Constructor Detail

• GettingStarted

  GettingStarted()

Method Detail

• tut01_mm1_basics

   static Network tut01_mm1_basics()

  Getting started example 1: Basic M/M/1 queue.

  Features: - Simple M/M/1 queueing system - Exponential arrivals (rate 1.0) and service (rate 2.0) - FCFS scheduling with single server - JMT solver demonstration with result printing - Shows basic network construction and solution

  Returns:

  configured M/M/1 network model

• tut02_mg1_multiclass_solvers

   static Network tut02_mg1_multiclass_solvers()

  Getting started example 2: M/G/1 queue with two classes.

  Features: - Two open classes with different service distributions - Class1: Erlang service distribution (SCV = 1/3) - Class2: Trace-driven service using APH fitting - Demonstrates empirical data integration via Replayer - Manual routing matrix construction

  Returns:

  configured M/G/1 multi-class network model

• tut03_repairmen

   static Network tut03_repairmen()

  Getting started example 3: Machine Repair Problem (MRP).

  Features: - Closed network with 3 machines - Working state (delay) and repair queue with 2 servers - CTMC solver for exact state space analysis - Steady state probability computation - Demonstrates closed system modeling

  Returns:

  configured machine repair model

• tut04_lb_routing

   static Network tut04_lb_routing()

  Getting started example 4: Load balancing with routing strategies.

  Features: - Load balancer (router) distributing jobs to two PS queues - Demonstrates RAND (random) and RROBIN (round-robin) routing - Identical service rates for fair comparison - Shows how to change routing strategies dynamically - Model reset and re-solve for strategy comparison

  Returns:

  configured load balancing network model

• tut05_completes_flag

   static Network tut05_completes_flag()

  Getting started example 5: Class switching in single-node network.

  Features: - Three closed classes cycling through Class1→Class2→Class3→Class1 - Single FCFS queue with different Erlang service for each class - Demonstrates class switching routing matrix - Shows impact of setCompletes(false) on performance metrics - NC solver for exact normalizing constant computation

  Returns:

  configured class switching model

• tut06_cache_lru_zipf

   static Network tut06_cache_lru_zipf()

  Getting started example 6: Cache modeling with Zipf access pattern.

  Features: - LRU cache with 1000 items and capacity 50 - Zipf distribution (α=1.4) for realistic access patterns - Three classes: ClientClass, HitClass, MissClass - Different service times for cache hits vs misses - Demonstrates cache performance analysis

  Returns:

  configured cache network model

• tut07_respt_cdf

   static Network tut07_respt_cdf()

  Getting started example 7: Simple closed network for optimization.

  Features: - Basic closed network with delay and PS queue - Single closed class with 5 jobs - Demonstrates foundation for optimization studies - Shows basic structure for parameter analysis

  Returns:

  configured simple closed network model

• tut08_opt_load_balancing

   static Network tut08_opt_load_balancing()

  Getting started example 8: Optimization with COBYLA algorithm.

  Features: - Closed load balancing network with optimization - Two PS queues with different service rates - COBYLA optimizer to find optimal routing probability - Minimizes average system response time - Demonstrates integration of queueing models with optimization

  Returns:

  optimized load balancing network model

• gallery_merl1

   static Array<Object> gallery_merl1()

  Helper method to create M/E/1 model (equivalent to gallery_merl1).

  Features: - M/E/1 queueing system with exponential arrivals and Erlang service - Arrival rate: 1.0, Service: Erlang with mean 0.5 and order 2 - Basic serial routing topology

  Returns:

  array containing [model, source, queue, sink, oclass]

• tut09_dep_process_analysis

   static void tut09_dep_process_analysis()

  Getting started example 9: Studying a departure process.

  Features: - Analysis of departure process from M/E/1 queue - CTMC solver with system sampling (sampleSysAggr) - Departure event filtering and inter-departure time analysis - Comparison of empirical vs theoretical SCV (Marshall's formula) - Demonstrates advanced sampling and stochastic analysis The Java implementation fully supports departure process analysis including: - Filtering departure events from sampled trajectories - Computing inter-departure times and empirical SCV - Calculating theoretical SCV using Marshall's formula - Comparing empirical vs theoretical results

• main

   static void main(Array<String> args)

  Main method for testing and demonstrating getting started examples.

  Currently configured to run tut09_dep_process_analysis() and solve it using the SSA solver with specified sampling parameters.

  Parameters:

  args - command line arguments (not used)