, service accessibility, and network overhead.
  1. Task Scheduling in Distributed Computing Networks
  • Project Focus: Execute task scheduling algorithms in a distributed computing environment to enhance task allocation over several nodes.
  • Objective: Replicate task scheduling methods like first-come-first-served (FCFS), shortest job first (SJF), or priority-based scheduling and evaluate their performance.
  • Metrics: Task completion time, scheduling overhead, network latency, and CPU consumption.
  1. Distributed File Sharing and Data Replication
  • Project Focus: Model a distributed file-sharing system where nodes in the network share data files and replicate them for optimized availability and fault tolerance.
  • Objective: Establish data replication techniques and assess the performance of file sharing and access speed in distributed systems.
  • Metrics: File access latency, replication overhead, data consistency, and fault tolerance.
  1. Load Balancing in Distributed Cloud Computing
  • Project Focus: Emulate load-balancing features in a distributed cloud computing scenarios where tasks are dynamically allocated among various data centers.
  • Objective: Implement load-balancing algorithms involve weighted round-robin, least response time, and resource-aware algorithms to improve task distribution in cloud environments.
  • Metrics: Server load, task response time, network bandwidth utilization, and throughput.
  1. Energy-Efficient Computing in Distributed Systems
  • Project Focus: Model energy-efficient task scheduling and resource allocation algorithms in a distributed computing network.
  • Objective: Concentrate on how energy-efficient algorithms like Dynamic Voltage and Frequency Scaling (DVFS) can reduce energy utilization in distributed computing situations.
  • Metrics: Energy consumption, task completion time, and communication overhead.
  1. Distributed Network Caching for Content Delivery
  • Project Focus: Apply and replicate caching methods in a distributed content delivery network (CDN) to minimize latency and increase content availability.
  • Objective: Simulate caching algorithms like Least Recently Used (LRU), Least Frequently Used (LFU), or collaborative caching across several nodes to enhance content delivery.
  • Metrics: Cache hit ratio, content access latency, network bandwidth usage, and entire system throughput.
  1. Distributed Computing in Wireless Sensor Networks (WSNs)
  • Project Focus: Imitate distributed task execution in wireless sensor networks (WSNs) where tasks are distributed between sensor nodes for estimation.
  • Objective: Implement distributed computing algorithms in resource-constrained incidents like WSNs to prolong network lifetime and task execution productivity.
  • Metrics: Task execution time, energy consumption, network lifetime, and communication overhead.
  1. Blockchain-based Distributed Computing Simulation
  • Project Focus: Replicate a distributed computing network that uses blockchain technology for secure and tamper-proof task execution via numerous nodes.
  • Objective: Accomplish consensus algorithms like Proof of Work (PoW) or Proof of Stake (PoS) to make sure data integrity and security in distributed computing.
  • Metrics: Consensus time, data integrity, network overhead, and blockchain transaction latency.
  1. Latency Optimization in Distributed Computing
  • Project Focus: Emulate strategies to enhance communication latency in a distributed computing environment where nodes are dispersed over various geographic positions.
  • Objective: Execute latency reduction methods like edge computing, data replication, and content delivery to decrease delay in distributed applications.
  • Metrics: End-to-end latency, network congestion, task completion time, and system throughput.
  1. Distributed Computing over MANET (Mobile Ad-hoc Networks)
  • Project Focus: Mimic distributed task execution in a mobile ad-hoc network (MANET), where mobile nodes work together to finish computational tasks.
  • Objective: Attach task allocation techniques that consider node mobility and network topology variations in a dynamic MANET environment.
  • Metrics: Task completion time, network connectivity, mobility impact, and energy consumption.
  1. Distributed Machine Learning Model Training
  • Project Focus: Emulate the training of machine learning models in distributed computing situations that has several nodes cooperate to train a global model.
  • Objective: Execute data parallelism and model parallelism functions and simulate their performance according to the communication cost and model precision.
  • Metrics: Training time, model accuracy, communication overhead, and network bandwidth utilization.
  1. Distributed Consensus Algorithms for Resource Sharing
  • Project Focus: Establish and simulate consensus algorithms like Raft or Paxos in a distributed system for resource-sharing between several nodes.
  • Objective: Understand how distributed consensus can make certain dependability and coordination among nodes in resource allocation tasks.
  • Metrics: Consensus time, resource consumption, communication overhead, and fault tolerance.
  1. Data Partitioning and Load Distribution in Big Data Systems
  • Project Focus: Model the distributed data partitioning and load distribution strategies in big data systems, where large datasets are split across several computing nodes.
  • Objective: Implement techniques like hash partitioning or range partitioning to improve query processing and data recovery in a distributed environment.
  • Metrics: Query processing time, data retrieval latency, load distribution efficiency, and communication overhead.
  1. Distributed Computing in Edge and Fog Networks
  • Project Focus: Replicate distributed computing in edge and fog networks, where tasks are accomplished nearer to the data source (edge or fog nodes) instead of in centralized cloud servers.
  • Objective: Learn how edge and fog computing minimize network latency, optimize task execution speed, and increase service availability.
  • Metrics: Task completion time, network latency, energy utilization, and edge node usage.

This manual contains the several example project and their implementation details in brief manner regarding the Distributed Computing which is executed in ns2 tool and their evaluation process and simulation set up. We will deliver any additional information on these projects, if needed.