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How can I query for the configured maximum IBM MQ queue depth in Java?

How can I use the IBM MQ Java APIs to query for the maximum queue depth attribute of a queue?

You can use MQ PCF to retrieve attribute values of a queue.
i.e.
request = new PCFMessage(CMQCFC.MQCMD_INQUIRE_Q);
/**
* You can explicitly set a queue name like "TEST.Q1" or
* use a wild card like "TEST.*"
*/
request.addParameter(CMQC.MQCA_Q_NAME, "*");
// Add parameter to request only local queues
request.addParameter(CMQC.MQIA_Q_TYPE, CMQC.MQQT_LOCAL);
// Add parameter to request all of the attributes of the queue
request.addParameter(CMQCFC.MQIACF_Q_ATTRS, new int [] { CMQC.MQCA_Q_NAME,
CMQC.MQIA_CURRENT_Q_DEPTH,
CMQC.MQIA_MAX_Q_DEPTH
});
responses = agent.send(request);
for (int i = 0; i < responses.length; i++)
{
if ( ((responses[i]).getCompCode() == CMQC.MQCC_OK) &&
((responses[i]).getParameterValue(CMQC.MQCA_Q_NAME) != null) )
{
String name = responses[i].getStringParameterValue(CMQC.MQCA_Q_NAME);
if (name != null)
name = name.trim();
int depth = responses[i].getIntParameterValue(CMQC.MQIA_CURRENT_Q_DEPTH);
int maxDepth = responses[i].getIntParameterValue(CMQC.MQIA_MAX_Q_DEPTH);
}
}
I thought I posted MQListQueueAttributes01.java to StackOverflow but I cannot find it. It is on my blog.
It is a complete and fully functioning Java/MQ/PCF program to retrieve all attributes values of a queue. If you click on "PCF" category on my blog, you will find a variety of complete sample Java/MQ/PCF programs.

One way of doing it is by using the inquire method on the MQQueue object:
import com.ibm.mq.MQException;
import com.ibm.mq.MQQueue;
import com.ibm.mq.MQQueueManager;
import static com.ibm.mq.constants.CMQC.MQIA_MAX_Q_DEPTH;
import static com.ibm.mq.constants.CMQC.MQOO_INQUIRE;
public class SampleJavaCode {
public static void main(String[] args) throws MQException {
MQQueueManager mqQueueManager = ...;
MQQueue mqQueue = mqQueueManager.accessQueue("ORDER", MQOO_INQUIRE);
int[] selectors = new int[]{MQIA_MAX_Q_DEPTH};
int[] intAttrs = new int[1];
byte[] charAttrs = new byte[0];
mqQueue.inquire(selectors, intAttrs, charAttrs);
System.out.println("Max Queue depth = " + intAttrs[0]);
}
}

How can I feed a sparse placeholder in a TensorFlow model from Java

I'm trying to calculate the best match for a given address with the kNN algorithm in TensorFlow, which works pretty good, but when I'm trying to export the model and use it in our Java Environment I got stuck on how to feed the sparse placholders from Java.
Here is a pretty much stripped down version of the python part, which returns the smallest distance between the test name and the best reference name. So far this work's as expected. When I export the model and import it in my Java program it always returns the same value (distance of the placeholders default). I asume, that the python function sparse_from_word_vec(word_vec) isn't in the model, which would totally make sense to me, but then how should i make this sparse tensor? My input is a single string and I need to create a fitting sparse tensor (value) to calculate the distance. I also searched for a way to generate the sparse tensor on the Java side, but without success.
import tensorflow as tf
import pandas as pd
d = {'NAME': ['max mustermann',
'erika musterfrau',
'joseph haydn',
'johann sebastian bach',
'wolfgang amadeus mozart']}
df = pd.DataFrame(data=d)
input_name = tf.placeholder_with_default('max musterman',(), name='input_name')
output_dist = tf.placeholder(tf.float32, (), name='output_dist')
test_name = tf.sparse_placeholder(dtype=tf.string)
ref_names = tf.sparse_placeholder(dtype=tf.string)
output_dist = tf.edit_distance(test_name, ref_names, normalize=True)
def sparse_from_word_vec(word_vec):
num_words = len(word_vec)
indices = [[xi, 0, yi] for xi,x in enumerate(word_vec) for yi,y in enumerate(x)]
chars = list(''.join(word_vec))
return(tf.SparseTensorValue(indices, chars, [num_words,1,1]))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
t_data_names=tf.constant(df['NAME'])
reference_names = [el.decode('UTF-8') for el in (t_data_names.eval())]
sparse_ref_names = sparse_from_word_vec(reference_names)
sparse_test_name = sparse_from_word_vec([str(input_name.eval().decode('utf-8'))]*5)
feeddict={test_name: sparse_test_name,
ref_names: sparse_ref_names,
}
output_dist = sess.run(output_dist, feed_dict=feeddict)
output_dist = tf.reduce_min(output_dist, 0)
print(output_dist.eval())
tf.saved_model.simple_save(sess,
"model-simple",
inputs={"input_name": input_name},
outputs={"output_dist": output_dist})
And here is my Java method:
public void run(ApplicationArguments args) throws Exception {
log.info("Loading model...");
SavedModelBundle savedModelBundle = SavedModelBundle.load("/model", "serve");
byte[] test_name = "Max Mustermann".toLowerCase().getBytes("UTF-8");
List<Tensor<?>> output = savedModelBundle.session().runner()
.feed("input_name", Tensor.<String>create(test_names))
.fetch("output_dist")
.run();
System.out.printl("Nearest distance: " + output.get(0).floatValue());
}

I was able to get your example working. I have a couple of comments on your python code before diving in.
You use the variable output_dist for 3 different value types throughout the code. I'm not a python expert, but I think it's bad practice. You also never actually use the input_name placeholder, except for exporting it as an input. Last one is that tf.saved_model.simple_save is deprecated, and you should use the tf.saved_model.Builder instead.
Now for the solution.
Looking at the libtensorflow jar file using the command jar tvf libtensorflow-x.x.x.jar (thanks to this post), you can see that there are no useful bindings for creating a sparse tensor (maybe make a feature request?). So we have to change the input to a dense tensor, then add operations to the graph to convert it to sparse. In your original code the sparse conversion was on the python side which means that the loaded graph in java wouldn't have any ops for it.
Here is the new python code:
import tensorflow as tf
import pandas as pd
def model():
#use dense tensors then convert to sparse for edit_distance
test_name = tf.placeholder(shape=(None, None), dtype=tf.string, name="test_name")
ref_names = tf.placeholder(shape=(None, None), dtype=tf.string, name="ref_names")
#Java Does not play well with the empty character so use "/" instead
test_name_sparse = tf.contrib.layers.dense_to_sparse(test_name, "/")
ref_names_sparse = tf.contrib.layers.dense_to_sparse(ref_names, "/")
output_dist = tf.edit_distance(test_name_sparse, ref_names_sparse, normalize=True)
#output the index to the closest ref name
min_idx = tf.argmin(output_dist)
return test_name, ref_names, min_idx
#Python code to be replicated in Java
def pad_string(s, max_len):
return s + ["/"] * (max_len - len(s))
d = {'NAME': ['joseph haydn',
'max mustermann',
'erika musterfrau',
'johann sebastian bach',
'wolfgang amadeus mozart']}
df = pd.DataFrame(data=d)
input_name = 'max musterman'
#pad dense tensor input
max_len = max([len(n) for n in df['NAME']])
test_input = [list(input_name)]*len(df['NAME'])
#no need to pad, all same length
ref_input = list(map(lambda x: pad_string(x, max_len), [list(n) for n in df['NAME']]))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
test_name, ref_names, min_idx = model()
#run a test to make sure the model works
feeddict = {test_name: test_input,
ref_names: ref_input,
}
out = sess.run(min_idx, feed_dict=feeddict)
print("test output:", out)
#save the model with the new Builder API
signature_def_map= {
"predict": tf.saved_model.signature_def_utils.predict_signature_def(
inputs= {"test_name": test_name, "ref_names": ref_names},
outputs= {"min_idx": min_idx})
}
builder = tf.saved_model.Builder("model")
builder.add_meta_graph_and_variables(sess, ["serve"], signature_def_map=signature_def_map)
builder.save()
And here is the java to load and run it. There is probably a lot of room for improvement here (java isn't my main language), but it gives you the idea.
import org.tensorflow.Graph;
import org.tensorflow.Session;
import org.tensorflow.Tensor;
import org.tensorflow.TensorFlow;
import org.tensorflow.SavedModelBundle;
import java.util.ArrayList;
import java.util.List;
import java.util.Arrays;
public class Test {
public static byte[][] makeTensor(String s, int padding) throws Exception
{
int len = s.length();
int extra = padding - len;
byte[][] ret = new byte[len + extra][];
for (int i = 0; i < len; i++) {
String cur = "" + s.charAt(i);
byte[] cur_b = cur.getBytes("UTF-8");
ret[i] = cur_b;
}
for (int i = 0; i < extra; i++) {
byte[] cur = "/".getBytes("UTF-8");
ret[len + i] = cur;
}
return ret;
}
public static byte[][][] makeTensor(List<String> l, int padding) throws Exception
{
byte[][][] ret = new byte[l.size()][][];
for (int i = 0; i < l.size(); i++) {
ret[i] = makeTensor(l.get(i), padding);
}
return ret;
}
public static void main(String[] args) throws Exception {
System.out.println("Loading model...");
SavedModelBundle savedModelBundle = SavedModelBundle.load("model", "serve");
List<String> str_test_name = Arrays.asList("Max Mustermann",
"Max Mustermann",
"Max Mustermann",
"Max Mustermann",
"Max Mustermann");
List<String> names = Arrays.asList("joseph haydn",
"max mustermann",
"erika musterfrau",
"johann sebastian bach",
"wolfgang amadeus mozart");
//get the max length for each array
int pad1 = str_test_name.get(0).length();
int pad2 = 0;
for (String var : names) {
if(var.length() > pad2)
pad2 = var.length();
}
byte[][][] test_name = makeTensor(str_test_name, pad1);
byte[][][] ref_names = makeTensor(names, pad2);
//use a with block so the close method is called
try(Tensor t_test_name = Tensor.<String>create(test_name))
{
try (Tensor t_ref_names = Tensor.<String>create(ref_names))
{
List<Tensor<?>> output = savedModelBundle.session().runner()
.feed("test_name", t_test_name)
.feed("ref_names", t_ref_names)
.fetch("ArgMin")
.run();
System.out.println("Nearest distance: " + output.get(0).longValue());
}
}
}
}

How can I use feed and fetch functions in TensorFlowInferenceInterface?

Although I want to use feed and fetch functions in TensorFlowInferenceInterface, I can't understand feed and fetch args.
public void feed(String inputName, float[] src, long... dims)
public void fetch(String outputName, float[] dst)
Here is TensorflowInferenceInterface.↓
https://github.com/tensorflow/tensorflow/blob/r1.4/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
Now, I use Android-Studio and want to import program using MNIST.
Here is program that make protocol buffer.
import tensorflow as tf
import shutil
import os.path
if os.path.exists("./tmp/beginner-export"):
shutil.rmtree("./tmp/beginner-export")
# Import data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("./tmp/data/", one_hot=True)
g = tf.Graph()
with g.as_default():
# Create the model
x = tf.placeholder("float", [None, 784])
W = tf.Variable(tf.zeros([784, 10]), name="vaiable_W")
b = tf.Variable(tf.zeros([10]), name="variable_b")
y = tf.nn.softmax(tf.matmul(x, W) + b)
# Define loss and optimizer
y_ = tf.placeholder("float", [None, 10])
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
sess = tf.Session()
# Train
init = tf.initialize_all_variables()
sess.run(init)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
train_step.run({x: batch_xs, y_: batch_ys}, sess)
# Test trained model
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print(accuracy.eval({x: mnist.test.images, y_: mnist.test.labels}, sess))
# Store variable
_W = W.eval(sess)
_b = b.eval(sess)
sess.close()
# Create new graph for exporting
g_2 = tf.Graph()
with g_2.as_default():
# Reconstruct graph
x_2 = tf.placeholder("float", [None, 784], name="input")
W_2 = tf.constant(_W, name="constant_W")
b_2 = tf.constant(_b, name="constant_b")
y_2 = tf.nn.softmax(tf.matmul(x_2, W_2) + b_2, name="output")
sess_2 = tf.Session()
init_2 = tf.initialize_all_variables();
sess_2.run(init_2)
graph_def = g_2.as_graph_def()
tf.train.write_graph(graph_def, './tmp/beginner-export',
'beginner-graph.pb', as_text=False)
# Test trained model
y__2 = tf.placeholder("float", [None, 10])
correct_prediction_2 = tf.equal(tf.argmax(y_2, 1), tf.argmax(y__2, 1))
accuracy_2 = tf.reduce_mean(tf.cast(correct_prediction_2, "float"))
print(accuracy_2.eval({x_2: mnist.test.images, y__2: mnist.test.labels}, sess_2))
placeholder name for input is "input".
placeholder name for output is "output".
Please tell me feed and fetch usage.

I have given a sample code with the comments. hope you will understand.
private static final String INPUT_NODE = "input:0"; // input tensor name
private static final String OUTPUT_NODE = "output:0"; // output tensor name
private static final String[] OUTPUT_NODES = {"output:0"};
private static final int OUTPUT_SIZE = 10; // number of classes
private static final int INPUT_SIZE = 784; // size of the input
INPUT_IMAGE //MNIST Image
float[] result = new float[OUTPUT_SIZE]; // get the output probabilities for each class
inferenceInterface.feed(INPUT_NODE, INPUT_IMAGE, 1, INPUT_SIZE); //1-D input (1,INPUT_SIZE)
inferenceInterface.run(OUTPUT_NODES);
inferenceInterface.fetch(OUTPUT_NODE, result);
For the Android Tensorflow library version that I'm using, I need to give a 1-D input. Therefore, the Tensorflow code needs to modify according to that,
x_2 = tf.placeholder("float", [None, 1, 784], name="input") //1-D input
x_2 = tf.reshape(x_2,[-1, 784]) // reshape according to the model requirements
Hope this helps.

Mapreduce java program to search QuadTree index and also run GeometryEngine.contains to confirm point in polygon using wkt file

This post is a map reduce implementation suggested for my previous question: "How to optimize scan of 1 huge file / table in Hive to confirm/check if lat long point is contained in a wkt geometry shape"
I am not well-versed in writing java programs for map-reduce and I mainly use Hive or Pig or spark to develop in Hadoop eco-system. To give a background of task at hand: I am trying to associate every latitude/longitude ping to corresponding ZIP postal code. I have a WKT multi-polygon shape file (500 MB) with all the zip information. I have loaded this in Hive and can do a join using ST_Contains(polygon, point). However, it takes very long to complete. To over come this bottle neck I am trying to leverage the example in ESRI ("https://github.com/Esri/gis-tools-for-hadoop/tree/master/samples/point-in-polygon-aggregation-mr") by building a quad tree index for searching a point derived from lat-long in polygon.
I have managed to write the code and it clogs up the Java heap memory of the cluster. Any suggestions on improving the code or looking at a different approach will be greatly appreciated:
Error message:
Error: Java heap space
Container killed by the ApplicationMaster.
Container killed on request. Exit code is 143
Container exited with a non-zero exit code 143
My code:
public class MapperClass extends Mapper<LongWritable, Text, Text, IntWritable> {
// column indices for values in the text file
int longitudeIndex;
int latitudeIndex;
int wktZip;
int wktGeom;
int wktLineCount;
int wktStateID;
// in boundaries.wkt, the label for the polygon is "wkt"
//creating ArrayList to hold details of the file
ArrayList<ZipPolyClass> nodes = new ArrayList<ZipPolyClass>();
String labelAttribute;
EsriFeatureClass featureClass;
SpatialReference spatialReference;
QuadTree quadTree;
QuadTreeIterator quadTreeIter;
BufferedReader csvWkt;
// class to store all the values from wkt file and calculate geometryFromWKT
public class ZipPolyClass {
public String zipCode;
public String wktPoly;
public String stateID;
public int indexJkey;
public Geometry wktGeomObj;
public ZipPolyClass(int ijk, String z, String w, String s ){
zipCode = z;
wktPoly = w;
stateID = s;
indexJkey = ijk;
wktGeomObj = GeometryEngine.geometryFromWkt(wktPoly, 0, Geometry.Type.Unknown);
}
}
//building quadTree Index from WKT multiPolygon and creating an iterator
private void buildQuadTree(){
quadTree = new QuadTree(new Envelope2D(-180, -90, 180, 90), 8);
Envelope envelope = new Envelope();
int j=0;
while(j<nodes.size()){
nodes.get(j).wktGeomObj.queryEnvelope(envelope);
quadTree.insert(j, new Envelope2D(envelope.getXMin(), envelope.getYMin(), envelope.getXMax(), envelope.getYMax()));
}
quadTreeIter = quadTree.getIterator();
}
/**
* Query the quadtree for the feature containing the given point
*
* #param pt point as longitude, latitude
* #return index to feature in featureClass or -1 if not found
*/
private int queryQuadTree(Point pt)
{
// reset iterator to the quadrant envelope that contains the point passed
quadTreeIter.resetIterator(pt, 0);
int elmHandle = quadTreeIter.next();
while (elmHandle >= 0){
int featureIndex = quadTree.getElement(elmHandle);
// we know the point and this feature are in the same quadrant, but we need to make sure the feature
// actually contains the point
if (GeometryEngine.contains(nodes.get(featureIndex).wktGeomObj, pt, spatialReference)){
return featureIndex;
}
elmHandle = quadTreeIter.next();
}
// feature not found
return -1;
}
/**
* Sets up mapper with filter geometry provided as argument[0] to the jar
*/
#Override
public void setup(Context context)
{
Configuration config = context.getConfiguration();
spatialReference = SpatialReference.create(4326);
// first pull values from the configuration
String featuresPath = config.get("sample.features.input");
//get column reference from driver class
wktZip = config.getInt("sample.features.col.zip", 0);
wktGeom = config.getInt("sample.features.col.geometry", 18);
wktStateID = config.getInt("sample.features.col.stateID", 3);
latitudeIndex = config.getInt("samples.csvdata.columns.lat", 5);
longitudeIndex = config.getInt("samples.csvdata.columns.long", 6);
FSDataInputStream iStream = null;
try {
// load the text WKT file provided as argument 0
FileSystem hdfs = FileSystem.get(config);
iStream = hdfs.open(new Path(featuresPath));
BufferedReader br = new BufferedReader(new InputStreamReader(iStream));
String wktLine ;
int i=0;
while((wktLine = br.readLine()) != null){
String [] val = wktLine.split("\\|");
String qtZip = val[wktZip];
String poly = val[wktGeom];
String stID = val[wktStateID];
ZipPolyClass zpc = new ZipPolyClass(i, qtZip, poly, stID);
nodes.add(i,zpc);
i++; // increment in the loop before end
}
}
catch (Exception e)
{
e.printStackTrace();
}
finally
{
if (iStream != null)
{
try {
iStream.close();
} catch (IOException e) { }
}
}
// build a quadtree of our features for fast queries
if (!nodes.isEmpty()) {
buildQuadTree();
}
}
#Override
public void map(LongWritable key, Text val, Context context)
throws IOException, InterruptedException {
/*
* The TextInputFormat we set in the configuration, by default, splits a text file line by line.
* The key is the byte offset to the first character in the line. The value is the text of the line.
*/
String line = val.toString();
String [] values = line.split(",");
// get lat long from file and convert to float
float latitude = Float.parseFloat(values[latitudeIndex]);
float longitude = Float.parseFloat(values[longitudeIndex]);
// Create our Point directly from longitude and latitude
Point point = new Point(longitude, latitude);
int featureIndex = queryQuadTree(point);
// Each map only processes one record at a time, so we start out with our count
// as 1. Since we have a distinct record file we will not run reducer
IntWritable one = new IntWritable(1);
if (featureIndex >= 0){
String zipTxt =nodes.get(featureIndex).zipCode;
String stateIDTxt = nodes.get(featureIndex).stateID;
String latTxt = values[latitudeIndex];
String longTxt = values[longitudeIndex];
String pointTxt = point.toString();
String name;
name = zipTxt+"\t"+stateIDTxt+"\t"+latTxt+"\t"+longTxt+ "\t" +pointTxt;
context.write(new Text(name), one);
} else {
context.write(new Text("*Outside Feature Set"), one);
}
}
}

I was able to resolve the out of memory issue by modifying the arrayList < classObject > to just hold arrayList < geometry > type.
Creating a class object (around 50k) to hold each row of a text file, consumed all the java heap memory. After this change code ran fine even in a 1-node virtual sandbox. I was able to crunch around 40 million rows in around 6 minutes.

Gridsim: Extend functionality in network flow

I'm working on gridsim project in Java eclipse. I have found a network flow program, which works only for one-to-one connection between the sender and receiver. If the same user (sender) wish to send a message to any other receiver, the program does not work. Similarly, if a receiver wish to send message to two sender users, it does not work. Here, I'm including all the java files for this work. In order to run the program, we need to include external .jar file path in the project. The gridsim.jar and simjava2.jar files can be downloaded from http://sourceforge.net/projects/gridsim/
The following are the programs. The main program is FlowNetEx01.java
package network.flow.example01;
import gridsim.*;
import gridsim.net.*;
import gridsim.net.flow.*;
import java.util.*;
// Test Driver class for this example
public class FlowNetEx01
{
// Creates main() to run this example
public static void main(String[] args)
{
System.out.println("Starting network example ...");
try
{
int num_user = 4; // number of grid users
Calendar calendar = Calendar.getInstance();
boolean trace_flag = false; // mean trace GridSim events
System.out.println("Initializing GridSim package");
// It is essential to set the network type before calling GridSim.init()
GridSim.initNetworkType(GridSimTags.NET_FLOW_LEVEL);
GridSim.init(num_user, calendar, trace_flag);
// In this example, the topology is:
// user(s) --10Mb/s-- r1 --1.5Mb/s-- r2 --10Mb/s-- GridResource(s)
Router r1 = new FlowRouter("router1", trace_flag); // router 1
Router r2 = new FlowRouter("router2", trace_flag); // router 2
String sender1 = "user1";
String receipient1 = "test1";
String sender2 = "user2";
String receipient2 = "test2";
// these entities are the senders
FlowNetUser user1 = new FlowNetUser(sender1, receipient2, 5.0);
FlowNetUser user2 = new FlowNetUser(sender2, receipient1, 20.0);
// these entities are the receipients
FlowTest test1 = new FlowTest(receipient1, sender2);
FlowTest test2 = new FlowTest(receipient2, sender1);
// The schedulers are redundent and will be stripped out soon
FIFOScheduler userSched1 = new FIFOScheduler("NetUserSched_0");
r1.attachHost(user1, userSched1);
FIFOScheduler userSched2 = new FIFOScheduler("NetUserSched_1");
r1.attachHost(user2, userSched2);
FIFOScheduler testSched1 = new FIFOScheduler("FlowTestSched_0");
r2.attachHost(test1, testSched1);
FIFOScheduler testSched2 = new FIFOScheduler("FlowTestSched_1");
r2.attachHost(test2, testSched2);
//////////////////////////////////////////
// Second step: Creates a physical link
double baud_rate = 1572864; // bits/sec (baud) [1.5Mb/s]
double propDelay = 300; // propagation delay in millisecond
int mtu = Integer.MAX_VALUE;; // max. transmission unit in byte
Link link = new FlowLink("r1_r2_link", baud_rate, propDelay, mtu);
FIFOScheduler r1Sched = new FIFOScheduler("r1_Sched");
FIFOScheduler r2Sched = new FIFOScheduler("r2_Sched");
r1.attachRouter(r2, link, r1Sched, r2Sched);
//////////////////////////////////////////
// Final step: Starts the simulation
GridSim.startGridSimulation();
System.out.println("\nFinish network example ...");
}
catch (Exception e)
{
e.printStackTrace();
System.err.print(e.toString());
System.out.println("Unwanted errors happen");
}
}
} // end class
Program-2:
package network.flow.example01;
import gridsim.*;
import gridsim.net.*;
import gridsim.net.flow.*;
import eduni.simjava.*;
import java.util.*;
public class FlowNetUser extends GridSim
{
private int myID_; // my entity ID
private String name_; // my entity name
private String destName_; // destination name
private int destID_; // destination id
private double wait_; // Delay until I begin sending
public static final int SEND_MSG = 1;
public static final int ACK_MSG = 2;
public FlowNetUser(String name, String destName, Link link, double wait) throws Exception
{
super(name, link);
// get this entity name from Sim_entity
this.name_ = super.get_name();
// get this entity ID from Sim_entity
this.myID_ = super.get_id();
// get the destination entity name
this.destName_ = destName;
// get the waiting time before sending
this.wait_ = wait;
}
public FlowNetUser(String name, String destName, double wait) throws Exception
{
// 10,485,760 baud = 10Mb/s
super(name, new FlowLink(name+"_link",10485760,450,Integer.MAX_VALUE));
// get this entity name from Sim_entity
this.name_ = super.get_name();
// get this entity ID from Sim_entity
this.myID_ = super.get_id();
// get the destination entity name
destName_ = destName;
// get the waiting time before sending
this.wait_ = wait;
}
public void body()
{
int packetSize = 524288000; // packet size in bytes [5MB]
//int packetSize = 52428800; // packet size in bytes [50MB]
//int packetSize = 524288000; // packet size in bytes [500MB]
//int packetSize = 5242880000; // packet size in bytes [5000MB]
int size = 3; // number of packets sent
int i = 0;
// get the destination entity ID
this.destID_ = GridSim.getEntityId(destName_);
//super.sim_pause(this.wait_);
this.gridSimHold(this.wait_);
// sends messages over the other side of the link
for (i = 0; i < size; i++)
{
String msg = "Message_" + i;
IO_data data = new IO_data(msg, packetSize, destID_);
System.out.println(name_ + ".body(): Sending " + msg +
", at time = " + GridSim.clock() );
// sends through Output buffer of this entity
super.send(super.output, GridSimTags.SCHEDULE_NOW,
GridSimTags.FLOW_SUBMIT, data);
//super.sim_pause();
super.sim_pause(10.0);
//this.gridSimHold((Math.random()*10)+1.0);
}
// get the ack back
Object obj = null;
for (i = 0; i < size; i++)
{
// waiting for incoming event in the Input buffer
obj = super.receiveEventObject();
System.out.println(name_ + ".body(): Receives Ack for " + obj);
}
// Wait for other FlowNetUser instances to finish
this.gridSimHold(1000.0);
super.send(destID_, GridSimTags.SCHEDULE_NOW,
GridSimTags.END_OF_SIMULATION);
// shut down I/O ports
shutdownUserEntity();
terminateIOEntities();
System.out.println(this.name_ + ":%%%% Exiting body() at time " +
GridSim.clock() );
}
} // end class
Program-3:
package network.flow.example01;
import java.util.*;
import gridsim.*;
import gridsim.net.*;
import gridsim.net.flow.*;
import gridsim.util.SimReport;
import eduni.simjava.*;
public class FlowTest extends GridSim
{
private int myID_; // my entity ID
private String name_; // my entity name
private String destName_; // destination name
private int destID_; // destination id
private SimReport report_; // logs every activity
public FlowTest(String name, String destName, Link link) throws Exception
{
super(name, link);
// get this entity name from Sim_entity
this.name_ = super.get_name();
// get this entity ID from Sim_entity
this.myID_ = super.get_id();
// get the destination entity name
this.destName_ = destName;
// logs every activity. It will automatically create name.csv file
report_ = new SimReport(name);
report_.write("Creates " + name);
}
public FlowTest(String name, String destName) throws Exception
{
// 10,485,760 baud = 10Mb/s
super(name, new FlowLink(name+"_link",10485760,250,Integer.MAX_VALUE));
// get this entity name from Sim_entity
this.name_ = super.get_name();
// get this entity ID from Sim_entity
this.myID_ = super.get_id();
// get the destination entity name
this.destName_ = destName;
// logs every activity. It will automatically create name.csv file
report_ = new SimReport(name);
report_.write("Creates " + name);
}
public void body()
{
// get the destination entity ID
this.destID_ = GridSim.getEntityId(destName_);
int packetSize = 1500; // packet size in bytes
Sim_event ev = new Sim_event(); // an event
// a loop waiting for incoming events
while ( Sim_system.running() )
{
// get the next event from the Input buffer
super.sim_get_next(ev);
// if an event denotes end of simulation
if (ev.get_tag() == GridSimTags.END_OF_SIMULATION)
{
System.out.println();
write(super.get_name() + ".body(): exiting ...");
break;
}
// if an event denotes another event type
else if (ev.get_tag() == GridSimTags.FLOW_SUBMIT)
{
System.out.println();
write(super.get_name() + ".body(): receive " +
ev.get_data() + ", at time = " + GridSim.clock());
// No need for an ack, it is handled in FlowBuffer now on our behalf
// sends back an ack
IO_data data = new IO_data(ev.get_data(), packetSize, destID_);
write(name_ + ".body(): Sending back " +
ev.get_data() + ", at time = " + GridSim.clock() );
// sends through Output buffer of this entity
super.send(super.output, GridSimTags.SCHEDULE_NOW,
GridSimTags.FLOW_ACK, data);
}
else if (ev.get_tag() == GridSimTags.INFOPKT_SUBMIT)
{
processPingRequest(ev);
}
}
// shut down I/O ports
shutdownUserEntity();
terminateIOEntities();
// don't forget to close the file
if (report_ != null) {
report_.finalWrite();
}
System.out.println(this.name_ + ":%%%% Exiting body() at time " +
GridSim.clock() );
}
private void processPingRequest(Sim_event ev)
{
InfoPacket pkt = (InfoPacket) ev.get_data();
pkt.setTag(GridSimTags.INFOPKT_RETURN);
pkt.setDestID( pkt.getSrcID() );
// sends back to the sender
super.send(super.output, GridSimTags.SCHEDULE_NOW,
GridSimTags.INFOPKT_RETURN,
new IO_data(pkt,pkt.getSize(),pkt.getSrcID()) );
}
private void write(String msg)
{
System.out.println(msg);
if (report_ != null) {
report_.write(msg);
}
}
} // end class
After running these programs, someone can tell us how to extend the required functionality as I mentioned in the beginning.

Personal experience ... GridSim5.2 potentially buggy.
The examples are dated written for version < 4.0, demonstrating not very complex scenarios.
Using version 5.2. According to the API docs every simulation should have at least one TopRegionalRC. This appends a UniqueID to the filename and records the location of the file in two hashmaps one for filename and the other for fileattr. Now the event filename used for the lookup remains unchanged - the lookup fails - compares to the fileattrmap.name_. Consequently, the waiting for ack block is never executed when performing a addmaster operation.
Fix: Since the UniqueId is returned by the initial request to the CTLG this can be appended to the filename for the subsequent event requiring the lookup. Alternatively, add the filename to fileattrmap and the filename+uniqueid to fileattrmap, then test for both in the lookup.
Also, GridSimTags uses -1 to signal END_OF_SIMULATION, but this conflicts with Advanced Reservation (AR) block of tags. That also use negative numbers. GridSimTags has an optional routine to check for duplicates but its use is optional and does not apply to DataGridTags. I created a reversemap for ease of debugging adding validation to ensure no duplicates occur and deprecated the GridSimTags method.
I am now wrestling with the DataGrid user tasks that do not seem to create events, I am also concerned that the delay operations are not effective.