For example if "m going to run a query that will process 100mb of data but will require billing tier 12 will that be more expensive than a query that requires billing tier 1 but processes 500mb?
Cost of query execution is billing bytes x billing tier x $5 per 1 TB
so in your example
12 x 100 MB will have cost of of 2.4 times higher than 1 x 500 MB
just because of simple math - (12 x 100) / (1 x 500) = 2.4
Related
The title says it.
I was wondering if normalization has any effect on computing.
Does normalization effect computing?
It is costlier to compute 0.02 * 0.02 compared to 2 * 2 has 2 * 2 is a single math operation where only one multiplication is involved.
Floating point number are stored has 2*10^-2 format(scientific notation). Therefore, two operations are involved here,
2 * 2
(-2) + (-2)
Thus, the answer is computed as 4 * 10^(-4) or 0.0004.
Thus, 0.02 * 0.02 is costlier compared to 2*2.
I've been experiencing with GAMS but I still haven't got a clue what I'm doing.
Can someone take a look at this short model and try to point me in the right direction?
I have problems in the compilation at the equations, getting a few of these:
Dimension different - The symbol is referenced with more/less
indices as declared
Uncontrolled set entered as constant
Sets
i months / 1, 2, 3 /
j months / 1, 2, 3 /;
Parameters
cp(i) production cost in month i
/ 1 1.08
2 1.11
3 1.10
/
rh(i) number of necessary workers in month i
/ 1 3
2 4
3 6
/
cap(i) production capacity in month i
/ 1 25
2 20
3 25
/
q(j) number of motors to deliver in month j
/ 1 10
2 15
3 25
/
Scalar ca cost to store motors for a month /0.15/ ;
variables
mc(i,j) cost of production of motors in month i to be delivered in month j
x(i,j) number of motors produced in month i to be delivered in month j;
free variables
wf workforce
z cost of production
hr human resources;
Equations
cost cost
human_resources human resources
r1 restriction1
r2 restriction2 ;
cost .. z =e= sum((i,j), (cp(i)+(j-i)*ca)*x(i,j)) ;
human_resources .. hr =e= sum(i, sum(j, rh(i)*x(i, j))) ;
*lower than
r1.. sum(j, x(i,j)) =l= cap(i) ;
*greater than
r2.. sum(i, x(i,j)) =g= q(j) ;
Model
motors 'temp' /all/;
Solve motors using mip minimizing mc;
Display mc, x;
This works but check the solution. I added the positive variable x because otherwise you would have negative productions.
The main problem was the fact that you were optimizing a variable that is declared but that you never use in the equations. Also, the variable you are optimizing cannot have to dimensions (I think).
Then, for constraints r1 and r2 you need to add an index because they must be verified for each month, so r1(i) and r2(j). They are actually a "family of constraints".
You cannot subtract the indexes of the months (can't explain why), but you can subtract their order in the set.
And finally, calculate the mc(i,j) as a parameter after you have obtained the solution.
Sets
i months / 1, 2, 3 /
j months / 1, 2, 3 /;
Parameters
cp(i) production cost in month i
/ 1 1.08
2 1.11
3 1.10
/
rh(i) number of necessary workers in month i
/ 1 3
2 4
3 6
/
cap(i) production capacity in month i
/ 1 25
2 20
3 25
/
q(j) number of motors to deliver in month j
/ 1 10
2 15
3 25
/
Scalar ca cost to store motors for a month /0.15/ ;
variables
* mc(i,j) cost of production of motors in month i to be delivered in month j
x(i,j) number of motors produced in month i to be delivered in month j;
positive variable x;
free variables
wf workforce
z cost of production
hr human resources;
Equations
cost cost
human_resources human resources
r1(i) restriction1
r2(j) restriction2 ;
cost .. z =e= sum((i,j), (cp(i)+(ord(j)-ord(i))*ca)*x(i,j)) ;
human_resources .. hr =e= sum(i, sum(j, rh(i)*x(i, j))) ;
*lower than
r1(i).. sum(j, x(i,j)) =l= cap(i) ;
*greater than
r2(j).. sum(i, x(i,j)) =g= q(j) ;
Model
motors 'temp' /all/;
Solve motors using mip minimizing z;
Parameter mc(i,j);
mc(i,j)= (cp(i)+(ord(j)-ord(i))*ca)*x.l(i,j);
Display mc, x.l;
I have been able to get various stats (Jitter, RTT, Packet lost, etc) of WebRTC audio call using RTCPeerConnection.getStats() API.
I need to rate the overall call quality as Excellent, Good, Fair, or Poor.
Is there a formula that uses WebRTC stats to give a overall rating? if not, which of the WebRTC stat(s) that I should give more weightage?
We ended up using MOS (mean opinion score) algorithm to calculate voice call quality metric.
Here is the formula we used -
Take the average latency, add jitter, but double the impact to latency
then add 10 for protocol latencies
EffectiveLatency = ( AverageLatency + Jitter * 2 + 10 )
Implement a basic curve - deduct 4 for the R value at 160ms of latency
(round trip). Anything over that gets a much more agressive deduction
if EffectiveLatency < 160 then
R = 93.2 - (EffectiveLatency / 40)
else
R = 93.2 - (EffectiveLatency - 120) / 10
Now, let's deduct 2.5 R values per percentage of packet loss
R = R - (PacketLoss * 2.5)
Convert the R into an MOS value.(this is a known formula)
MOS = 1 + (0.035) * R + (.000007) * R * (R-60) * (100-R)
We found the formula from https://www.pingman.com/kb/article/how-is-mos-calculated-in-pingplotter-pro-50.html
There is this question that I'm having a bit a difficulty to answer
Here it is:
An n-bit register can hold 2^n distinct bit patterns. As such,
it can only be used to address a memory whose number of addressable units
(typically, bytes) is less than or equal to 2^n. In this question, register
sizes need not be a power of two. K = 2^10
a) What is the minimum size of an address register for a computer
with 5 TB of memory?
b) What is the minimum size of an address register for a computer
with 7 TBs of memory?
c) What is the minimum size of an address register for a computer
with 2.5 PBs of memory?
From the conversion, I know that:
1KB = $2^{10}$ bytes
1MB = $2^{20}$ bytes
1GB = $2^{30}$ bytes
1TB = $2^{40}$ bytes
If I convert 5TB into bytes we get 5,497,558,138,880 bytes
What would be the next step though? I know that 1 byte = 8 bits
This is how I would proceed:
1 TB = 2^40 bytes
Calculate the number of bytes in 5 TB = 5,497,558,138,880 bytes (assume this number is n);
The logarithmic function log(Base2)(n) = the minimum size of an address register and in this case it would be 42.321928095 bits which I would round up to 43 bits.
Same logic for the other questions.
I suggest you divide by 8.
5,497,558,138,880/8 = 687194767360
Using logarithms, 2^n = 687194767360 therefore log2(687194767360) = n
Therefore n = 39.321928095
The same steps can be used to achieve part b and c
I am trying to solve a LP, which is a facility location problem.
The task asks me to deduct 10.000$ iff the optimal model results in having less than 3 Distribution Centers open (y1,y2,y3,y4).
The objective function looks like this: min z = Σ(fiyi) + ΣΣ(cijxij) + ΣΣ(xij*bi) - Σqi*10.000
fi: fixed costs
yi: binary variable; yi = 1 - DC is open; yi = 0 - DC is closed
cij: transportation costs from DC i to customer j
xij: quantity shipped from DC i to customer j
bi: variable warehouse costs at DC i
qi: binary variable; bi = 1 - IT Cost reduction yes; bi = 0 - no IT cost reduction
Now I need to introduce a logical constraint for having the "if..then..." thingy in it. I want to express the following dependence as a constraint in xpress:
if Σyi ≤ 2 ; then Σqi = 1 → IT cost reduction
if Σyi > 2 ; then Σqi = 0 → no IT cost reduction
Any help highly appreciated!
Solved it myself by introducing the following constraints:
3 ≤ Σyi + 2q ≤ 4