I am trying to monitor the uptime and send data at high frequency on node.js server. The server will broadcast network data every few milliseconds.
But using Date.now() is not accurate enough. So I am thinking of using the high resolution timer process.hrtime(). I don't know what is the max value of process.hrtime. I need to run the server for at least 6 months. Will it overflow very soon?
Primary use of process.hrtime() is is for measuring performance between intervals (docs) but it can be also used for measuring uptime with nanosecond precision.
It returns time in array [seconds, nanoseconds], where nanoseconds are the remaining part of time not representable by whole second.
Seconds will reach max safe integer (9007199254740991) in thousands of years. Nanosecond will never reach it as the maximum nanoseconds not representable by whole second is 999999999.
Related
I have a service and its bandwidth graph looks like this
What does it represent.? I am using tutum which shows me these graphs.!
Should I worry about it.? Please Explain! Any help is appreciated.!
Bandwidth is the the amount of data sent (Out) or received (In) in a period of time. Mbps stands for Mega bits per seconds, i.e., how many bits did you send or receive during that past whole second.
I am sure you heard about xxx Mpbs from your internet provider, in which case, it correspond to the maximum speed you can have, but you are not required to use the whole bandwidth all the time.
Same thing on Tutum, depending on your hosting provider / instance type you will also have a maximum Mbps bandwidth, but at any given t time, you are using YY Mbps out of your XX Mpbs maximum.
As the graph increase, it simply means that you send/receive more data, which can mean that you have a higher traffic or you are doing some kind of networking activity.
When running a benchmark e.g. dhrystone with the command:
make output/dhrystone.riscv.out
as described at: http://riscv.org/download.html#tab_rocket,
on the C++ emulator. I get the following output:
When running it for the first time:
Microseconds for one run through Dhrystone: 1064
Dhrystones per Second: 939
cycle = 533718
instret = 148672
and the second time:
Microseconds for one run through Dhrystone: 1064
Dhrystones per Second: 939
cycle = 533715
instret = 148672
Why do the cycles differ? Shouldn't they be exactly the same. I have tried this with other benchmarks too and had even higher deviations. If this is normal where do the deviations come from?
There are small amounts of nondeterminism from randomly initialized registers (e.g., the clock that is recovered by the HTIF is initialized to a random phase). It doesn't seem like these minor deviations would impact any performance benchmarking.
If you need identical results each time (e.g., for verification?), you could modify the emulator code to initialize registers to some known value each time.
I need to tune PI(D) gains in a system which has a quite large delay. It's a common temperature controller, but the temperature probe is far away from the heater. Some further info:
the response of the probe is delayed about 10 seconds from any change on the heater
the temperature is sampled # 1 Hz, with a resolution of 0.01 °C
the heater is controller in PWM with a period of 1 Hz, with a 10-bit PWM
the goal is to maintain the oscillation below ±0.05 °C
Currently I'm using the controller as PI. I can't avoid oscillations. The higher the gain, the smaller and faster the oscillations. Still too high (about ±0.15 °C).
Reducing the P and I gains leads to very long and deep oscillations.
I think this is due to the delay.
The settling time is not a problem, it may take all the time it needs.
I'm puzzling over how get the system to work. Let's think to use only I. When the probe reaches the target value and the I output starts to decrease, the temperature will rise for some other time. I cannot use the derivative term because the variations are too slow and the dError is very close to zero (if I set the dGain to a huge value there is too much noise).
Any idea?
Try P-only. How fast are the proportional-only oscillations? If you can't tune Kp small enough to get no oscillations, then your heater is overpowered for your system.
If the dead time of the of the system is on the order of 10s, the time constant (T_i) for the Integral term should be 3.3 times the dead time, using a Ziegler Nichols open-loop PI rule ( https://controls.engin.umich.edu/wiki/index.php/PIDTuningClassical#Ziegler-Nichols_Open-Loop_Tuning_Method_or_Process_Reaction_Method: ) , and then Integral term should be Ki = Kp/T_i. So with deadtime = 10s, then Ki should be Kp/33 or slower.
If you are getting integral-only oscillations, then the integral is winding up and down quicker than the process responds, and it should be even smaller.
Also -- think of the units of the different terms. It might not be the delay causing your problems so much as the resolution of the measurement and control systems. If you're driving a (for example) 100W heater with a 1/1024 resolution PWM, you've got 0.1W resolution per PWM count that you are trying to adjust based on 0.01C temperature differences. At less than Kp = 100 PWMcount/degree (or 10W/degree) you don't have enough resolution in the PWM to make changes in response to a 0.01C error. At a Kp=10PWM/C you might need a 0.10C change to result in an actual change in the PWM power. Can you use a higher resolution PWM?
Thinking of it the other way, if you want to operate a system over a range of 30C at 0.01C, I'd think you would want at least a 15bit PWM to have 10 times the resolution in the controlled system. With only 10 bits of PWM you only get about 1C of total range with control at 10x the resolution of the measurements.
Normally for large delays you have two options: Lower the gains of the system or, if you have a model of the plant you are controlling, use a Smith Predictior.
I would start by modelling your system (using open-loop steps in the input) to quantify the delay and the time constant of your plant, then check if the sampling of the temperature and the PWM rate are OK.
Notice that if your PWM frequency is too small in comparison to the plant dynamics, you will have sustained oscillations because of the slow PWM. You can check it using just an constant input to your PWM (with no controllers, open loop).
EDIT: Didn't see that the problem was already solved, but I'll leave this here for reference.
I am doing some performance profiling for part of my program. And I try to measure the execution with the following four methods. Interestingly they show different results and I don't fully understand their differences. My CPU is Intel(R) Core(TM) i7-4770. System is Ubuntu 14.04. Thanks in advance for any explanation.
Method 1:
Use the gettimeofday() function, result is in seconds
Method 2:
Use the rdtsc instruction similar to https://stackoverflow.com/a/14019158/3721062
Method 3 and 4 exploits Intel's Performance Counter Monitor (PCM) API
Method 3:
Use PCM's
uint64 getCycles(const CounterStateType & before, const CounterStateType &after)
Its description (I don't quite understand):
Computes the number core clock cycles when signal on a specific core is running (not halted)
Returns number of used cycles (halted cyles are not counted). The counter does not advance in the following conditions:
an ACPI C-state is other than C0 for normal operation
HLT
STPCLK+ pin is asserted
being throttled by TM1
during the frequency switching phase of a performance state transition
The performance counter for this event counts across performance state transitions using different core clock frequencies
Method 4:
Use PCM's
uint64 getInvariantTSC (const CounterStateType & before, const CounterStateType & after)
Its description:
Computes number of invariant time stamp counter ticks.
This counter counts irrespectively of C-, P- or T-states
Two samples runs generate result as follows:
(Method 1 is in seconds. Methods 2~4 are divided by a (same) number to show a per-item cost).
0.016489 0.533603 0.588103 4.15136
0.020374 0.659265 0.730308 5.15672
Some observations:
The ratio of Method 1 over Method 2 is very consistent, while the others are not. i.e., 0.016489/0.533603 = 0.020374/0.659265. Assuming gettimeofday() is sufficiently accurate, the rdtsc method exhibits the "invariant" property. (Yep I read from Internet that current generation of Intel CPU has this feature for rdtsc.)
Methods 3 reports higher than Method 2. I guess its somehow different from the TSC. But what is it?
Methods 4 is the most confusing one. It reports an order of magnitude larger number than Methods 2 and 3. Shouldn't it be also kind of cycle counts? Let alone it carries the "Invariant" name.
gettimeofday() is not designed for measuring time intervals. Don't use it for that purpose.
If you need wall time intervals, use the POSIX monotonic clock. If you need CPU time spent by a particular process or thread, use the POSIX process time or thread time clocks. See man clock_gettime.
PCM API is great for fine tuned performance measurement when you know exactly what you are doing. Which is generally obtaining a variety of separate memory, core, cache, low-power, ... performance figures. Don't start messing with it if you are not sure what exact services you need from it that you can't get from clock_gettime.
Everyone knows that MRTG needs at least one value to be passed on it's input.
In per-target options MRTG has 'gauge', 'absolute' and default (with no options) behavior of 'what to do with incoming data'. Or, how to count it.
Lets look at the elementary, yet popular example :
We pass cumulative data from network interface statistics of 'how much packets were recieved by the interface'.
We take it from '/proc/net/dev' or look at 'ifconfig' output for certain network interface. The number of recieved bytes is increasing every time. Its cumulative.
So as i can imagine there could be two types of possible statistics:
1. How fast this value changes upon the time interval. In oher words - activity.
2. Simple, as-is growing graphic that just draw every new value per every minute (or any other time interwal)
First graphic will be saltatory (activity). Second will just grow up every time.
I read twice rrdtool's and MRTG's docs and can't understand which option mentioned above counts what.
I suppose (i am not sure) that 'gauge' draw values as is, without any differentiation calculations (good for measuring how much memory or cpu is used every 5 minutes). And default or 'absolute' behavior tryes to calculate the speed between nearby measures, but what's the differencr between last two?
Can you, guys, explain in a simple manner which behavior stands after which option of three options possible?
Thanks in advance.
MRTG assumes that everything is being measured as a rate (even if it isnt a rate)
Type 'gauge' assumes that you have already calculated the rate; thus, the provided value is stored as-is (after Data Normalisation). This is appropriate for things like CPU usage.
Type 'absolute' assumes the value passed is the count since the last update. Thus, the value is divided by the number of seconds since the last update to get a rate in thingies per second. This is rarely used, and only for certain unusual data sources that reset their value on being read - eg, a script that counts the number of lines in a log file, then truncates the log file.
Type 'counter' (the default) assumes the value passed is a constantly growing count, possibly that wraps around at 16 or 64 bits. The difference between the value and its previous value is divided by the number of seconds since the last update to get a rate in thingies per second. If it sees the value decrease, it will assume a counter wraparound at 16 or 64 bit. This is appropriate for something like network traffic counters, which is why it is the default behaviour (MRTG was originally written for network traffic graphs)
Type 'derive' is like 'counter', but will allow the counter to decrease (resulting in a negative rate). This is not possible directly in MRTG but you can manually create the necessary RRD if you want.
All types subsequently perform Data Normalisation to adjust the timestamp to a multiple of the Interval. This will be more noticeable for Gauge types where the value is small than for counter types where the value is large.
For information on this, see Alex van der Bogaerdt's excellent tutorial