I am trying to simulate different network speeds using selenium
Maybe I'm missing the point of the question but:
"Bandwidth and throughput have to do with speed, but what's the difference? To be brief, bandwidth is the theoretical speed of data on the network, whereas throughput is the actual speed of data on the network."
Pretty much: bandwidth is what your ISP will market to you, but your throughput is what you'll actually get on your side, in terms of speed. Throughput will almost always be lower than the marketed/advertised bandwidth.
source:
https://study.com/academy/lesson/bandwidth-vs-throughput.html#:~:text=Lesson%20summary,fast%20data%20is%20being%20sent.&text=Bandwidth%20refers%20to%20the%20theoretical,data%20on%20your%20network%20travels.
Possibly the term upload speed in a broader aspect indicates the internet speed where for uploading and downloading you need speed. Bandwidth and Throughput are the two major indicators of speed where:
Bandwidth is the theoretical speed of data on the network.
Throughput is the actual speed of data on the network.
Bandwidth
In true essence, Bandwidth refers to the maximum amount of data you can get from point A to point B in a specific amount of time. Thesedays while dealing with computers Bandwidth refers to, how many bits of information we can theoretically transmit in specific amount of time, as an example bits per second. E.g. Kbps (kilobits per second) and Mbps (megabits per second).
Throughput
Throughput can only send as much as the bandwidth will allow and is actually less than that as factors like latency (delays), jitter (irregularities in the signal), and error rate (actual mistakes during transmission) reduces the overall throughput.
I think what you are looking for is the method to do it.
Sets Chromium network emulation settings.
driver.set_network_conditions(
offline=False,
latency=5, # additional latency (ms)
download_throughput=500 * 1024, # maximal throughput
upload_throughput=500 * 1024) # maximal throughput
Note: 'throughput' can be used to set both (for download and upload).
Source
Related
I have conducted a performance testing on e-commerce website and trying to find some bottlenecks. From Azure application insight>performance i checked the process I/O rate.
As you can see from the picture the process I/O rate was 33.57 during the performance test duration. But i am not sure if that's a good or not. Can please advise me on what is a good I?o rate for e-commerce application? Thanks
Over millions of recorded servers in the Live Optics program the Read Ratio is 69% and the average IO Transfer size is 34.4K. Just for simplicity sake, let's round to 32K. Most environments will not have a single IO transfer size.
When reading a paper (not free) comparing Kafka and RabbitMQ, I came across the following (emphasis mine):
Latency. In any transport architecture, latency of a packet/message is
determined by the serial pipeline (i.e., sequence of processing steps)
that it passes through. Latency can only be reduced by pipelining the packet transport over resources that can work concurrently on the same packet in a series architecture (multiple processing cores, master DMA engines in case of disk or network access,…) . It is not infuenced by scaling out resources in
parallel.
Throughput. Throughput of a transport architecture is the number of
packets (or alternatively,bytes) per time unit that can be transported
between producers and consumers. Contrary to latency,throughput can
easily be enhanced by adding additional resources in parallel.
For a simple pipeline throughput and latency are inversely
proportional.
Why is it so? Isn't that the contrary of saying that "(latency) is not influenced by scaling out resources in parallel"? If I add more machines to increase the throughput, how is the latency reduced?
Let's examine the scenario of a highway, and for purposes of discussion we'll use I-66 in the Washington, DC metro. This highway experiences rush hour delays each morning amounting to about 40-60 minutes of additional travel time. This is because the throughput of the road is constrained. As a result, latency for a single car increases.
The general theory behind this is known as Little's Law. It states that the average amount of time a customer (or in this case, a driver) spends in a system (i.e. the highway) is equal to the arrival rate divided by total number of customers in the system. Expressed algebraically,
The practical implications of this are that, given an increase in the number of cars L, such as what happens around rush hour, and given constant throughput of the highway Lambda (Virginia got a little creative and figured out how to dynamically convert a shoulder into a traffic lane, but it wasn't very effective), what results is an increase in the time it takes to travel a defined distance W. The inverse of W is the speed of a car.
It is clear that, by Little's Law, throughput lambda is inversely proportional to latency (time) W for a constant number of cars L.
I'm trying to accelerate this database search application with CUDA, and I'm working on running a core algorithm in parallel with CUDA.
In one test, I run the algorithm in parallel across a digital sequence of size 5000 with 500 blocks per grid and 100 threads per block and came back with a runt time of roughly 500 ms.
Then I increased the size of the digital sequence to 8192 with 128 blocks per grid and 64 threads per block and somehow came back with a result of 350 ms to run the algorithm.
This would indicate that how many blocks and threads used and how they're related does impact performance.
My question is how to decide the number of blocks/grid and threads/block?
Below I have my GPU specs from a standard device query program:
You should test it because it depends on your particular kernel. One thing you must aim to do is to make the number of threads per block a multiple of the number of threads in a warp. After that you can aim for high occupancy of each SM but that is not always synonymous with higher performance. It was been shown that sometimes lower occupancy can give better performance. Memory bound kernels usually benefit more from higher occupancy to hide memory latency. Compute bound kernels not so much. Testing the various configurations is your best bet.
Typically the CPU runs for a while without stopping, then a system call is made to read from a file or write to a file. When the system call completes, the CPU computes again until it needs more data or has to write more data, and so on.
Some processes spend most of their time computing, while others spend most of their time waiting for I/O. The former are called compute-bound; the latter are called I/O-bound. Compute-bound processes typically have long CPU bursts and thus infrequent I/O waits, whereas I/O-bound processes have short CPU bursts and thus frequent I/O waits.
As CPU gets faster, processes tend to
get more I/O-bound.
Why and how?
Edited:
It's not a homework question. I was studying the book (Modern Operating Systems by Tanenbaum) and found this matter there. I didn't get the concept that's why I am asking here. Don't tag this question as a homework please.
With a faster CPU, the amount of time spent using the CPU will decrease (given the same code), but the amount of time spent doing I/O will stay the same (given the same I/O performance), so the percentage of time spent on I/O will increase, and I/O will become the bottleneck.
That does not mean that "I/O bound processes are faster".
As CPU gets faster, processes tend to get more I/O-bound.
What it's trying to say is:
As CPU gets faster, processes tend to not increase in speed in proportion to CPU speed because they get more I/O-bound.
Which means that I/O bound processes are slower than non-I/O bound processes, not faster.
Why is this the case? Well, when only CPU speed increases all the rest of your system haven't increased in speed. Your hard disk is still the same speed, your network card is still the same speed, even your RAM is still the same speed*. So as the CPU increases in speed, the limiting factor to your program becomes less and less the CPU speed but more about how slow your I/O is. In other words, programs naturally shift to being more and more I/O bound. In other words: ..as CPU gets faster, processes tend to get more I/O-bound.
*note: Historically everything else also improved in speed along with the CPU, just not as much. For example CPUs went from 4MHz to 2GHz, a 500x speed increase whereas hard disk speed went from around 1MB/s to 70MB/s, a lame 70x increase.
Short radio link with a data source attached with a needed throughput of 1280 Kbps over IPv6 with a UDP Stop-and-wait protocol, no other clients or noticeable noise sources in the area. How on earth can I calculate what the best packet size is to minimise overhead?
UPDATE
I thought it would be an idea to show my working so far:
IPv6 has a 40 byte header, so including ACK responses, that's 80 bytes overhead per packet.
To meet the throughput requirement, 1280 K/p packets need to be sent a second, where p is the packet payload size.
So by my reckoning that means that the total overhead is (1280 K/p)*(80), and throwing that into Wolfram gives a function with no minima, so no 'optimal' value.
I did a lot more math trying to shoehorn bit error rate calculations into there but came up against the same thing; if there's no minima, how do I choose the optimal value?
Your best bet is to use a simulation framework for networks. This is a hard problem, and doesn't have an easy answer.
NS2 or SimPy can help you devise a discrete event simulation to find optimal conditions, if you know your model in terms of packet loss.
Always work with the largest packet size available on the network, then in deployment configure the network MTU for the most reliable setting.
Consider latency requirements, how is the payload being generated, do you need to wait for sufficient data before sending a packet or can you immediately send?
The radio channel is already optimized for noise as the low packet level, you will usually have other demands of the implementation such as power requirements: sending in heavy batches or light continuous load.