I am using sumo for traffic signal control, and want to optimize the phase to reduce some objectives. During the process, I use the traci module as an output of states in traffic junction. The confusing part is traci.lane.getWaitingTime.
I don't know how the waiting time is calculated and also after I use two detectors as an output to observe, I think it is too large.
Can someone explain how the waiting time is calculated in SUMO?
The waiting time essentially counts the number of seconds a vehicle has a speed of less than 0.1 m/s. In the case of traci.lane this means it is the number of (nearly) standing vehicles multiplied with the time step length (since traci.lane returns the values for the last step).
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Using INFO CPU command on Redis, I get the following values back (among other values):
used_cpu_sys:688.80
used_cpu_user:622.75
Based on my understanding, the value indicates the CPU time (expressed in seconds) accumulated since the launch of the Redis instance, as reported by the getrusage() call (source).
What I need to do is calculate the % CPU utilization based on these values. I looked extensively for an approach to do so but unfortunately couldn't find a way.
So my questions are:
Can we actually calculate the % CPU utilization based on these 2 values? If the answer is yes, then I would appreciate some pointers in that direction.
Do we need some extra data points for this calculation? If the answer is yes, I would appreciate if someone can tell me what those data points would be.
P.S. If this question should belong to Server Fault, please let me know and I will post it there (I wasn't 100% sure if it belongs here or there).
You need to read the value twice, calculate the delta, and divide by the time elapsed between the two reads. That should give you the cpu usage in % for that duration.
I have looked into FlowMon which counts packets over the entire simulation interval. Can I specify shorter intervals and many such intervals? Are there other options to measure instantaneous bandwidth (or at least, bandwidth averaged over short intervals like 1ms) in ns-3?
You could still use FlowMon for this. FlowMon accumulates the number of packets as you progress through the simulation so at different time intervals you can get values such as txBytes and do some calculations with that.
Take a look at NS3 scheduling, and try using that
Simulator::Schedule(Seconds(1), &StatsCalculationCallback);
Where the StatsCalculationsCallback is the function where you would calculate the difference between the value now and value before to get for that short interval. Then in the StatsCalculationsCallback function you would then also need to reschedule again for the next interval.
I am trying to find a way to determine when a value is stabilized in excel. In this case, temperature. I have a set of time-series data, TIMESTAMP and TEMPERATURE. at T=0, the temperature begins to rise at a rapid rate. When the setpoint temperature is approached, the rate of rise decreases, until finally, it stabilizes around the setpoint. The temperature tends to overshoot and undershoot the setpoint by several degrees before stabilization.
How can I have excel figure out when the temperature is "Stable" around the setpoint? For example, Delta T (SP-PV) <= 2. (Delta T is difference between Set Point SP and Process Variable PV)
I was thinking possibly using a time variable to determine a data set size (i.e. 5 minutes) and see if the average Delta T within that time is <= 2.
I do not know how to get excel to search through the whole series effectively though.
If you only look at the difference between the setpoint and the value at some time 0, you may mistake a crossing as a "stabilized" point. You need to examine both the estimated rate of change (Tn - Tn-1)/dt and the current value.
Stability is going to be a combination of "how fast is my signal still changing" and "how close am I". It isn't uncommon in real systems for there to be a fixed offset (steady state error) between a setpoint and a measurement with no change happening in the signal over time. This is typically corrected by an integrative term in the control system.
today I created a small batch of 20 categorization HITs with the name Grammatical or Ungrammatical using the web UI. Can you tell me the easiest way to manage this batch so that I can reduce its time allotted to 15 minutes from 1 hour and remove also remove the categorization of masters. This is a very simple task that's set to auto-approve within 1 hour, and I am fine with that. I just need to make it more lucrative for people to attempt this at the penny rate.
You need to register a new HITType with the relevant properties (reduced time and no masters qualification) and then perform a ChangeHITTypeOfHIT operation on all of the HITs in the batch.
API documentation here: http://docs.aws.amazon.com/AWSMechTurk/latest/AWSMturkAPI/ApiReference_ChangeHITTypeOfHITOperation.html
I am working on the power management of a device using Q-learning algorithm. The device has two power modes, i.e., idle and sleep. When the device is asleep, the requests for processing are buffered in a queue. The Q-learning algorithm looks for minimizing a cost function which is a weighted sum of the immediate power consumption and the latency caused by an action.
c(s,a)=lambda*p_avg+(1-lambda)*avg_latency
In each state, the learning algorithm takes an action (executing time-out values) and evaluates the effect of the taken action in next state (using above formula). The actions are taken by executing certain time-out values from a pool of pre-defined time-out values. The parameter lambda in above equation is a power-performance parameter (0_<lambda<1). It defines whether the algorithm should look for power saving (lambda-->1) or should look for minimizing latency (lambda-->0). The latency for each request is calculated as queuing-time + execution-time.
The problem is that the learning algorithm always favors small time-out values in sleep state. It is because the average latency for small time-out values is always lower, and hence their cost is also small. When I change the value of lambda from lower to higher, I don't see any effect in the final output policy. The policy always selects small time-out values as best actions in each state. Instead of average power and average latency for each state, I have tried using overall average power consumption and overall average latency for calculating cost for a state-action pair, but it doesn't help. I also tried using total energy consumption and total latency experinced by all the request for calculating cost in each state-action pair, but it doesn't help either. My question is: what could be a better cost function for this scenario? I update the Q-value as follows:
Q(s,a)=Q(s,a)+alpha*[c(s,a)+gamma*min_a Q(s',a')-Q(s,a)]
Where alpha is a learning rate (decreased slowly) and gamma=0.9 is a discount factor.
To answer the questions posed in the comments:
shall I use the entire power consumption and entire latency for all
the requests to calculate the cost in each state (s,a)?
No. In Q-learning, reward is generally considered an instantaneous signal associated with a single state-action pair. Take a look at Sutton and Barto's page on rewards. As shown the instantaneous reward function (r_t+1) is subscripted by time step - indicating that it is indeed instantaneous. Note that R_t, that expected return, considers the history of rewards (from time t back to t_0). Thus, there is no need for you to explicitly keep track of accumulated latency and power consumption (and doing so is likely to be counter-productive.)
or shall I use the immediate power consumption and average latency
caused by an action a in state s?
Yes. To underscore the statement above, see the definition of an MDP on page 4 here. The relevant bit:
The reward function specifies expected instantaneous reward as a
function of the current state and action
As I indicated in a comment above, problems in which reward is being "lost" or "washed out" might be better solved with a Q(lambda) implementation because temporal credit assignment is performed more effectively. Take a look at Sutton and Barto's chapter on TD(lambda) methods here. You can also find some good examples and implementations here.