I'm writing an app using sms as communication.
I have chosen to subscribe to an sms-gateway, which provides me with an API for doing so.
The API has functions for sending as well as pulling new messages. It does however not have any kind of push functionality.
In order to do my queries most efficient, I'm seeking data on how long time people wait before they answer a text message - as a probability function.
Extra info:
The application is interactive (as can be), so I suppose the times will be pretty similar to real life human-human communication.
I don't believe differences in personal style will play a big impact on the right times and frequencies to query, so average data should be fine.
Update
I'm impressed and honered by the many great answers recieved. I have concluded that my best shot will be a few adaptable heuristics, including exponential (or maybe polynomial) backoff.
All along I will be gathering statistics for later analysis. Maybe something will show up. I think I will cheat start on the algorithm for generating poll-frquenzies from a probability distribution. That'll be fun.
Thanks again many times.
In the absence of any real data, the best solution may be to write the code so that the application adjusts the wait time based on current history of response times.
Basic Idea as follows:
Step 1: Set initial frequency of pulling once every x seconds.
Step 2: Pull messages at the above frequency for y duration.
Step 3: If you discover that messages are always waiting for you to pull decrease x otherwise increase x.
Several design considerations:
Adjust forever or stop after sometime
You can repeat steps 2 and 3 forever in which case the application dynamically adjusts itself according to sms patterns. Alternatively, you can stop after some time to reduce application overhead.
Adjustment criteria: Per customer or across all customers
You can chose to do the adjustment in step 3 on a per customer basis or across all customers.
I believe GMAIL's smtp service works along the same lines.
well I would suggest finding some statistics on daily SMS/Text Messaging usage by geographical location and age groups and come up with an daily average, it wont be an exact measurement for all though.
Good question.
Consider that people might have multiple tasks and that answering a text message might be one of those tasks. If each of those tasks takes an amount of time that is exponentially distributed, the time to get around to answering the text message is the sum of those task completion times. The sum of n iid random variables has a Gamma distribution.
The number of tasks ahead of the text return also has a dicrete distribution - let's say it's Poisson. I don't have the time to derive the resulting distribution, but simulating it using #Risk, I get either a Weibull or Gamma distribution.
SMS is a store-and-forward messaging service, so you have to add in the delay that can be added by the various SMSCs (Short Message Service Centers) along the way. If you are connecting to one of the big aggregation houses (Sybase, TNS, mBlox etc) commercial bulk SMS providers (Clickatel, etc) then you need to allow for the message to transverse their network as well as the carriers network. If you are using a smaller shop then most likely they are using a GSM Modem (or modems) and there is a throughput limit on the message the can receive and process (as well as push out)
All that said, if you are using a direct connection or one of the big guys MO (mobile originated) messages coming to you as a CP (content provider) should take less than 5 seconds. Add to that the time it takes the Mobile Subscribers to reply.
I would say that anecdotal evidence form services I've worked on before, where the Mobile Subscriber needs to provide a simple reply it's usually within 10 seconds or not at all.
If you are polling for specific replies I would poll at 5 and 10 seconds then apply an exponential back off.
All of this is from a North American point-of-view. Europe will be fairly close, but places like Africa, Asia will be a bit slower as the networks are a bit slower. (unless you are connected directly to the operator and even then some of them are slow).
Related
I have a piece of code that includes a specific feature that I can turn on and off. I want to know the execution time of the feature.
I need to measure this externally, i.e. by simply measuring execution time with a load test tool. Assume that I cannot track the feature's execution time internally.
Now, I execute two runs (on/off) and simply assume that the difference between the resulting execution time is my feature's execution time.
I know that it is not entirely correct to do this as I'm looking at two separate runs that may be influenced by networking, programmatic overhead, or the gravitational pull of the moon. Still, I hope I can assume that the result will still be viable if I have a sufficiently large number of requests.
Now for the real question. I do the above using the average response time. Which is not perfect, but more or less ok.
My question is, what if I now use a percentile (say, 95th) instead?
Would my imperfect subtract-A-from-B approach become significantly more imperfect when using percentiles?
I would stick to the percentiles as the "average" approach can mask the problem, for example if you have very low response times during the initial phase of the test when the load is low and very high response times during the main phase of test when the load is immense the arithmetic mean approach will give you okayish values while with the percentiles you will get the information that the response time for 95% of requests was X or higher.
More information: Understanding Your Reports: Part 3 - Key Statistics Performance Testers Need to Understand
We are working on a Algorithmic trading software in C#. We monitor Market Price and then based on certain conditions, we want to buy the stock.
User input can be taken from GUI (WPF) and send to back-end for monitoring.
Back - end receives data continuously from Stock Exchange and checks if user entered price is met with certain limits and conditions. If all are satisfied, then we will buy / sell the stock (in Futures FUT).
Now, I want to design my Back end service.
I need Task Parallel Library or Custom Thread Pool where I want to create my tasks / threads / pool when application starts (may be incremental or fixed say 5000).
All will be in waiting state.
Once user creates an algorithm, we will activate one thread from the pool and monitors price for each incoming string. If it matches, then buy / sell and then go into waiting state again. (I don't want to create and destroy the threads / tasks as it is time consuming).
So please can you guys help me in this regard? If the above approach is good or do we have any other approach?
I am struck with this idea and not able to go out of box to think on this.
The above approach is definitely not "good"
Given the idea above, the architecture is wrong in many cardinal aspects. If your Project aspires to survive in 2017+ markets, try to learn from mistakes already taken in 2007-2016 years.
The percentages demonstrate the NBBO flutter for all U.S. Stocks from 2007-01 ~ 2012-01. ( Lower values means better NBBO stability. Higher values: Instability ) ( courtesy NANEX )
Financial Markets operate on nanosecond scales
Yes, a few inches of glass-fibre signal propagation transport delay decide on PROFIT or LOSS.
If planning to trading in Stock Markets, your system will observe the HFT crowd, doing dirty practice of Quote Stuffing and Vacuum-Cleaning 'em right in front of your nose at such scales, that your single-machine multi-threaded execution will just move through thin-air of fall in gap already created many microseconds before your decision took place on your localhost CPU.
The rise of HFT from 2007-01 ~ 2012-01 ( courtesy NANEX ).
May read more about an illusion of liquidity here.
See the expansion of Quotes against the level of Trades:
( courtesy NANEX )
Even if one decides to trade in a single instrument, on FX, the times are prohibitively short ( more than 20% of the ToB Bids are changed in time less than 2 ms and do not arrive to your localhost before your trading algorithm may react accordingly ).
If your TAMARA-measurements are similar to this, at your localhost, simply forget to trade in any HF/MF/LF-HFT instruments -- you simply do not see the real market ( the tip of the iceberg ) -- as the +20% price-events happen in the very first column ( 1 .. 2 ms ), where you do not see any single event at all!
5000 threads is bad, don't do that ever, you'll degrade the performance with context switch loss much more than parallel execution timing improvement. Traditionally the number of threads for your application should be equal to the number of cores in your system, by default. There are other possible variants, but probably they aren't the best option for your.
So you can use a ThreadPool with some working item method there with infinite loop, which is very low level, but you have control on what is going on in your system. Callback function could update the UI so the user will be notified about the trading results.
However, if you are saying that you can use the TPL, I suggest to consider these two options for your case:
Use a collection of tasks running forever for checking the new trading request. You still should tune up the number of simultaneously running tasks because you probably don't want them to fight each other for a CPU time. As the LongRunning tasks are created with dedicated background thread, many of them will degrade your application performance as well. Maybe in this approach you should introduce a strategy pattern implementation for a algorithm being run inside the task.
Setup a TPL Dataflow process within your application. For such approach your should encapsulate the info about the algorithm inside a DTO-object, and introduce a pipeline:
BufferBlock for storing all the incoming requests. Maybe you can use here a BroadcastBlock, if you want to check the sell or buy options in parallel. You can link the block with a boolean predicate here so the different block will process different types of requests.
ActionBlock (maybe one block for each algorithm from user) for processing the algorithmic check for a pattern based on which you are providing the decision.
ActionBlock for storing all the buy / sell requests for a data successfully passed by the algorithm.
BufferBlock for UI reaction with a Reactive Extensions (Introductory book for Rx, if you aren't familiar with it)
This solution still has to be tuned up with a block creation options, and more informative for you how exactly your data flow across the trading algorithm, the speed of the decision making and overall performance. You should properly examine for a defaults for TPL Dataflow blocks, you can find them into the official documentation. Other good place to start is Stephen Cleary's introductory blog posts (Part 1, Part 2, Part 3) and the chapter #4 about this library in his book.
With C# 5.0, the natural approach is to use async methods running on top of the default thread pool.
This way, you are creating Tasks quite often, but the most prominent cost of that is in GC. And unless you have very high performance requirements, that cost should be acceptable.
I think you would be better with an event loop, and if you need to scale, you can always shard by stock.
I'm struggling with a vehicle availability problem in Excel. I need to track overlapping date-time ranges in a way that will show me how often more than two incidents are going on at the same time.
To put it differently, if I have only two units available to handle transports and I am given a list of start and end times of historical transports, how many times would I miss a transport on that list (how often are more then two transports going on at the same time)?
For example, if I start with 2 units and a call for service comes in, now I have 1 unit available. If another call comes in before the previous call ends, I now have 0 units available. If I get another call for service before either unit returns to service, I will miss a call.
How can I evaluate a list of date-time intervals in Excel to determine which calls I would have missed?
I've tried using =SUMPRODUCT((A2<=$B$2:$B$3584)*(B2>=$A$2:$A$3584)) but this isn't quite right and I can't quite figure out why.
Please try (where I have assumed as a start point 1/1/15 has both units available):
=IF(INDEX(B:B,MATCH(F2,A:A))>F2,1,0)+IF(INDEX(D:D,MATCH(F2,C:C))>F2,1,0)
I have some metrics (like counts of logged in users, or SQL queries, or whatever), and I want to gather some time-dependent stats on a regular basis.
For example I want to know how many users were registered in some particular year, month, week, day or even hour.
I thought maybe Redis can be a good fit. But I can't imagine a good strategy for storing such stats. The only idea I have is to store independent counters for days, weeks, etc, and bump them all at once.
How do you do it? I need a good trick. Or maybe Redis isn't any good for my task.
If all you need is a count for each period, the multiple counter approach you suggest is a good one. Incrementing 5 counters in a single pipelined transaction is O(1), while set operations are O(log n + m) with potentially large values of n/m.
The set solution Frank suggested does have its place - I use something similar where I need to know which actions happened rather than just how many. Obviously storing details of each action takes more memory than the counters, but with the amount of RAM typically available these days you can store millions of records before that becomes a problem.
I would just use a sorted sets where the score is the timestamp in seconds since the epoch (unix time). Say you have a sorted set of logins and you want to see how many logins occured in the year 2010, just convert 20101231 23:59:59 and 20100101 00:00:00 to seconds and use those are the max and min arguments to zcount.
The obviously difficulty here is handling the time conversion yourself, but its actually very easy because it the standard Unix format. You can use the date command with %S (on linux at least) or use the system calls time(), localtime() and mktime(), as well as any of the myriad ways available within specific languages that are built on top of these system calls.
I am sure there is some equivalent paradigm in Windows, but that I don't have any experience there.
I'm looking for a design pattern that would fit my application design.
My application processes large amounts of data and produces some graphs.
Data processing (fetching from files, CPU intensive calculations) and graph operations (drawing, updating) are done in seperate threads.
Graph can be scrolled - in this case new data portions need to be processed.
Because there can be several series on a graph, multiple threads can be spawned (two threads per serie, one for dataset update and one for graph update).
I don't want to create multiple progress bars. Instead, I'd like to have single progress bar that inform about global progress. At the moment I can think of MVC and Observer/Observable, but it's a little bit blurry :) Maybe somebody could point me in a right direction, thanks.
I once spent the best part of a week trying to make a smooth, non-hiccupy progress bar over a very complex algorithm.
The algorithm had 6 different steps. Each step had timing characteristics that were seriously dependent on A) the underlying data being processed, not just the "amount" of data but also the "type" of data and B) 2 of the steps scaled extremely well with increasing number of cpus, 2 steps ran in 2 threads and 2 steps were effectively single-threaded.
The mix of data effectively had a much larger impact on execution time of each step than number of cores.
The solution that finally cracked it was really quite simple. I made 6 functions that analyzed the data set and tried to predict the actual run-time of each analysis step. The heuristic in each function analyzed both the data sets under analysis and the number of cpus. Based on run-time data from my own 4 core machine, each function basically returned the number of milliseconds it was expected to take, on my machine.
f1(..) + f2(..) + f3(..) + f4(..) + f5(..) + f6(..) = total runtime in milliseconds
Now given this information, you can effectively know what percentage of the total execution time each step is supposed to take. Now if you say step1 is supposed to take 40% of the execution time, you basically need to find out how to emit 40 1% events from that algorithm. Say the for-loop is processing 100,000 items, you could probably do:
for (int i = 0; i < numItems; i++){
if (i % (numItems / percentageOfTotalForThisStep) == 0) emitProgressEvent();
.. do the actual processing ..
}
This algorithm gave us a silky smooth progress bar that performed flawlessly. Your implementation technology can have different forms of scaling and features available in the progress bar, but the basic way of thinking about the problem is the same.
And yes, it did not really matter that the heuristic reference numbers were worked out on my machine - the only real problem is if you want to change the numbers when running on a different machine. But you still know the ratio (which is the only really important thing here), so you can see how your local hardware runs differently from the one I had.
Now the average SO reader may wonder why on earth someone would spend a week making a smooth progress bar. The feature was requested by the head salesman, and I believe he used it in sales meetings to get contracts. Money talks ;)
In situations with threads or asynchronous processes/tasks like this, I find it helpful to have an abstract type or object in the main thread that represents (and ideally encapsulates) each process. So, for each worker thread, there will presumably be an object (let's call it Operation) in the main thread to manage that worker, and obviously there will be some kind of list-like data structure to hold these Operations.
Where applicable, each Operation provides the start/stop methods for its worker, and in some cases - such as yours - numeric properties representing the progress and expected total time or work of that particular Operation's task. The units don't necessarily need to be time-based, if you know you'll be performing 6,230 calculations, you can just think of these properties as calculation counts. Furthermore, each task will need to have some way of updating its owning Operation of its current progress in whatever mechanism is appropriate (callbacks, closures, event dispatching, or whatever mechanism your programming language/threading framework provides).
So while your actual work is being performed off in separate threads, a corresponding Operation object in the "main" thread is continually being updated/notified of its worker's progress. The progress bar can update itself accordingly, mapping the total of the Operations' "expected" times to its total, and the total of the Operations' "progress" times to its current progress, in whatever way makes sense for your progress bar framework.
Obviously there's a ton of other considerations/work that needs be done in actually implementing this, but I hope this gives you the gist of it.
Multiple progress bars aren't such a bad idea, mind you. Or maybe a complex progress bar that shows several threads running (like download manager programs sometimes have). As long as the UI is intuitive, your users will appreciate the extra data.
When I try to answer such design questions I first try to look at similar or analogous problems in other application, and how they're solved. So I would suggest you do some research by considering other applications that display complex progress (like the download manager example) and try to adapt an existing solution to your application.
Sorry I can't offer more specific design, this is just general advice. :)
Stick with Observer/Observable for this kind of thing. Some object observes the various series processing threads and reports status by updating the summary bar.