How to Implement Network Scalability Improvement in NS2

To implement the Network Scalability Improvement in Network Simulator 2 (NS2) encompasses to enhance how ns2 manages larger network simulations containing numerous nodes, links and traffic flows at the same time reducing simulation time, memory utilization and making sure precise outputs. NS2, by default, can manage small-to-medium scale networks efficiently, yet for large-scale simulations, particular optimizations and strategies are needed to improve performance.

Below is a comprehensive guide to implementing network scalability improvements in NS2:

Step-by-Step Implementation:

  1. Understanding NS2 Scalability Issues

NS2 can face numerous challenges as they scaling to large networks:

  • High memory consumption: Large-scale networks need more memory for storing node information, packet queues, etc.
  • Increased simulation time: The time to simulate each event maximizes as the amount of nodes and events grows.
  • Limited event queue efficiency: The default event scheduling system can become inefficient for large-scale networks.
  • Complex topology creation: As networks grow, building and handling topologies can become burdensome.

We can improve the scalability by enhancing memory management, event scheduling and optimizing topology development.

  1. Optimizing Event Scheduling

NS2 uses a distinct event scheduler that processes simulation events. In large-scale networks, inefficient event handling can leads to delays. One way to improve scalability is by optimizing the event scheduling system.

2.1. Hierarchical Event Scheduling

In large simulations, the count of events can overwhelm the standard scheduler. Implementing hierarchical event scheduling divides the event queue into smaller, more manageable parts in terms of regions of the network. This can minimize the time it takes to handle events in large networks.

Steps:

  • Manage regional or hierarchical queues by altering the event scheduler.
  • Allocate nodes into regions or clusters, and maintain a separate event queue for each region.
  • Only process events in the relevant region rather than verfiying the entire event queue.

2.2. Efficient Event List Handling

NS2 uses linked lists to handle events, which can be slow for large event queues. Switching to a more efficient data structure like heap or binary tree, for the event queue can improve performance.

Steps:

  • Use binary heap to swap the linked list with a priority queue by fine-tuning the scheduler.cc file.
  • This improves the time difficulty for event insertion and extraction.

Example Modification: You can use a heap-based priority queue like the following:

class HeapScheduler : public Scheduler {

public:

virtual void schedule(Event* p, double t);

virtual Event* deque();

private:

std::priority_queue<Event*, std::vector<Event*>, CompareEventTime> eventQueue;

};

void HeapScheduler::schedule(Event* p, double t) {

// Insert event into the priority queue

eventQueue.push(p);

}

Event* HeapScheduler::deque() {

// Remove the event with the earliest execution time

if (!eventQueue.empty()) {

Event* event = eventQueue.top();

eventQueue.pop();

return event;

}

return nullptr;

}

  1. Memory Management Optimization

Memory consumption grows with the number of nodes and events in the simulation. By optimizing memory management, you can decrease the overall memory footprint of large simulations.

3.1. Use Compact Data Structures

Switching to more memory-efficient data structures can help reduce memory usage, especially for storing network topologies and packet queues.

  • Use arrays instead of linked lists for storing neighbor information in nodes.
  • Reduce needless memory usage by releasing memory when nodes or packets are no longer required (e.g., at the end of their lifecycle).

Example: If you’re storing packet information, enhance the packet storage by:

class OptimizedPacket {

public:

int packetID;  // Unique ID for the packet

int srcNode;   // Source node ID

int dstNode;   // Destination node ID

// Minimize the memory for payload, e.g., compress payload data

char* payload; // Simplified for demonstration

OptimizedPacket(int id, int src, int dst, char* data) {

packetID = id;

srcNode = src;

dstNode = dst;

payload = data; // Use compressed payloads to save memory

}

~OptimizedPacket() {

delete[] payload; // Release memory when the packet is destroyed

}

};

3.2. Disable Unnecessary Debugging

NS2 stores a lot of debugging details, which can improve memory utilization. Restricting or dipping the logging output for large-scale simulations can save memory and improve performance.

Steps:

  • Deactivate or reduce the use of trace files (trace files can grow very large in large simulations).
  • Store only particular events or nodes instead of the entire network by using selective logging.

# Disable tracing for certain network objects

set tracefd [open /dev/null w]  ;# Redirect trace output to null

$ns trace-all $tracefd

  1. Optimize Topology Generation

Large-scale networks need efficient generation and managing of network topologies. Use methods like hierarchical topologies or grid-based models to simplify the topology structure.

4.1. Use Topology Generation Tools

Instead of manually designing large topologies in OTcl, use external topology generators that are more efficient and built for scalability like:

  • BRITE: A tool for generating large network topologies.
  • GT-ITM: A tool for generating random network topologies.

BRITE Example:

  1. Define the topology using BRITE:

./brite -f example.conf -o output.brite

  1. Import the topology into NS2 using the BRITE NS2 module:

set brite_topology [new BriteTopology]

$brite_topology load_flatgrid 1000 1000 output.brite

4.2. Use Hierarchical Topologies

Minimize the difficulty of the network by organizing the network into clusters, regions, or domains. This can simplify routing and event management.

Example:

# Create a hierarchical topology with core, aggregation, and edge layers

set core_node [$ns node]

set agg_node1 [$ns node]

set agg_node2 [$ns node]

set edge_node1 [$ns node]

set edge_node2 [$ns node]

# Core-to-aggregation links

$ns duplex-link $core_node $agg_node1 1Gb 10ms DropTail

$ns duplex-link $core_node $agg_node2 1Gb 10ms DropTail

# Aggregation-to-edge links

$ns duplex-link $agg_node1 $edge_node1 100Mb 5ms DropTail

$ns duplex-link $agg_node2 $edge_node2 100Mb 5ms DropTail

  1. Leverage Parallelism

NS2 is single-threaded by default, which restricts its scalability. For large-scale simulations, you can use allocated or parallel simulation strategies to execute fragments of the network on various processors.

5.1. Use Parallel/Distributed Simulation with MPI

NS2 can be extended to use Message Passing Interface (MPI) for parallel simulation. This permits you to execute various parts of the simulation on different processors or machines.

Steps:

  1. Install MPI: Install MPI (e.g., OpenMPI or MPICH).

sudo apt-get install openmpi-bin openmpi-common libopenmpi-dev

  1. Modify NS2 for MPI: Recompile NS2 with MPI support by permitting MPI modules in the configuration.
  2. Partition the Simulation: Break down the simulation into smaller parts that can execute in parallel on various nodes. For instance, you can partition the simulation by network regions.
  3. Run the Simulation in Parallel: Execute the simulation through many processes by using MPI:

mpirun -np 4 ns your_parallel_simulation.tcl

5.2. Federated Simulation

In a federated simulation approach, various network elements (like wireless and wired parts) can be simulated using multiple simulators or instances of NS2. These simulators can communicate using standardized protocols like the High-Level Architecture (HLA).

  1. Efficient Traffic Generation

Traffic generation can become a bottleneck in large-scale simulations. Replicate realistic traffic without overwhelming the simulation by using efficient traffic generators.

6.1. Use Aggregate Traffic Models

Rather than creating single traffic flows for each pair of nodes, use accumulate traffic models to replicate the entire traffic amongst regions or clusters of nodes.

Example:

# Generate aggregate traffic between two clusters

set cluster1 [$ns node]

set cluster2 [$ns node]

# Use a traffic generator like CBR or exponential to simulate aggregate traffic

set udp [new Agent/UDP]

$ns attach-agent $cluster1 $udp

set null [new Agent/Null]

$ns attach-agent $cluster2 $null

$ns connect $udp $null

# Traffic pattern

set traffic [new Application/Traffic/CBR]

$traffic set packetSize_ 500

$traffic set rate_ 1Mb

$traffic attach-agent $udp

6.2. Optimize Traffic Tracing

For large-scale networks, traffic tracing can generate large number of data. Disable or reduce the level of tracing for unnecessary nodes or events.

  1. Testing and Validation
  1. Run the Simulation: After establishing these optimizations, execute your simulation.

ns your_optimized_simulation.tcl

  1. Monitor Performance: Compute the performance (execution time, memory utilization) of the simulation before and after accomplishing scalability improvements.
  2. Validate the Results: Make sure that the optimizations do not impact the precision of the simulation by relating results with the original unoptimized version.
  1. Future Enhancements

You can further improve scalability in NS2 by:

  • Leveraging NS3: NS3 is a more advanced version of NS2 that offers better scalability and performance. Consider transferring to NS3 for large-scale simulations.
  • Cloud-Based Simulation: Execute large-scale simulations in distributed environments by using cloud resources (such as AWS, Google Cloud).
  • Model Reduction: For very large networks, consider simplifying models by abstracting particular network elements to minimize the simulation difficulty.

In this demonstration, we had aggregated the overall information to understand and to learn for the implementation of Network Scalability Improvement using NS2 tool. We can improve it by enhancing memory utilization and improving topology generation and so on. We had also offered the details for the future enhancements with some strategies.

Get  in-depth analysis of your network performance with straightforward explanations. Our expertise lies in optimizing traffic flows while minimizing simulation time and memory usage. Reach out to us for guidance on implementing Network Scalability Improvements in NS2.