I'm using IT Guru's Opnet to simulate different networks. I've run the basic HomeLAN scenario and by default it uses an ethernet connection running at a data rate of 20Kbps. Throughout the scenarios this is changed from 20K to 40K, then to 512K and then to a T1 line running at 1.544Mbps. My question is - does increasing the data rate for the line increase the throughput?
I have this graph output from the program to display my results:
Please note it's the image on the forefront which is of interest
In general, the signaling capacity of a data path is only one factor in the net throughput.
For example, TCP is known to be sensitive to latency. For any particular TCP implementation and path latency, there will be a maximum speed beyond which TCP cannot go regardless of the path's signaling capacity.
Also consider the source and destination of the traffic: changing the network capacity won't change the speed if the source is not sending the data any faster or if the destination cannot receive it any faster.
In the case of network emulators, also be aware that buffer sizes can affect throughput. The size of the network buffer must be at least as large as the signal rate multiplied by the latency (the Bandwidth Delay Product). I am not familiar with the particulars of Opnet, but I have seen other emulators where it is possible to set a buffer size too small to support the select rate and latency.
I have written a couple of articles related to these topics which may be helpful:
This one discusses common network bottlenecks: Common Network Performance Problems
This one discusses emulator configuration issues: Network Emulators
Related
I am currently looking at 1Gb/s download and 35 MB/s upload over coax. We are looking at setting up some VOIP services etc which will be impacted by such a low upload speed. How do I determine what the max bandwidth usage for the day was? I'm aware that netstat, netsh, and network monitor provide information regarding individual processes but I cannot find the data I need to determine whether upgrading to fiber would be marginally beneficial or entirely necessary. Any help would be greatly appreciated.
Netstat, netsh, performance monitor, network monitor
I can obtain the information regarding any connection in particular but i need something more akin to over all statistics so that i can make an informed decision regarding our network limitations ( fiber vs coax)....Do we need an additional 200 mb/s ? etc
Typical VOIP services only require a few kilobytes per second of upload bandwidth per phone call. Do you anticipate having many (hundreds of) concurrent phone calls which would add up to 35MBytes/s (or more likely 35Mbits/sec). As an aside, network bandwidth is typically expressed with big-M and little-b (e.g. Mb) to denote megabits per second.
I would suggest first using a utility like SolarWinds RealTime Bandwidth Monitor to look at your router/gateways utilization.
I would like to test an upload service with hundreds, if not thousands,
of slow HTTPS connections simultaneously.
I would like to have lots of, say, 3G-quality connections,
each throttled with low bandwidth and high latency,
each sending a few megabytes of data up to the server,
resulting in lots of concurrent, long-lived requests being handled by the server.
There are many load generation tools that can generate thousands of simultaneous requests.
(I'm currently using Locust, mostly so that I can take
advantage of my existing client library written in Python.)
Such tools typically run each concurrent request as fast as possible
over the shared network link.
There are various ways to adjust the apparent bandwidth and latency of TCP connections,
such as Linux's TC
and handy wrappers like Comcast.
As far as I can tell, TC and the like control the shared link
but they cannot throttle the individual requests.
If you want to throttle a single request, TC works well.
In theory, with many clients sharing the same throttled network link,
each request could be run serially,
subject to the constrained bandwidth,
rather than having lots of requests executing concurrently,
a few packets at a time.
The former would result in much fewer active requests executing
concurrently on the server.
I suspect that the tool I want has to actively manage each individual client's sending
and receiving to throttle them fairly.
Is there such a tool?
You can take a look at Apache JMeter, it can "throttle" connections to the throughput configurable via the following properties:
httpclient.socket.http.cps=0
httpclient.socket.https.cps=0
The properties can be defined either in user.properties file or passed to JMeter via -J command-line argument
cps stands for character per second so you can "slow down" JMeter threads (virtual users) to the given throughput rate, the formula for cps calculation is:
cps = (target bandwidth in kbps * 1024) / 8
Check out How to Simulate Different Network Speeds in Your JMeter Load Test for more information.
Yes, these are network simulators. A very primitive one is in the form of WanEM. It is not going to cover your testing needs. You will need something akin to Shunra Storm, a hardware device which can manage individual connections and impairment with models derived from Ookla (think speedtest.com) related to 3,4,5g connections from the wild. Well, perhaps I should say, "could manage," as this product has been absent since the HP acquisition of Shunra.
There are some other market competitors on the network front from companies such as Ixia, Agilent, PacketStorm, Spirent and the like. None of them are inexpensive, but I see your need. Slow, and particularly dirty connections likes cell phones, have a disproportionate impact on the stack and can result in the server running out of resources with fewer mobile connections than desktop ones.
On a side note, be sure you are including a representative model for think time in your test code. If you collapse the client-server model with no or extremely limited think time & impair the network only bad things can happen. This will play particular havoc with both predictability and repeatability on your tests. You may also wind up chasing dozens of engineering ghosts related to load in your code that will not occur in production because of the natural delays and the release of resources which should occur during those windows of activity between client requests.
Suppose, there is a network which gives a lot of Timeout errors when packets are transmitted over it. Now, timeouts can happen either because the network itself is inherently lossy (say, poor hardware) or it might be that the network is highly congested, due to which network devices are losing packets in between, leading to Timeouts. Now, what additional statistics about the traffic being transmitted (like Missing Packets errors etc.) are required that might help us to find out whether timeouts are happening due to poor hardware, or too much network load.
Please note that we have access only to one node in the network (from which we are transmitting packets) and as such, we cannot get to know the load being put by other nodes on the network. Similarly, we don't really have any information about the hardware being used in the network. Statistics is all that we have.
A network node only has hardware information about its local collision domain, which on a standard network will be the cable that links the host to the switch.
All the TCP stack will know about lost packets is that it is not receiving acknowledgements so it needs to resend, there is no mechanism for devices (E.g. switches & routers) between a source and destination to tell the source that there is a problem.
Without access to any other nodes the only way to ascertain if your problem is load based would be to run a test that sends consistent traffic over the network for a long period, if the packet retry count per second/minute/hour remains the same then it would suggest that there is a hardware issue, if the losses only occur during peak traffic periods then the issue could be load related. Of course there could be a situation where misconfigured hardware issues will only be apparent during high traffic periods, this takes things back to the main problem which is that you need access to network stats from beyond your single node.
In practice, nearly all loss on terrestrial network paths is due to either congestion or firewalls. Loss due to bit-errors is extremely rare. Even on wireless networks, forward error correction handles most bit/media/transmission errors. Congestion can be caused by a lot of different factors: any given network path will involve dozens of devices and if any one of them becomes overloaded for even a moment, packets will be dropped.
The only way to tell the difference between congestion induced packet loss and media errors is that media errors will occur independent of load. In other words, the loss rate will be the same whether you are sending a lot of data or only a little data.
To test that, you will need some control, or at least knowledge, of the load on the path. Since you don't have control and the only knowledge you have is from source-node observation, the best you can do is to take test samples (using ping is the easiest) around the clock and throughout the week, recording loss rates and latencies. These should give you an idea of when the path is relatively idle. If loss rates remain significant even when the path is (probably) idle, then there might be a media-loss issue. But again, that is extremely rare.
For background, I have written a few articles on the subject:
Loss, Latency, and Speed, discussing what statistics you can observe about a path and what they mean.
Common Network Performance Problems, discussing the most common components in a network path and how they affect performance (congestion).
Imagine you have many clustered servers, across many hosts, in a heterogeneous network environment, such that the connections between servers may have wildly varying latencies and bandwidth. You want to build a map of the connections between servers by transferring data between them.
Of course, this map may become stale over time as the network topology changes - but lets ignore those complexities for now and assume the network is relatively static.
Given the latencies between nodes in this host graph, calculating the bandwidth is a relative simply timing exercise. I'm having more difficulty with the latencies - however. To get round-trip time, it is a simple matter of timing a return-trip ping from the local host to a remote host - both timing events (start, stop) occur on the local host.
What if I want one-way times under the assumption that the latency is not equal in both directions? Assuming that the clocks on the various hosts are not precisely synchronized (at least that their error is of the the same magnitude as the latencies involved) - how can I calculate the one-way latency?
In a related question - is this asymmetric latency (where a link is quicker in direction than the other) common in practice? For what reasons/hardware configurations? Certainly I'm aware of asymmetric bandwidth scenarios, especially on last-mile consumer links such as DSL and Cable, but I'm not so sure about latency.
Added: After considering the comment below, the second portion of the question is probably better off on serverfault.
To the best of my knowledge, asymmetric latencies -- especially "last mile" asymmetries -- cannot be automatically determined, because any network time synchronization protocol is equally affected by the same asymmetry, so you don't have a point of reference from which to evaluate the asymmetry.
If each endpoint had, for example, its own GPS clock, then you'd have a reference point to work from.
In Fast Measurement of LogP Parameters
for Message Passing Platforms, the authors note that latency measurement requires clock synchronization external to the system being measured. (Boldface emphasis mine, italics in original text.)
Asymmetric latency can only be measured by sending a message with a timestamp ts, and letting the receiver derive the latency from tr - ts, where tr is the receive time. This requires clock synchronization between sender and receiver. Without external clock synchronization (like using GPS receivers or specialized software like the network time protocol, NTP), clocks can only be synchronized up to a granularity of the roundtrip time between two hosts [10], which is useless for measuring network latency.
No network-based algorithm (such as NTP) will eliminate last-mile link issues, though, since every input to the algorithm will itself be uniformly subject to the performance characteristics of the last-mile link and is therefore not "external" in the sense given above. (I'm confident it's possible to construct a proof, but I don't have time to construct one right now.)
There is a project called One-Way Ping (OWAMP) specifically to solve this issue. Activity can be seen in the LKML for adding high resolution timestamps to incoming packets (SO_TIMESTAMP, SO_TIMESTAMPNS, etc) to assist in the calculation of this statistic.
http://www.internet2.edu/performance/owamp/
There's even a Java version:
http://www.av.it.pt/jowamp/
Note that packet timestamping really needs hardware support and many present generation NICs only offer millisecond resolution which may be out-of-sync with the host clock. There are MSDN articles in the DDK about synchronizing host & NIC clocks demonstrating potential problems. Timestamps in nanoseconds from the TSC is problematic due to core differences and may require Nehalem architecture to properly work at required resolutions.
http://msdn.microsoft.com/en-us/library/ff552492(v=VS.85).aspx
You can measure asymmetric latency on link by sending different sized packets to a port that returns a fixed size packet, like send some udp packets to a port that replies with an icmp error message. The icmp error message is always the same size, but you can adjust the size of the udp packet you're sending.
see http://www.cs.columbia.edu/techreports/cucs-009-99.pdf
In absence of a synchronized clock, the asymmetry cannot be measured as proven in the 2011 paper "Fundamental limits on synchronizing clocks over networks".
https://www.researchgate.net/publication/224183858_Fundamental_Limits_on_Synchronizing_Clocks_Over_Networks
The sping tool is a new development in this space, which uses clock synchronization against nearby NTP servers, or an even more accurate source in the form of a GNSS box, to estimate asymmetric latencies.
The approach is covered in more detail in this blog post.
I've been checking out using a system called ROS (http://www.ros.org) for some work.
There are lots of different types of data that get sent between network nodes in ROS.
You define a struct of data that you want to send in a message, and ROS will handle opening a specific port between the two nodes that will only send that struct of data.
So if there are 5 different messages, there will be 5 different ports.
As opposed to this scenario, I have seen other platforms that just push all the different messages across one port. This means that there needs to be a sort of multiplexing/demultiplexing (done by some sort of message parsing on the receivers end).
What I wonder is... which is better from a performance perspective?
Do operating systems switch based on ports quickly, so that a system like ROS doesn't have to do too much work to work out what is in the message and interpreting it?
OR
Is opening lots of ports going to mean lots of slower kernel calls, and the cost of having to work out and translate message types end up being more then the time spent switching between ports?
When this scales to a large amount of data at high rates and lots of different messages types there will be lots of ports. So I imagine that when scaling each of these topologies that performance will be a big factor in selecting the way to work.
I should also point out that these nodes usually exist on one small network, or most of the time on the one machine in which networking is used as a force of inter-process communication. So the transmission time is only a very small factor in the overall system timing.
ROS being an architecture for robots may have one node for every sensor and actuator, so depending on the complexity of your system we may be talking about 20-30 nodes pushing small-ish (100bytes or so) data between 10-100Hz
It depends. I do not know the specifics of ROS but in networking it comes down to the following constraints:
Distance: speed of light is fast but over a distance it starts making a difference
Protocol Overhead: connection oriented vs. connection-less
On the OS side, maintaining a list of free ports isn't such much of an overhead - of course there is a cost to it but everything is relative: if you are talking about a distributed system with long distance links, then it is easy to argue that cycling through OS network ports ranks as lower concern compared to managing communication quality.
Without a more specific question, I'll stop here.
I don't have any data on this, but it seems plausible that multiple ports might be handled more efficiently by multi-core systems, as opposed to demultiplexing within the program.