I have a question on theory of multi-agent systems. The question is about how do agents understand messages sended by other agents. What algorithms or methods can be used to parse text in messages?
How do agent decide what acrion should be done when it has read message?
I have read tons of articles about multi-agent systems, but no one of them discusses the matter of parsing messages and making decisions based on what they read.
Probably someone would be so kind and share with me some information, examples about that.
Related
I'm working with RabbitMQ 3.7, and I'm finding that my microservice architecture is starting to feel tangled and coupled.
I'm finding that I'm publishing messages from within my consumer's received event to other queues. This feels wrong. But I'm not sure what the alternative is, since I benefit from the efficiency in passing the data from the consumer directly to the next queue/task.
Note that the above is just an example, and the service I'm running are similar, and fairly work-flow dependent (although they can be ran independently!)
Questions:
How is data normally passed from process to process (or consumer to publisher) in situations where the micro-services are fairly dependent on each other. Not that they can't be ran individually, but that they work best in a work-flow scenario?
If the solution involves not publishing new messages from within the received event of a consumer, then what is the proper way to get the data to that microservice/process?
I find that chaining workflows across queues can create more complex workflows than desired, where on the other hand, creating simpler consumer applications can make for more maintainable code.
Do you gain or lose any scalability or simplicity in your code by splitting the first two steps? Without more detailed info to consider, I probably would not split up the first two parts of the functionality. I don't see anything wrong with directly storing the scraping results.
I like your isolated consumer for sending email, though you might consider making a generic email sending consumer that any of your applications could use and have the message format contain the proper mail parts and have the consumer construct the mail and deliver it.
I don't think there's a "right" answer to your architecture here other than to think about finding the right balance of simplicity/complexity, scalability, and maintainability.
I am not a senior programmer but I have been deploying applications for a while and devloped small complete systems.
I am starting to hear about queueing systems such as RabbitMQ. May be, I never developed any systems that had to use a queueing system. But, I am worried if I am not using it because I have no idea what to do with this. I have read RabbitMQ tutorial on their site but I am not sure why I would use this for. I am not sure if any of those cannot be achieved by conventional programming with no additional component and regular databases or similar.
Can someone please explain why I would use a queueing system with a small example. I mean not a hello world example, but a a practical scenario.
Thanks a lot for your time
RM
One of the key uses of middleware like message queues is to be able to send data between non homogenous systems. The messages themselves can be many things. Strings are the easiest to be understood by different languages on different systems but are often less useful for transferring more meaningful data. As a result JSON and XML are very popular for the messages. These are just structured strings that can be converted into objects in the language of choice at the consumer end.
Additional useful features:
In some MQ systems like RabbitMQ (not true in all MQ systems) is that the client handles the communication side of things very nicely.
The messages can be asynchronous. If the consumer goes down, the messages will remain until the consumer is back online.
The MQ system can be setup to varying degrees of message durability. They can be removed from the queue once read or remain until the are acknowledged. They can be persistent so even if the MQ systems goes down message will not be lost.
Here goes with some possibly contrived examples. A Java program on a local system wants to send a message to a system on the connected through the internet. The local system has a server connected to the internet. Everything is blocked coming from the internet except a connection to the MQ. The Java program can publish the message to the MQ with out needing access to the internet. The message sits on the queue until the external system picks it up. The Java program publishes a message, lets say XML, and the consumer could be a Perl program. As long as they have some way of understanding the XML with a predefined way of serialization and deserialization it will be fine.
MQ systems tend to work best in "fire-and-forget" scenarios. If an event happens and others need to be notified of it, but the source system has no need for feedback from the other systems, then MQ might be a good fit.
If you understand the pros and cons of MQ and still don't understand why it would be a good fit for a particular system, then it probably isn't. I've seen systems where MQ was used but not needed, and the result was not pretty.
Most of the scenarios I've seen where it's worked out well is integration between unrelated systems (usually out-of-the-box type system). Let's say you have one system that takes orders, and a different system that fills the orders and ships them. In that scenario, the order system can use a MQ to notify the fulfillment system of the order, but the order system has no interest in waiting until the fulfillment system receives the order. So it puts a message in a queue keep going.
This is a very simplified answer, but it gives the general ideas.
Let's think about this in terms of telephone vs. email. Pretend for a minute that email does not exist. To get work done, you must phone everyone. When you communicate with someone via telephone, you need to have them at their desk in order to reach them (assume they are in a factory and can't hear their cell phone ring) :-) If the person you wish to reach isn't at the desk, you are stuck waiting until they return your call (or far more likely, you call them back later). It's the same with you - you don't have any work to do until someone calls you up. If multiple people call at once, you don't know about it because you can only handle one person at a time.
However, if we have email, it is possible for you to "queue" your requests with someone else, to answer (but more likely ignore) at their convenience. If they do ignore your email, you can always re-send it. You don't have to wait for them to be at the desk, and they don't have to wait until you are off the phone. The workload evens out and things run much more smoothly. As an added bonus, you can forward messages that you don't want to deal with to your peons.
In systems engineering, we use the term "closely coupled" to define programs (or parts of programs) that work like the telephone scenario above. They depend very closely upon each other, often sharing implementations among various parts of the program. In these programs, data is processed in serial order, one at a time. These systems are typically easy to build, but there are a few important drawbacks to consider: (1) changing any part of the program likely will cause cascading changes throughout the code, and this introduces bugs; (2) the system is not very scalable, and typically must be scrapped and rebuilt as needs grow; (3) all parts of the system must be functioning simultaneously or the whole system will not work.
Basically, closely-coupled programs are good if the program is very simple or if there is some specialized reason to use a closely-coupled program.
In the real world, things are much more complex. Programs cannot be that simple, and it becomes a nightmare to develop enterprise applications in a closely-coupled manner. Therefore, we use the term "loosely-coupled" to define large systems that are composed of many smaller pieces. The pieces have very well-defined boundaries and functions, so that changing of the system may be accomplished more easily. It is the essence of object-oriented design. Message queues (like RabbitMQ) allow email-like communication to take place among various programs and parts of programs, thus making workflow much more like it would be with people. Adding extra capacity then becomes a simple matter of starting up and additional computer wherever you need it.
Obviously, this is a gross simplification, but I think it conveys the general idea. Building applications that use message queuing enables you to deploy massively scalable applications leveraging cloud service providers. Here is an article that talks about designing for the cloud:
http://blogs.msdn.com/b/silverlining/archive/2011/08/23/designing-and-building-applications-for-the-cloud.aspx
Is there a "smart" UDP protocol analyzer that can help me reverse engineer a message based protocol?
I'm using Wireshark to do the sniffing, but if there's a tool that can detect regularities in the protocol (repeated strings, bits of the protocol that are CRC/Checksum or length, ...) and aid the process that would help.
You are asking for a universal inference engine. The best way to try to recover the protocol (assuming you are in a jurisdiction that permits this) is to understand the underlying message transfer from the beginning of a session, and then trying to manually simulate the behaviour of each party through a sequence of ping-pong message trials. This way you develop an understanding of the message structures and their functioning.
Using the UDP frame boundaries is a good place to start looking for structure.
If you have no documentation, you will find that even if you gain a good understanding of the protocol, expect to be surprised many times during the project.
If you can, have your existing systems carry out exactly the scenario you need to use, and then simply replicate the same sequence with payload (and any checksum) changes only. This way you can possibly achieve the requirement without a comprehensive understanding of the protocol.
For an example of the effort in doing this you could look at a historical review of the Samba project at A bit of history and a bit of fun.
as opposed to writing your own library.
We're working on a project here that will be a self-dividing server pool, if one section grows too heavy, the manager would divide it and put it on another machine as a separate process. It would also alert all connected clients this affects to connect to the new server.
I am curious about using ZeroMQ for inter-server and inter-process communication. My partner would prefer to roll his own. I'm looking to the community to answer this question.
I'm a fairly novice programmer myself and just learned about messaging queues. As i've googled and read, it seems everyone is using messaging queues for all sorts of things, but why? What makes them better than writing your own library? Why are they so common and why are there so many?
what makes them better than writing your own library?
When rolling out the first version of your app, probably nothing: your needs are well defined and you will develop a messaging system that will fit your needs: small feature list, small source code etc.
Those tools are very useful after the first release, when you actually have to extend your application and add more features to it.
Let me give you a few use cases:
your app will have to talk to a big endian machine (sparc/powerpc) from a little endian machine (x86, intel/amd). Your messaging system had some endian ordering assumption: go and fix it
you designed your app so it is not a binary protocol/messaging system and now it is very slow because you spend most of your time parsing it (the number of messages increased and parsing became a bottleneck): adapt it so it can transport binary/fixed encoding
at the beginning you had 3 machine inside a lan, no noticeable delays everything gets to every machine. your client/boss/pointy-haired-devil-boss shows up and tell you that you will install the app on WAN you do not manage - and then you start having connection failures, bad latency etc. you need to store message and retry sending them later on: go back to the code and plug this stuff in (and enjoy)
messages sent need to have replies, but not all of them: you send some parameters in and expect a spreadsheet as a result instead of just sending and acknowledges, go back to code and plug this stuff in (and enjoy.)
some messages are critical and there reception/sending needs proper backup/persistence/. Why you ask ? auditing purposes
And many other use cases that I forgot ...
You can implement it yourself, but do not spend much time doing so: you will probably replace it later on anyway.
That's very much like asking: why use a database when you can write your own?
The answer is that using a tool that has been around for a while and is well understood in lots of different use cases, pays off more and more over time and as your requirements evolve. This is especially true if more than one developer is involved in a project. Do you want to become support staff for a queueing system if you change to a new project? Using a tool prevents that from happening. It becomes someone else's problem.
Case in point: persistence. Writing a tool to store one message on disk is easy. Writing a persistor that scales and performs well and stably, in many different use cases, and is manageable, and cheap to support, is hard. If you want to see someone complaining about how hard it is then look at this: http://www.lshift.net/blog/2009/12/07/rabbitmq-at-the-skills-matter-functional-programming-exchange
Anyway, I hope this helps. By all means write your own tool. Many many people have done so. Whatever solves your problem, is good.
I'm considering using ZeroMQ myself - hence I stumbled across this question.
Let's assume for the moment that you have the ability to implement a message queuing system that meets all of your requirements. Why would you adopt ZeroMQ (or other third party library) over the roll-your-own approach? Simple - cost.
Let's assume for a moment that ZeroMQ already meets all of your requirements. All that needs to be done is integrating it into your build, read some doco and then start using it. That's got to be far less effort than rolling your own. Plus, the maintenance burden has been shifted to another company. Since ZeroMQ is free, it's like you've just grown your development team to include (part of) the ZeroMQ team.
If you ran a Software Development business, then I think that you would balance the cost/risk of using third party libraries against rolling your own, and in this case, using ZeroMQ would win hands down.
Perhaps you (or rather, your partner) suffer, as so many developers do, from the "Not Invented Here" syndrome? If so, adjust your attitude and reassess the use of ZeroMQ. Personally, I much prefer the benefits of Proudly Found Elsewhere attitude. I'm hoping I can proud of finding ZeroMQ... time will tell.
EDIT: I came across this video from the ZeroMQ developers that talks about why you should use ZeroMQ.
what makes them better than writing your own library?
Message queuing systems are transactional, which is conceptually easy to use as a client, but hard to get right as an implementor, especially considering persistent queues. You might think you can get away with writing a quick messaging library, but without transactions and persistence, you'd not have the full benefits of a messaging system.
Persistence in this context means that the messaging middleware keeps unhandled messages in permanent storage (on disk) in case the server goes down; after a restart, the messages can be handled and no retransmit is necessary (the sender does not even know there was a problem). Transactional means that you can read messages from different queues and write messages to different queues in a transactional manner, meaning that either all reads and writes succeed or (if one or more fail) none succeeds. This is not really much different from the transactionality known from interfacing with databases and has the same benefits (it simplifies error handling; without transactions, you would have to assure that each individual read/write succeeds, and if one or more fail, you have to roll back those changes that did succeed).
Before writing your own library, read the 0MQ Guide here: http://zguide.zeromq.org/page:all
Chances are that you will either decide to install RabbitMQ, or else you will make your library on top of ZeroMQ since they have already done all the hard parts.
If you have a little time give it a try and roll out your own implemntation! The learnings of this excercise will convince you about the wisdom of using an already tested library.
I am reading an article in IEEE Computer magazine about using data mining on applications.
The part that is intriguing to me is the idea that we can have software that can monitor the execution flow of an program, and put the data into a database, where we can do some data mining.
This data could then be used by a data mining tool to look for information, such as if there is certain patterns that may be called that may lead to changing the API, and, ideally, it may also be able to determine bugs, in that if you have to call functions in some order, it can help detect that.
There are probably other uses, but this would be a start.
So, would such a tool be useful?
I am thinking that AOP may be the only way to really do this on a dynamic application, as you could then track the flow of every call and save the stack, and perhaps gather some other information, such as parameters.
Unfortunately software engineers don't tend to be experts on data mining, and those that do data mining may not be an expert on writing complex applications.
For me, where this would get interesting is to then start to analyze distributed applications, or those using cloud computing, but that may be very complicated.
Second question, is this type of question that should be a community wiki?
Yes, I think it would be useful.
No, it shouldn't be a community wiki.
Check out the book "Programming Collective Intelligence" by Segaran for some good programmatic use of data mining strategies.