text:
# How to Get Webhooks Into Your System Using Solace PubSub+ Cloud

In some cases you might want to get some data from cloud based systems to an environment that doesn’t expose APIs or ports to the outside world. Using webhooks makes that a little easier, but you still need a system that accepts webhooks and is able to get them across your firewall. That is exactly where Solace PubSub+ Cloud comes in! I’ve built a small webhook forwarder app, that gets data from Solace and sends it onwards without having any of my systems exposed to the Internet.

My initial quest to get this working started with looking at PubNub to get this done, and while relatively successful there was one major feature I was missing. In some of my use cases, I needed to get the HTTP header information (like the X-GitHub-Delivery or X-Hub-Signature when using GitHub webhooks) which wasn’t possible with PubNub.

Luckily, one of my friends, Jeremy Meiss, pointed me to Solace and said that would be the answer to all my messaging needs. So, I decided to look into it and after a bit of head-scratching and reading docs, I managed to get it all working very nicely. To be fair, one of their best features is probably that it’s free 😇!

By default the Solace PubSub+ Cloud instance you get is set to “messaging” mode, which means that all non-standard HTTP headers are removed from the message when it is sent onwards. To fix that, you can set the instance to “microgateway” mode, which will add any received HTTP header fields not specifically mentioned to the Solace message as metadata. As you go into the web-console of your Solace instance, you can change the mode in Message VPN -> Connectivity. The mode should be set to “Gateway

As you set it to gateway, the Solace microgateway will add all non-standard headers to your message as JMS_Solace_HTTP_field_<header>.

The other thing I discovered, is that in most REST based requests, you’ll not be adding a correlation ID. The Microgateway however, uses that field to correlate the request ith the response. If that field is absent, the gateway will generate a field called appMessageID and use that for correlation between request and reply.

The problem is that the default session.sendReply() method takes the correlationID of the original message if you provide a message to reply to (which I found out after reading the API docs for the session object). When I manually construct the reply, and set the appropriate destination and correlation ID headers, it works perfectly. So in my case, the response should have been constructed as:

var reply = solace.SolclientFactory.createMessage();


text:
# Import of the field notebook#

Back from the field, the plugin offers an import tool to automatically integrate the field notebook:

## Options#

The import is done by selecting:

• the codification file used for the field survey (.qlsc)

• the CSV file with the data from the field survey

• the archive directory (where the export will take place)

• the level of logging (log)

• the file of logging (log)

## Processing#

The plugin copies the source [GeoPackage] in the archive directory. If no path is specified QGIS creates a temporary directory before copying the data into it.

LSCI[^lsci] also offers you to log these different steps, especially useful for developers in case of problems (DEBUG), but also for you (INFO) to tell you what has been done and if there are errors in your codification.

## Output result#

You have the possibility to redo the processing as many times as you want. If you notice an error, you can manually correct your drawing or take your field file and reimport it.

Note

As the plugin is integrated in QGIS and especially the processings, it is possible to chain the import with other processings.

## Principle of CSV processing to your GeoPackage#

This part details the technical functioning of the plugin. It is not necesary to use it.

### Separation of codes#

The tool will separate the codes that are on the same point, and then match them according to their code. For example a CSV like:

2,1980244.900,5190520.938,1002.461,300+200
3,1980249.438,5190515.953,1002.329,101
1,1980242.941,5190519.460,1002.521,200+300

will be transformed into:

    2,1980244.900,5190520.938,1002.461,300
2,1980244.900,5190520.938,1002.461,200
3,1980249.438,5190515.953,1002.329,101
1,1980242.941,5190519.460,1002.521,200
1,1980242.941,5190519.460,1002.521,300

then:

    3,1980249.438,5190515.953,1002.329,101
2,1980244.900,5190520.938,1002.461,200
1,1980242.941,5190519.460,1002.521,200
2,1980244.900,5190520.938,1002.461,300
1,1980242.941,5190519.460,1002.521,300

The tool processes the codes in a row, special attention is required to the operator entering the codes because an error in entering will shift the codification and the resulting drawing.

### Conversion of points into geometries#

Once the CSV is processed internally, the plugin will read it sequentially and convert the points into geometries following the code.

### Format#

At the moment, the plugin can only read point files in CSV format. This one is often present of the devices. They will have to respect this positioning:

numéro,x,y,z,code,att1,att2, [...] attn

where:

• numéro is the number of the point,

• x, y and z the coordinates,

• code the code(s) on the point,

• att1, att2 and attn are the attributes that can be exported from your field notebook. There is no limit on the number of attributes.

1

acronym of the Land Survey Codes Import plugin name

format: plain


text:
# Objected-Oriented Hangman game In Python

I'm still a beginner in python and made this simple program to practice OOP, I've ended up using the self keyword a lot and I'm not sure if this is normal or if I just misunderstood something.

Any criticism on the program itself or good practices in python are also greatly appreciated.

(Text File 'Words' is just 3000+ English words listed)

Edit: Instance variable cont was only used in 1 function so it was switched to a local variable inside function play

Hangman.py

import random
from phases import hang_phases

class HangMan:
def __init__(self, random_word, letters):
self.word = random_word
self.letters = letters
self.excluded_letters = []
self.included_letters = []
self.original_word = random_word
self.chances = 9

def remove_letter(self, guessed_letter):
self.word = self.word.replace(guessed_letter, "")
self.included_letters.append(guessed_letter)

def guess_letter(self):
print(hang_phases[self.chances])
guessed_letter = input("Guess A Letter: ").upper()

if guessed_letter in self.excluded_letters:
elif guessed_letter in self.word:
number_of_letter = self.word.count(guessed_letter)
if number_of_letter == 1:
print(f"There is {number_of_letter} {guessed_letter}")
else:
print(f"There are {number_of_letter} {guessed_letter}'s")
self.remove_letter(guessed_letter)
else:
print(f"There are no {guessed_letter}'s")
self.excluded_letters.append(guessed_letter)
self.chances -= 1

def play(self):
cont = True
while cont:
if self.word == "":
print(f"Congrats! The Word Was {self.original_word}")
cont = False
else:
try:
if self.chances == 0:
print("The Man Has Been Hung! Better Luck Next Time :/")
print(f"The Word Was {self.original_word}")
print(hang_phases[0])
cont = False
else:
print()
print("What Would You Like To Do?:\n*Please Enter The Integer Value Corresponding To The Action")
print("1. Guess A Letter\n"
"2. Check Included Guessed Letters\n"
"3. Check Excluded Guessed Letters\n"
"4. Get Length Of The Word\n"
"5. Give Up\n")

action = int(input("> "))
int(action)
if action == 1:
self.guess_letter()
elif action == 2:
if len(self.included_letters) == 0:
print("You Have Not Guessed Any Letters That Are In The Word")
else:
print(self.included_letters)
elif action == 3:
if len(self.excluded_letters) == 0:
print("You Have Not Guessed Any Letters That Are Not In The Word")
else:
print(self.excluded_letters)
elif action == 4:
print(len(self.original_word))
elif action == 5:
print(f"The Word Is {self.original_word}")
cont = False
else:
print("Pleas Enter A Value Between 1 & 5")
except ValueError:

def main():
play = True
while play:
word = random.choice(word_list).upper()

hm = HangMan(word, len(word))
hm.play()
print("Play Again?")
play_again = input("Y/n > ").upper()
if play_again == "Y":
print("Setting Up...")
elif play_again == "N":
print("Goodbye")
play = False

if __name__ == "__main__":
main()

phases.py

hang_phases = [
'''
______    rip lol.
|     |
|     O
|    \|/
|     |
|   _/ \_
_|_
'''
,
'''
______
|     |
|     O
|    \|/
|     |
|   _/ \\
_|_
'''
,
'''
______
|     |
|     O
|    \|/
|     |
|   _/
_|_
'''
,
'''
______
|     |
|     O
|    \|/
|     |
|    /
_|_
'''
,
'''
______
|     |
|     O
|    \|/
|     |
|
_|_
'''
,
'''
______
|     |
|     O
|    \|/
|
|
_|_
'''
,
'''
______
|     |
|     O
|    \|
|
|
_|_
'''
,
'''
______
|     |
|     O
|     |
|
|
_|_
'''
,
'''
______
|     |
|     O
|
|
|
_|_
'''
,
'''
______
|     |
|
|
|
|
_|_
'''
]

Simpler UI. A lot of your code is devoted to managing the user-interface, which is organized around five numbered choices (guess letter, see successful guesses, see failed guesses, see length of word, or give up). As a user, I find that process very tedious: type 1, press enter, type a letter, press enter, press 1, type letter, press enter, type 4, see length of word, etc etc etc. All of that hassle is unnecessary because you could easily display all of the relevant information all of the time. Furthermore, that's what most Hangman implementations do. The other thing that most Hangman games do is tell you where the successful guesses reside within the entire word. Here's a mock-up showing everything the user needs to know, followed by a prompt waiting for the next guess. And if the user just presses enter, you can interpret that as "give up", which would eliminate the need for a numbered UI. That relatively simple adjustment to your plan would substantially reduce the amount of code needed while also making life better for users:

  ______
|     |
|     O
|    \|/
|     |
|
_|_

Word         T _ D I O U _

Missses      A C F R Z

>

Strings are sequences with stealth members. Python strings are great because they are built on top of sequences, which means you can easily iterate over them character by character. However, strings are not merely character sequences, and naive membership queries can sometimes go awry. In your case, if a user guessing a letter just presses enter rather than typing a letter, your program incorrectly reports that the "letter" (in this case the empty string) appears in the word N + 1 times, where N is the length of the word. And for strings, that is technically correct: the empty string can be found before every letter and at the end of the string. That makes sense from a computer-sciency point of view of strings, but it's not intuitive if we view strings purely as a sequence of characters.

Don't force the caller to do routine work. A user of your HangMan class is required to supply both a word and the number of letters in that word. A friendlier hangman would do that computation for the victim, free of charge.

Boundaries: consider the purpose of the HangMan class. It's good to find projects to practice OOP. One part of that learning process it developing solid instincts about boundaries: what belongs in the class and what does not. These can be tricky questions and software engineers have a variety of opinions about how to draw such lines. If it were me, I would probably treat HangMan as nothing more that a dataclass to keep basic facts about game state (the word, prior guesses) and a variety of derived or computable attributes (prior hits, prior misses, number of remaining guesses). In other words, the class would not collect user input or orchestrate the playing of the game. Such matters would be handled elsewhere, maybe in a different class, maybe in a static method of the class, or in an ordinary function (I would probably opt for the latter). Why that division? Because most of the algorithmic aspects of the code are questions about data: letters in the word, prior guesses, remaining guesses before death. I want algorithmic logic in data-oriented code that can be easily tested and debugged. I don't want such code to be burdened by the annoyances of user-interface. Meanwhile, I want the UI portions of the code to be very simple from an algorithmic/logical point of view so that I don't need to spend too much effort with testing and debugging. I want the inevitably annoying UI-oriented code to be as free of logical complexity as possible: just procedural step-by-step stuff whenever possible. Gary Bernhardt's talk on the subject of class boundaries is a classic in this line of thought.


text:
Berkeley Notes
Search…
⌃K

# Random Graphs

A random graph is one which is generated through some amount of randomness.

## Definition 76

An Erdos-Renyi random graph
$G(n, p)$
is an undirected graph on
$n \geq 1$
vertices where each edge exists independently with probability
$p$
.
With random graphs, we often ask what happens to particular properties as
$n\to\infty$
and
$p$
scales with some relationship to
$n$
. In particular, we want that property to hold with high probability (i.e, as
$n\to\infty$
, the probabilty that
$G(n,p)$
has the property approaches 1).

## Theorem 42

Every monotone graph property (adding more edges doesn't delete the property) has a sharp threshold
$t_n$
where if
$p \gg t_n$
, then
$G(n, p)$
has
$p$
with high probability and does not have
$p$
with high probability if
$t_n \ll G(n,p)$
.
One example of a threshold is the connectivity threshold.

## Theorem 43 (Erdos-Renyi Connectivity Theorem)

Fix
$\lambda > 0$
and let
$p_n = \lambda \frac{\log n}{n}$
. If
$\lambda > 1$
, then
$P(G(n,p_n)\text{ is connected})$
with probability approaching 1, and if
$\lambda < 1$
, then
$P(G(n,p_n)\text{ is disconnected})$
with probability approaching 1


text:
Just for fun

1. Jun 23, 2012

vish22

Ok just for fun,could someone please give a general linear transformation of p vectors in R(n) to R(m),by expressing the transformation as a Matrix vector product of lets say n vectors in R(m).p vectors in R(n).I've already done it for fun but I'd like to see how you guys go about it..

2. Jun 23, 2012

HallsofIvy

When you say "a general linear transformation of p vectors", what do you mean by "p vectors"?

3. Jun 24, 2012

vish22

as in "p" no. of vectors in R(n)

4. Jun 24, 2012

pwsnafu

Your problem is not worded properly. So we have $v_1, \ldots, v_p \in \mathbb{R}^n$ and you want a matrix that does what?

I really don't understand the point of the p. Do you want a matrix with $v_i$ as column vectors, and apply a GLT to the resulting matrix?

5. Jun 25, 2012

vish22

yea,the 1st part is right.. v1,v2...vp vectors in N-space-a GLT of these vectors expressed as a product with N vectors (represented in a matrix ofc) in M-space!

6. Jun 25, 2012

HallsofIvy

I don't consider trying to guess what you mean to be "fun".


text:
>> Home >> Resources & support >> FAQs >> Missing R-squared for 2SLS/IV

## For three-stage least-squares (3SLS/reg3) estimates, why are the R-squared and model sum of squares sometimes negative?

 Title Negative and missing R-squared for 2SLS/IV Authors William Sribney, Vince Wiggins, and David Drukker, StataCorp

### Background

Two-stage least-squares (2SLS) estimates, or instrumental variables (IV) estimates, are obtained in Stata using the ivregress command.

ivregress sometimes reports no R2 and returns a negative value for the model sum of squares in e(mss).

Three-stage least-squares (3SLS) estimates are obtained using reg3. reg3 sometimes reports a negative R2 and model sum of squares. The discussion below focuses on 2SLS/IV; the issues for 3SLS are the same.

Missing R2s, negative R2s, and negative model sum of squares are all the same issue.

Stata’s ivregress command suppresses the printing of an R2 on 2SLS/IV if the R2 is negative, which is to say, if the model sum of squares is negative.

Whether a negative R2 should be reported or simply suppressed is a matter of taste. At any rate, the R2 really has no statistical meaning in the context of 2SLS/IV.

If it makes you feel better, you can compute the R2 yourself from the returned results (see An example section of the FAQ).

For two-stage least squares, some of the regressors enter the model as instruments when the parameters are estimated. However, since our goal is to estimate the structural model, the actual values, not the instruments for the endogenous right-hand-side variables, are used to determine the model sum of squares (MSS). The model’s residuals are computed over a set of regressors different from those used to fit the model. This means a constant-only model of the dependent variable is not nested within the two-stage least-squares model, even though the two-stage model estimates an intercept, and the residual sum of squares (RSS) is no longer constrained to be smaller than the total sum of squares (TSS). When RSS exceeds TSS, the MSS and the R2 will be negative.

### The long answer—How can an R2 be negative?

The formula for R-squared is

R2 = MSS/TSS

where

MSS = model sum of squares = TSS − RSS and
TSS = total sum of squares = sum of (y − ybar)2 and
RSS = residual (error) sum of squares = sum of (y − Xb)2

For your model, MSS is negative, so R2 would be negative.

MSS is negative because RSS is greater than TSS. RSS is greater than TSS because ybar is a better predictor of y (in the sum-of-squares sense) than Xb!

How can Xb be worse than ybar, especially when the model includes the constant term? At first glance, this seems impossible. But it is possible with the 2SLS/IV model.

Here are the background essentials:

Let Z be the matrix of instruments (say, z1, z2, z3, z4).

Let X be the matrix of regressors (say, y2, y3, z3, z4, where y2 and y3 are endogenous and z3 and z4 are exogenous).

Let y be the endogenous variable of interest. That is, we want to estimate b, where

y = Xb + error

Let P = Z (Z'Z)−1 Z' be the projection matrix into the space spanned by Z.

2SLS/IV gives point estimates

b = ((PX)' PX)-1 (PX)' y

The coefficients are simply those from an ordinary regression but with the predictors in the columns of PX (the projection of X into Z space).

Let’s assume you have two endogenous right-hand-side variables (y1 and y2), two exogenous variables (x1 and x2), and two instruments not in the structural equation (z1 and z2). This makes your structural equation

y = (Y)B1 + (X)B2 + e

or

y = b1*y1 + b2*y2 + b3*x1 + b3*x2 + e

(where B1 and B2 are components of the vector of coefficients—b). If you run the following,

 . regress y1 x1 x2 z1 z2
. predict yhat1
. regress y2 x1 x2 z1 z2
. predict yhat2
. regress y yhat1 yhat2 x1 x2

you will get exactly the coefficients of the 2SLS/IV model (but you will get different standard errors):

 . ivregress 2sls y (y1 y2 = z1 z2) x1 x2

Now if we computed residuals after

 . regress y yhat1 yhat2 x1 x2

the residuals would be

r = y − (PX)b

The sum of squares of these residuals would always be less than the total sum of squares.

But these are not the right residuals for 2SLS/IV. Because we are fitting a structural model, we are interested in the residuals using the actual values of the endogenous variables.

The correct two-stage least-squares residuals are

e = y − Xb

Here there is no guarantee that the sum of these residuals squared are less than the total sum of squares. These residuals do not come from a model that nests a constant-only model of y.

### An example

Let’s take a simple, and admittedly silly, example from our favorite dataset—auto.dta.

 . sysuse auto, clear
(1978 Automobile Data)

. ivregress 2sls price (mpg = foreign) headroom

Instrumental variables (2SLS) regression               Number of obs =      74
Wald chi2(2)  =    1.15
Prob > chi2   =  0.5619
R-squared     =       .
Root MSE      =  3363.6

price        Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

mpg     154.4941   239.2968     0.65   0.519     -314.519    623.5072
headroom     836.4137   821.6528     1.02   0.309    -773.9962    2446.824
_cons       371.36   7268.765     0.05   0.959    -13875.16    14617.88

Instrumented:  mpg

. display "MSS: " %15.0f e(mss)
MSS:      -202135715

There is your negative model sum of squares (−202135715). The model sum of squares is just the improvement over the sum of squares about the mean given by the full model. In this example, the sum of squared residuals from the model predictions is 837201111, whereas the sum of squared residuals about the mean of price is 635065396. By computing the model sum of square as

. display "MSS: " %15.0f 635065396 -  837201111
MSS:      -202135715

we can see that our model actually performs worse than the mean of price. Why didn’t our constant keep this from happening? The coefficients are estimated using an instrument for mpg. Thus the constant need not provide an intercept that minimizes the sum of squared residuals when the actual values of the endogenous variables are used.

Just to be sure, let’s perform the sum of square computations by hand.

To get the sum of squared residuals for our model, type

 . predict double errs, residuals

. gen double errs2 = errs*errs

. summarize errs2

Variable         Obs        Mean    Std. Dev.       Min        Max

errs2          74    1.13e+07    2.01e+07    3017.3    9.57e+07

. display "ESS: " %15.0f r(sum)
ESS:       837201111

which agrees exactly with the returned results from ivregress.

 . display "ESS: " %15.0f e(rss)
ESS:       837201111

To get the total sum of squared residuals about the mean of price, type

 . summarize price

Variable         Obs        Mean    Std. Dev.       Min        Max

price          74    6165.257    2949.496       3291      15906

. gen double pbarErr2 = (price - r(mean))^2

. summarize pbarErr2

Variable         Obs        Mean    Std. Dev.       Min        Max

pbarErr2          74     8581965    1.69e+07    .065924   9.49e+07

. display "TSS:  " %15.0f r(sum)
TSS:        635065396

So, our “hand” computations also give a model sum of squares of −202135715 and agree with the value returned by ivregress.

### Is a negative R2 a problem?

What does it mean when RSS is greater than TSS? Does this mean our parameter estimates are no good? Not really. You can easily develop simulations where the parameter estimates from two-stage are quite good while the MSS is negative. Remember why we fit two-stage models. We are interested in the parameters of the structural equation—the elasticity of demand, the marginal propensity to consume, etc. If our two-stage model produces estimates of these parameters with acceptable standard errors, we should be happy—regardless of MSS or R2. If we were interested strictly in projections of the dependent variable, we should probably consider the reduced form of the model.

Another way of stating this point is that there are models in which in the distribution of 2SLS estimates of the parameters will be well approximated by its theoretical distribution but that the R2 computed from some samples will be negative. There are several ways of illustrating this point. Perhaps the most accessible is via simulation.

We simulate data from the model

(1) y = 1 + − .1*x + e1 + e2

(2) x = w + z + c1 + .5*e1

(3) z = 1.5*c1 + e3

where e1, e2, w, and c1 are all independent normal random variables. The c1 term in (2) and (3) provide the correlation between x and z. The e1 term in (1) and (2) is the source of the correlation between x and the error term (e1 + e2) for y. The coefficient of −0.1 is the parameter that we are trying to estimate. We are going to estimate this parameter with 2SLS using ivregress with y as the dependent variable, x as the endogenous variable, and z as the instrument for x. For each simulated sample, we construct y, x, and z using independent draws of the standard normal variables e1, e2, w, and c1 and (1)–(3). Then we use

 . ivregress 2sls y (x = z)

to estimate the coefficient −0.1. For each simulated sample, we record the following statistics:

 b1 estimate of the coefficient (−.1) p p of the null hypothesis that b1 = −.1 reject if p<.05 and 0 otherwise r2 computed R2 (missing if mss < 0) mss value of the model sum of squares rho_x1e correlation between x1 and e=e1+e2 rho_x1z1 correlation between x1 and z1 fsf first stage F statistic p_fsf p-value from the first stage F statistic

The Stata code for drawing 2,000 simulations of this model, estimating the coefficient −0.1, computing the statistics of interest, and finally, summarizing the results, is saved in the file negr2.do. Each simulated sample contains 1,000 observations, so the results should not be attributed to a small sample size.

Here are the results we obtained with the summarize command:

 . summarize

Variable         Obs        Mean    Std. Dev.       Min        Max

b1        2000   -.0981982    .0541345  -.2771809   .0765793
p        2000    .4945649    .2884685   .0002706   .9995125
reject        2000       .0485     .214874          0          1
r2          64    .0068443    .0063426    .000051   .0264567
mss        2000    -78.4407    49.08486  -273.4773   47.94914

rho_x1e        2000     .235859    .0300348   .1194255   .3460462
rho_x1z1        2000    .5556971    .0216154   .4764362   .6183904
fsf        2000     448.584    50.32493   293.0595   617.9501
p_fsf        2000    2.62e-34    7.49e-33          0   3.29e-31

The results for rho_x1e, rho_x1z1, fsf, and p_fsf indicate the correlations between the endogenous variable and the error term and between the endogenous variable and its instrument are reasonable and there is no weak-instrument problem. The results for b1, p, and reject indicate that the mean estimate of the coefficient on x is very close to its true value of −0.1 and that there is no size distortion of the test that coefficient on x = −0.1. In short, the distribution of the estimates, b1, is very well approximated by its theoretical asymptotic distribution. Together, these results imply that the 2SLS estimator is performing according to the theory in these simulations.

There are only 48 observations on r2 because there are 1,952 observations in which mss < 0.

 . count if mss < 0
1936

Thus the results illustrate that there is at least one model for which the distribution of the 2SLS estimates of the parameters is very well approximated by its asymptotic distribution but that the R2 will be negative in most of the individual samples. To obtain more models that produce the same qualitative results, simply change the coefficient −0.1 by a small amount. As one would expect, increasing the coefficient −0.1 reduces the fraction of the of simulated samples that produce a negative R2.


text:
# Load Balancing in Distributed System

October 10, 2017

A serious difficulty in concurrent programming of a distributed system is how to deal with scheduling and load balancing of such a system which may consist of heterogeneous computers. Load balancing comes under the area of resource management. In this section we describe the detail overview of the load balancing is a distributed environment in the scheduling perspectives.

With the great advancements in computer technology and the availability of many distributed systems, the problem of load balancing in distributed systems has gained a higher attention and importance. A dynamic load balancing algorithm assumes no a priori knowledge about job behavior or the global state of the system, i.e., load balancing decisions is solely based on the current status of the system. The development of an effective dynamic load balancing algorithm involves many important issues: load estimation, load levels comparison, performance indices, system stability, amount of information exchanged among nodes, job resource requirements estimation, job’s selection for transfer, remote nodes selection, and more.

Outstanding progress in computer technology has lead to the rise of the demand of high speed processing and the need of high scalability, availability and rapid response. This resulted in use of parallel and distributed computing systems where more than one processor processes the job simultaneously. One of the main research issues in parallel and distributed system is effective strategy to distribute workload among multiple processors. Load balancing is used for minimizing the response time, maximizing the throughput, and to avoid the overload. Load balancing is to ensure that every processor in the system does approximately the same amount of work at any point of time.

The explosion of Internet has increased the amount of the online information and services available for the users and this growing information and services has placed distress on the Internet infrastructure. Any load balancing algorithm balances the load by transferring it from heavy loaded node to the lightly loaded node so as to minimize execution time. Load balancing makes every processor equally busy and make them finish the work approximately at the same time. In parallel and distributed systems more than one processor process parallel programs. The amount of processing time needed to execute all processes assigned to a processor is called workload of a processor. The amount of processing time needed to execute all processes assigned to processor is called load of a processor.

The main goal behind distributing load is to distribute process so as to maximize throughput, maintain stability, minimize resource utilization, minimize computational time, minimize communication delays and fault tolerance.

Load balancing is the process of distribution or redistribution of load among processor therefore improving the performance of the system.

Figure 1 Demonstration of Load Balancing

Figure 1 demonstrates the concept of load balancing. In computing, load balancing distributes workload across multiple computing resources, such as computers, a computer cluster, network links, or central processing units. Load balancing aims to optimize resource use, maximize throughput, minimize response time, and avoid overload of any single resource. Load balancing is the process of roughly equalizing the work load among all nodes of the distributed system. It strives to produce a global improvement in system performance. Load balancing is a mechanism that enables jobs to move from one computer to another within the distributed system. This creates faster job service e.g., minimize job response time1 and enhances resource utilization.

A distributed system contains number of processors working independently with each other and linked by communication channel. Some are not linked with any communication channel. Each processor has an initial load that is the amount of work to be performed, and each may have a different processing capacity. The workload has to be evenly distributed among all processors based on their processing speed so that time to execute all tasks gets minimized and idle time of each processor can be reduced. This is why we need load balancing. Load imbalance is also a main problem in data parallel applications and here also it mainly occurs due to the uneven distribution of data among the various processors in the system. Without good load distribution strategies and techniques, we cannot aim to reach good speedup and good efficiency.

### References

[1] Ali M. Alakeel, “A Guide to Dynamic Load Balancing in Distributed Computer Systems”, IJCSNS International Journal of Computer Science and Network Security, Volume 10 No.6, June 2010.

[2] Sharma, Eep, Sarabjit Singh, and Meenakshi Sharma, “Performance Analysis of Load Balancing Algorithms”, in proceeding of 38th World Academy of Science, Engineering and Technology, 2008.

[3] Abhijit A. Rajguru, S.S. Apt” A Comparative Performance Analysis of Load Balancing Algorithms in Distributed System using Qualitative Parameters”, International Journal of Recent Technology and Engineering (IJRTE), Volume-1, Issue-3, August 2012.

[4] Mohsen and HosseinDelda, “Balancing Load in a Computational Grid Applying Adaptive, Intelligent Colonies of Ants”, Informatica 32, PP. 327–335, 2008.

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Insert math as
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text:
# What are tally marks?

Tally marks are a way to represent data. In this method, it is usual to express numbers with the help of vertical lines.

The numbers are usually represented as groups of 5.

Tally marks representation of some numbers is shown below:

Tutorialspoint

Simply Easy Learning


text:
# The area of a rectangle is 72 cm^2. The length l, of the rectangle, is 6 cm longer than the...

## Question:

The area of a rectangle is 72 {eq}cm^2 {/eq}. The length l, of the rectangle, is 6 cm longer than the width, w.

What is the width of the rectangle?

a) 10 cm

b) 8 cm

c) 6 cm

d) 2 cm

## Rectangular Area:

The area of the rectangle is the product of the length and area. Where the length is the longer side of the rectangle and the width is the shorter side of the rectangle.

The width is c) 6 cm.

The area of the rectangle is {eq}\rm 72 \ cm^2 {/eq} and has a length of 6 cm longer than the width or {eq}l = w + 6 {/eq}.

Using the given information, determine the width from the formula for the area of a rectangle:

{eq}\begin{align} A &= lw \\ 72 &= w(w+6) \\ w^2 + 6w - 72 &=0 \end{align} {/eq}

Factoring:

{eq}(w +12)(w -6)=0 {/eq}

The factor that would have a positive answer is the second factor:

{eq}\begin{align} w - 6 &= 0 \\ w &= 6 \end{align} {/eq}

The width is {eq}\rm 6 \ cm. {/eq}


text:
# Which Unreal Engine 4 project files can I ignore in source control?

I want to put an Unreal Engine 4 project under source control with git. (I know the editor has Perforce and Subversion support, but I don't care for those.)

What folder and files do I include in the repository and which can I ignore?

The way I see it, the following is needed: Config, Content, Source, *.sln, *.uproject

Things that I can ignore: Binaries, DerivedDataCache, Intermediate, Saved, *.suo, *.sdf, *.opensdf

Is this correct?

• If there's a .sln, there's usually a .vXX.suo file (which is invisible, usually) which you may want to ignore since it contains user-specific options. – user1430 Mar 21 '14 at 15:41
• Github maintains a list of ignore files for various projects. There is no entry for Unreal4 yet. If you find the perfect list, perhaps you should submit a pull request to add it there. – Seth Battin Mar 21 '14 at 17:28
• I already ignored the VS temporary files, but thank you for making it obvious to everybody. I amended the question to reflect that. – rioki Mar 25 '14 at 9:26
• @rioki What's your impression about working with git? Yesterday was the firs time I worked with UE and saving the simple level used 500mb of my hard disk space. It's too much data for git to sync. – user44748 Apr 18 '14 at 9:32

I'm using GitHub for Windows which generates a nice gitignore ignoring most extraneous files like log files or files specific to VS that don't need to be shared.

From what I can gather from this page in the Unreal docs, you can probably ignore these directories:

DerivedDataCache
Intermediate
Saved

I'm not going to ignore Binaries, if only because I'm working with a level designer who doesn't have VS and so will need the DLL files I build (I assume, I could be wrong).

Again keep in mind I'm using Git for Windows that generates gitignore for you when you create a project, and it's pretty big. I'm pretty much only committing the following (where "ReallyCoolGame" is the name of your project):

.gitattributes
.gitignore
Binaries
Config
Content
Source
ReallyCoolGame.sln
ReallyCoolGame.uproject

I've only just committed this myself, and I've yet to test it with my level designer, so caveat emptor.

Also, I'm referring to a project created using the first-person C++ template, which gets saved to: E:\Documents\Unreal Projects\ReallyCoolGame (My Documents == E:\Documents on my machine) and not within the Unreal Engine source directory structure.

Edit: I also don't want to include all the default content, so I'm going to have a subdirectory within Content just for the game assets themselves with the same name as the project, and ignore all other subdirectories of Content. So the lines at the top of my .gitignore now look like:

DerivedDataCache
Intermediate
Saved
Content/*
!Content/ReallyCoolGame

And the level designer will put all of our assets in Content/ReallyCoolGame instead.

UPDATE:

The Intermediate directory actually contains Visual Studio's project files, which are needed to build the project. If the Intermediate directory is ignored, you won't be able to build the project because the Visual Studio solution won't find it. But there are two easy workarounds for this.

1) Just don't ignore the Intermediate directory. Of course in that case Visual Studio will still find all project files it needs and be able to build the project.

2) This one is even better, especially for version control: Open your project in Unreal Engine 4 editor and go to "File > Refresh Visual Studio Project". This will generate an all new solution file for you, which means you won't even need to commit it and can add the *.sln in the project folder to the .gitignore file.

Also *.suo and *.sdf files can be ignored in general, since Visual Studio just generates new ones when you open your solution.

• I solved the issue with the default content differently. I did not add the starter content into the generated project. Building up the materials to get started is really a piece of cake and once it gets serious I will have my own assets. This removes something like 100 MB of files from the project. – rioki Mar 25 '14 at 9:21

I don’t know the differences between the public version of UE4 and mine, but here is the .gitignore file I have been using:

Engine/Binaries/
Engine/DerivedDataCache/
Engine/Intermediate/
Engine/Plugins/*/*/Binaries/
Engine/Plugins/*/*/Intermediate/
Engine/Programs/UnrealPak/
Engine/Saved/
Engine/Source/*/*/*/obj/
Engine/Source/*/*/obj/
Engine/Source/*/obj/

UE4.opensdf
UE4.sdf
UE4.sln
UE4.suo
UE4.*.suo

GameName/Binaries/
GameName/DerivedDataCache/
GameName/Intermediate/
GameName/Saved/

• You have your game project withing the source three of the UE? If you have a custom build of the engine this may make sense, but I am using the stock engine. Basically I just need the GameName/* and VS ignores. – rioki Mar 25 '14 at 9:24
• Yes, the engine needs a lot of modifications, and upstream UE is regularly merged into the repository. – sam hocevar Mar 25 '14 at 10:02
• FWIW, when I was working with UE3 we also had our game project within the core UE source tree, and my impression was that this is SOP. I'll almost guarantee that you'll need to do engine mods as well, and it's much easier to keep track of everything if you consider UE+Your Game as a single 'entity'. – Steven Stadnicki Apr 10 '14 at 17:16

This is what I have in my .gitignore

#Unreal Engine
StarterContent/
Saved/
Binaries/
Build/
Debug/
Intermediate/
DerivedDataCache/