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Is it logical to say: "If average service time for a request is X and affordable waiting time for the requests is Y then maximum number of concurrent requests to serve would be Y / X" ?
I think what I'm asking is that if there're any hidden factors that I'm not taking into account!?
If you're talking specifically about webservers, then no, your formula doesn't work, because webservers are designed to handle multiple, simultaneous requests, using forking or threading.
This turns the formula into something far harder to quantify - in my experience, web servers can handle LOTS (i.e. hundreds or thousands) of concurrent requests which consume little or no time, but tend to reduce that concurrency quite dramatically as the requests consume more time.
That means that "average service time" isn't massively useful - it can hide wide variations, and it's actually the outliers that affect you the most.
Broadly yes, but your service provider (webserver in your case) is capable of handling more than one request in parallel, so you should take that into account. I assume you measured end to end service time and havent already averaged it by number of parallel streams. One other thing you didnt and cannot realistically measure is the delay to/from your website.
What you are heading towards is the Erlang unit (not the language using the same name) which is used to described how much load a system can take. Erlangs are unitless (it is just a number) and originated from old school telephony, POTS, where it was used to describe how many wires were needed to handle X calls per time period with low blocking probability. Beyond erlang is engset which is used more for high capacity systems, such as mobile systems.
It also gets used for expensive consultant reports into realtime computer systems and databases to describe the point at which performance degradation is likely to occur. Wikipedia has an article on this http://en.wikipedia.org/wiki/Erlang_(unit) and the book 'Fixed and mobile telecommunications, network systems and services' has a good chapter on performance analysis.
While aimed at telephone systems, just replace with word webserver and it behaves the same. A webserver is the same concept, load is offered that arrives at random intervals to a system with finite parallel capacity. In your case, you can probably calculate total load with load tools easier than parallel capacity and then back calculate the formulas. This is widely done to gain a level of confidence in overall system models.
Erlang/engsetformulas are really useful when you have a randomly arriving load over parallel stream (ie web requests) and a service time that can only be averaged or estimated (ie it varies in real life). You can then calculate the blocking probability, which is the probability a new request will need to wait while current requests are serviced, and how long it will wait. It also helps analyse whether you need to handle more requests in parallel, or make each faster (#lines and holding time in erlang speak)
You will probably look into queuing systems analysis next, as a soon as requests block (queue), the models change slightly.
many factors are not taken into account
memory limits
data locking constraints such as people wanting to update the same data
application latency
caching mechanisms
different users will have different tasks on the site and put different loads
That said, one easy way to get a rough estimate is with apache ab tool (apache benchmark)
Example, get 1000 times the homepage with 100 requests at a time:
ab -c 100 -n 1000 http://www.example.com/
I am about to start a HTML5 game, with heavy logic in java script, I want to keep some logic at the server side, so that I guarantee that my game will play only at my server.
I decided to chose node.js, as its very fast, I thought about two ways:
To use AJAX, client side will call a server side method which will return calculated numbers to refresh the game scene, this call will be called every 2 second.
To open a socket using node.js, so that client don't have to call the server each time, instead, it keep listening to data streamed from the opened socket, which will refresh data every x seconds.
The calculated data is not big, its about 0.5 kb per one second, client also needs to tell server what's the status, so data sent from client is about 0.1 kb / x second, depends on game play.
It seems that the second approach is better, but, I will need hundred of ports to handle concurrent players ..
So, in term of performance & minimizing used bandwidth, which way to chose? or, is there even a better way? any one can help?
As you mentioned you are creating a web-based JavaScript application that regularly sends information to, or retrieves updates from, a server then in my opinion you should use WebSocket(especially you are developing in HTML5), which reduce the amount of bandwidth your application uses.
In term of performance, I would chose WebSocket aswell, by doing some measurement experiments e.g averaging a round trip time for 100 requests at a time, WebSocket has a lower round trip time. Here is a link of a performance test might tell the result: http://www.peterbe.com/plog/are-websockets-faster-than-ajax
I am trying to reduce packets manipulation to its minimum in order to improve efficiency of a specific program i am working on but i am struggling with the time it takes to send through a udp socket using sendto/recvfrom. I am using 2 very basic processes (applications), one is sending, the other one receiving.
I am willing to understand how linux internally works when using these function calls...
Here are my observations:
when sending packets at:
10Kbps, the time it takes for the messages to go from one application to the other is about 28us
400Kbps, the time it takes for the messages to go from one application to the other is about 25us
4Mbps, the time it takes for the messages to go from one application to the other is about 20us
40Mbps, the time it takes for the messages to go from one application to the other is about 18us
When using different CPUs, time is obviously different and consistent with those observations. There must be some sort of setting that enables some queue readings to be done faster depending on the traffic flow on a socket... how can that be controlled?
When using a node as a forwarding node only, going in and out takes about 8us when using 400Kbps flow, i want to converge to this value as much as i can. 25us is not acceptable and deemed to slow (it is obvious that this is way less than the delay between each packet anyway... but the point is to be able to eventually have a greater deal of packets to be processed, hence, this time needs to be shortened!). Is there anything faster than sendto/recvfrom (must use 2 different applications (processes), i know i cannot use a monolitic block, thus i need info to be sent on a socket)?
I'm going through a bit of a re-think of large-scale multiplayer games in the age of Facebook applications and cloud computing.
Suppose I were to build something on top of existing open protocols, and I want to serve 1,000,000 simultaneous players, just to scope the problem.
Suppose each player has an incoming message queue (for chat and whatnot), and on average one more incoming message queue (guilds, zones, instances, auction, ...) so we have 2,000,000 queues. A player will listen to 1-10 queues at a time. Each queue will have on average maybe 1 message per second, but certain queues will have much higher rate and higher number of listeners (say, a "entity location" queue for a level instance). Let's assume no more than 100 milliseconds of system queuing latency, which is OK for mildly action-oriented games (but not games like Quake or Unreal Tournament).
From other systems, I know that serving 10,000 users on a single 1U or blade box is a reasonable expectation (assuming there's nothing else expensive going on, like physics simulation or whatnot).
So, with a crossbar cluster system, where clients connect to connection gateways, which in turn connect to message queue servers, we'd get 10,000 users per gateway with 100 gateway machines, and 20,000 message queues per queue server with 100 queue machines. Again, just for general scoping. The number of connections on each MQ machine would be tiny: about 100, to talk to each of the gateways. The number of connections on the gateways would be alot higher: 10,100 for the clients + connections to all the queue servers. (On top of this, add some connections for game world simulation servers or whatnot, but I'm trying to keep that separate for now)
If I didn't want to build this from scratch, I'd have to use some messaging and/or queuing infrastructure that exists. The two open protocols I can find are AMQP and XMPP. The intended use of XMPP is a little more like what this game system would need, but the overhead is quite noticeable (XML, plus the verbose presence data, plus various other channels that have to be built on top). The actual data model of AMQP is closer to what I describe above, but all the users seem to be large, enterprise-type corporations, and the workloads seem to be workflow related, not real-time game update related.
Does anyone have any daytime experience with these technologies, or implementations thereof, that you can share?
#MSalters
Re 'message queue':
RabbitMQ's default operation is exactly what you describe: transient pubsub. But with TCP instead of UDP.
If you want guaranteed eventual delivery and other persistence and recovery features, then you CAN have that too - it's an option. That's the whole point of RabbitMQ and AMQP -- you can have lots of behaviours with just one message delivery system.
The model you describe is the DEFAULT behaviour, which is transient, "fire and forget", and routing messages to wherever the recipients are. People use RabbitMQ to do multicast discovery on EC2 for just that reason. You can get UDP type behaviours over unicast TCP pubsub. Neat, huh?
Re UDP:
I am not sure if UDP would be useful here. If you turn off Nagling then RabbitMQ single message roundtrip latency (client-broker-client) has been measured at 250-300 microseconds. See here for a comparison with Windows latency (which was a bit higher) http://old.nabble.com/High%28er%29-latency-with-1.5.1--p21663105.html
I cannot think of many multiplayer games that need roundtrip latency lower than 300 microseconds. You could get below 300us with TCP. TCP windowing is more expensive than raw UDP, but if you use UDP to go faster, and add a custom loss-recovery or seqno/ack/resend manager then that may slow you down again. It all depends on your use case. If you really really really need to use UDP and lazy acks and so on, then you could strip out RabbitMQ's TCP and probably pull that off.
I hope this helps clarify why I recommended RabbitMQ for Jon's use case.
I am building such a system now, actually.
I have done a fair amount of evaluation of several MQs, including RabbitMQ, Qpid, and ZeroMQ. The latency and throughput of any of those are more than adequate for this type of application. What is not good, however, is queue creation time in the midst of half a million queues or more. Qpid in particular degrades quite severely after a few thousand queues. To circumvent that problem, you will typically have to create your own routing mechanisms (smaller number of total queues, and consumers on those queues are getting messages that they don't have an interest in).
My current system will probably use ZeroMQ, but in a fairly limited way, inside the cluster. Connections from clients are handled with a custom sim. daemon that I built using libev and is entirely single-threaded (and is showing very good scaling -- it should be able to handle 50,000 connections on one box without any problems -- our sim. tick rate is quite low though, and there are no physics).
XML (and therefore XMPP) is very much not suited to this, as you'll peg the CPU processing XML long before you become bound on I/O, which isn't what you want. We're using Google Protocol Buffers, at the moment, and those seem well suited to our particular needs. We're also using TCP for the client connections. I have had experience using both UDP and TCP for this in the past, and as pointed out by others, UDP does have some advantage, but it's slightly more difficult to work with.
Hopefully when we're a little closer to launch, I'll be able to share more details.
Jon, this sounds like an ideal use case for AMQP and RabbitMQ.
I am not sure why you say that AMQP users are all large enterprise-type corporations. More than half of our customers are in the 'web' space ranging from huge to tiny companies. Lots of games, betting systems, chat systems, twittery type systems, and cloud computing infras have been built out of RabbitMQ. There are even mobile phone applications. Workflows are just one of many use cases.
We try to keep track of what is going on here:
http://www.rabbitmq.com/how.html (make sure you click through to the lists of use cases on del.icio.us too!)
Please do take a look. We are here to help. Feel free to email us at info#rabbitmq.com or hit me on twitter (#monadic).
My experience was with a non-open alternative, BizTalk. The most painful lesson we learnt is that these complex systems are NOT fast. And as you figured from the hardware requirements, that translates directly into significant costs.
For that reason, don't even go near XML for the core interfaces. Your server cluster will be parsing 2 million messages per second. That could easily be 2-20 GB/sec of XML! However, most messages will be for a few queues, while most queues are in fact low-traffic.
Therefore, design your architecture so that it's easy to start with COTS queue servers and then move each queue (type) to a custom queue server when a bottleneck is identified.
Also, for similar reasons, don't assume that a message queue architecture is the best for all comminication needs your application has. Take your "entity location in an instance" example. This is a classic case where you don't want guaranteed message delivery. The reason that you need to share this information is because it changes all the time. So, if a message is lost, you don't want to spend time recovering it. You'd only send the old locatiom of the affected entity. Instead, you'd want to send the current location of that entity. Technology-wise this means you want UDP, not TCP and a custom loss-recovery mechanism.
FWIW, for cases where intermediate results are not important (like positioning info) Qpid has a "last-value queue" that can deliver only the most recent value to a subscriber.
I want to create a system that delivers user interface response within 100ms, but which requires minutes of computation. Fortunately, I can divide it up into very small pieces, so that I could distribute this to a lot of servers, let's say 1500 servers. The query would be delivered to one of them, which then redistributes to 10-100 other servers, which then redistribute etc., and after doing the math, results propagate back again and are returned by a single server. In other words, something similar to Google Search.
The problem is, what technology should I use? Cloud computing sounds obvious, but the 1500 servers need to be prepared for their task by having task-specific data available. Can this be done using any of the existing cloud computing platforms? Or should I create 1500 different cloud computing applications and upload them all?
Edit: Dedicated physical servers does not make sense, because the average load will be very, very small. Therefore, it also does not make sense, that we run the servers ourselves - it needs to be some kind of shared servers at an external provider.
Edit2: I basically want to buy 30 CPU minutes in total, and I'm willing to spend up to $3000 on it, equivalent to $144,000 per CPU-day. The only criteria is, that those 30 CPU minutes are spread across 1500 responsive servers.
Edit3: I expect the solution to be something like "Use Google Apps, create 1500 apps and deploy them" or "Contact XYZ and write an asp.net script which their service can deploy, and you pay them based on the amount of CPU time you use" or something like that.
Edit4: A low-end webservice provider, offering asp.net at $1/month would actually solve the problem (!) - I could create 1500 accounts, and the latency is ok (I checked), and everything would be ok - except that I need the 1500 accounts to be on different servers, and I don't know any provider that has enough servers that is able to distribute my accounts on different servers. I am fully aware that the latency will differ from server to server, and that some may be unreliable - but that can be solved in software by retrying on different servers.
Edit5: I just tried it and benchmarked a low-end webservice provider at $1/month. They can do the node calculations and deliver results to my laptop in 15ms, if preloaded. Preloading can be done by making a request shortly before the actual performance is needed. If a node does not respond within 15ms, that node's part of the task can be distributed to a number of other servers, of which one will most likely respond within 15ms. Unfortunately, they don't have 1500 servers, and that's why I'm asking here.
[in advance, apologies to the group for using part of the response space for meta-like matters]
From the OP, Lars D:
I do not consider [this] answer to be an answer to the question, because it does not bring me closer to a solution. I know what cloud computing is, and I know that the algorithm can be perfectly split into more than 300,000 servers if needed, although the extra costs wouldn't give much extra performance because of network latency.
Lars,
I sincerely apologize for reading and responding to your question at a naive and generic level. I hope you can see how both the lack of specifity in the question itself, particularly in its original form, and also the somewhat unusual nature of the problem (1) would prompt me respond to the question in like fashion. This, and the fact that such questions on SO typically emanate from hypotheticals by folks who have put but little thought and research into the process, are my excuses for believing that I, a non-practionner [of massively distributed systems], could help your quest. The many similar responses (some of which had the benefits of the extra insight you provided) and also the many remarks and additional questions addressed to you show that I was not alone with this mindset.
(1) Unsual problem: An [apparently] mostly computational process (no mention of distributed/replicated storage structures), very highly paralellizable (1,500 servers), into fifty-millisecondish-sized tasks which collectively provide a sub-second response (? for human consumption?). And yet, a process that would only be required a few times [daily..?].
Enough looking back!
In practical terms, you may consider some of the following to help improve this SO question (or move it to other/alternate questions), and hence foster the help from experts in the domain.
re-posting as a distinct (more specific) question. In fact, probably several questions: eg. on the [likely] poor latency and/or overhead of mapreduce processes, on the current prices (for specific TOS and volume details), on the rack-awareness of distributed processes at various vendors etc.
Change the title
Add details about the process you have at hand (see many questions in the notes of both the question and of many of the responses)
in some of the questions, add tags specific to a give vendor or technique (EC2, Azure...) as this my bring in the possibly not quite unbuyist but helpful all the same, commentary from agents at these companies
Show that you understand that your quest is somewhat of a tall order
Explicitly state that you wish responses from effective practionners of the underlying technologies (maybe also include folks that are "getting their feet wet" with these technologies as well, since with the exception of the physics/high-energy folks and such, who BTW traditionnaly worked with clusters rather than clouds, many of the technologies and practices are relatively new)
Also, I'll be pleased to take the hint from you (with the implicit non-veto from other folks on this page), to delete my response, if you find that doing so will help foster better responses.
-- original response--
Warning: Not all processes or mathematical calculations can readily be split in individual pieces that can then be run in parallel...
Maybe you can check Wikipedia's entry from Cloud Computing, understanding that cloud computing is however not the only architecture which allows parallel computing.
If your process/calculation can efficitively be chunked in parallelizable pieces, maybe you can look into Hadoop, or other implementations of MapReduce, for an general understanding about these parallel processes. Also, (and I believe utilizing the same or similar algorithms), there also exist commercially available frameworks such as EC2 from amazon.
Beware however that the above systems are not particularly well suited for very quick response time. They fare better with hour long (and then some) data/number crunching and similar jobs, rather than minute long calculations such as the one you wish to parallelize so it provides results in 1/10 second.
The above frameworks are generic, in a sense that they could run processes of most any nature (again, the ones that can at least in part be chunked), but there also exist various offerings for specific applications such as searching or DNA matching etc. The search applications in particular can have very short response times (cf Google for example) and BTW this is in part tied to fact that such jobs can very easily and quickly be chunked for parallel processing.
Sorry, but you are expecting too much.
The problem is that you are expecting to pay for processing power only. Yet your primary constraint is latency, and you expect that to come for free. That doesn't work out. You need to figure out what your latency budgets are.
The mere aggregating of data from multiple compute servers will take several milliseconds per level. There will be a gaussian distribution here, so with 1500 servers the slowest server will respond after 3σ. Since there's going to be a need for a hierarchy, the second level with 40 servers , where again you'll be waiting for the slowest server.
Internet roundtrips also add up quickly; that too should take 20 to 30 ms of your latency budget.
Another consideration is that these hypothethical servers will spend much of their time idle. That means they're powered on, drawing electricity yet not generating revenue. Any party with that many idle servers would turn them off, or at the very least in sleep mode just to conserve electricity.
MapReduce is not the solution! Map Reduce is used in Google, Yahoo and Microsoft for creating the indexes out of the huge data (the whole Web!) they have on their disk. This task is enormous and Map Reduce was built to make it happens in hours instead of years, but starting a Master controller of Map Reduce is already 2 seconds, so for your 100ms this is not an option.
Now, from Hadoop you may get advantages out of the distributed file system. It may allow you to distribute the tasks close to where the data is physically, but that's it. BTW: Setting up and managing an Hadoop Distributed File System means controlling your 1500 servers!
Frankly in your budget I don't see any "cloud" service that will allow you to rent 1500 servers. The only viable solution, is renting time on a Grid Computing solution like Sun and IBM are offering, but they want you to commit to hours of CPU from what I know.
BTW: On Amazon EC2 you have a new server up in a couple of minutes that you need to keep for an hour minimum!
Hope you'll find a solution!
I don't get why you would want to do that, only because "Our user interfaces generally aim to do all actions in less than 100ms, and that criteria should also apply to this".
First, 'aim to' != 'have to', its a guideline, why would u introduce these massive process just because of that. Consider 1500 ms x 100 = 150 secs = 2.5 mins. Reducing the 2.5 mins to a few seconds its a much more healthy goal. There is a place for 'we are processing your request' along with an animation.
So my answer to this is - post a modified version of the question with reasonable goals: a few secs, 30-50 servers. I don't have the answer for that one, but the question as posted here feels wrong. Could even be 6-8 multi-processor servers.
Google does it by having a gigantic farm of small Linux servers, networked together. They use a flavor of Linux that they have custom modified for their search algorithms. Costs are software development and cheap PC's.
It would seem that you are indeed expecting at least 1000-fold speedup from distributing your job to a number of computers. That may be ok. Your latency requirement seems tricky, though.
Have you considered the latencies inherent in distributing the job? Essentially the computers would have to be fairly close together in order to not run into speed of light issues. Also, the data center in which the machines would be would again have to be fairly close to your client so that you can get your request to them and back in less than 100 ms. On the same continent, at least.
Also note that any extra latency requires you to have many more nodes in the system. Losing 50% of available computing time to latency or anything else that doesn't parallelize requires you to double the computing capacity of the parallel portions just to keep up.
I doubt a cloud computing system would be the best fit for a problem like this. My impression at least is that the proponents of cloud computing would prefer to not even tell you where your machines are. Certainly I haven't seen any latency terms in the SLAs that are available.
You have conflicting requirements. You're requirement for 100ms latency is directly at odds with your desire to only run your program sporadically.
One of the characteristics of the Google-search type approach you mentioned in your question is that the latency of the cluster is dependent on the slowest node. So you could have 1499 machines respond in under 100ms, but if one machine took longer, say 1s - whether due to a retry, or because it needed to page you application in, or bad connectivity - your whole cluster would take 1s to produce an answer. It's inescapable with this approach.
The only way to achieve the kinds of latencies you're seeking would be to have all of the machines in your cluster keep your program loaded in RAM - along with all the data it needs - all of the time. Having to load your program from disk, or even having to page it in from disk, is going to take well over 100ms. As soon as one of your servers has to hit the disk, it is game over for your 100ms latency requirement.
In a shared server environment, which is what we're talking about here given your cost constraints, it is a near certainty that at least one of your 1500 servers is going to need to hit the disk in order to activate your app.
So you are either going to have to pay enough to convince someone to keep you program active and in memory at all times, or you're going to have to loosen your latency requirements.
Two trains of thought:
a) if those restraints are really, absolutely, truly founded in common sense, and doable in the way you propose in the nth edit, it seems the presupplied data is not huge. So how about trading storage for precomputation to time. How big would the table(s) be? Terabytes are cheap!
b) This sounds a lot like a employer / customer request that is not well founded in common sense. (from my experience)
Let´s assume the 15 minutes of computation time on one core. I guess thats what you say.
For a reasonable amount of money, you can buy a system with 16 proper, 32 hyperthreading cores and 48 GB RAM.
This should bring us in the 30 second range.
Add a dozen Terabytes of storage, and some precomputation.
Maybe a 10x increase is reachable there.
3 secs.
Are 3 secs too slow? If yes, why?
Sounds like you need to utilise an algorithm like MapReduce: Simplified Data Processing on Large Clusters
Wiki.
Check out Parallel computing and related articles in this WikiPedia-article - "Concurrent programming languages, libraries, APIs, and parallel programming models have been created for programming parallel computers." ... http://en.wikipedia.org/wiki/Parallel_computing
Although Cloud Computing is the cool new kid in town, your scenario sounds more like you need a cluster, i.e. how can I use parallelism to solve a problem in a shorter time.
My solution would be:
Understand that if you got a problem that can be solved in n time steps on one cpu, does not guarantee that it can be solved in n/m on m cpus. Actually n/m is the theoretical lower limit. Parallelism is usually forcing you to communicate more and therefore you'll hardly ever achieve this limit.
Parallelize your sequential algorithm, make sure it is still correct and you don't get any race conditions
Find a provider, see what he can offer you in terms of programming languages / APIs (no experience with that)
What you're asking for doesn't exist, for the simple reason that doing this would require having 1500 instances of your application (likely with substantial in-memory data) idle on 1500 machines - consuming resources on all of them. None of the existing cloud computing offerings bill on such a basis. Platforms like App Engine and Azure don't give you direct control over how your application is distributed, while platforms like Amazon's EC2 charge by the instance-hour, at a rate that would cost you over $2000 a day.