Assume I'm working on a multiplayer online game. Each group of players may start an instance of the game to play. Take League Of Legends as an example.
At any moment of time, there are many game matches being served at the same time. My question is about the architecture of this case. Here are my suggestions:
Assume we have a cloud with a gateway. Any game instance requires a game server behind this gateway to serve the game. For different clients outside the cloud to access different game servers in the cloud, the gateway may differentiate between connections according to ports. It is like we have one machine with many processes each of them listening on a different port.
Is this the best we can get?
Is there another way for the gateway to differentiate connections and forward them to different game instances?
Notice that these are socket connections NOT HTTP requests to an API gateway.
EDIT 1: This question is not about Load Balancing
The keyword is ports. Will each match be served on a different port? or is there another way to serve multiple services on the same host (host = IP)?
Elaboration: I'm using client-server model for each match instance. So multiple clients may connect to the same match server to participate in the same match. Each match need to be server by a match server.
The limitation in mind is: For one host (=IP) to serve multiple services it need to provide them on different ports. Match 1 on port 1234. So clients participating in match 1 will connect to and communicate with the match server on port 1234.
EDIT 2: Scalability is the target
My match server does not calculate and maintain the world of many matches. It maintains the world of one match. This is why each match need another instance of the match server. It is not scalable to have all clients communicating about different matches to connect to one process and to be processed by one process.
My idea is to serve the world of each match by different process. This will require each process to be listening on a different port.
Example: Any client will start a TCP connection with a server listening on port A. Is there is a way to serve multiple MatchServers on the same port A (so that more simultaneous MatchServers won't result in more ports)?
Is there a better scalable way to serve the different worlds of multiple matches?
Short answer: you probably shouldn't use proxy-gateway to handle user connections unless you are absolutely sure there's no other way - you are severely limiting your scaling ability.
Long answer:
What you've described is just a load balancing problem. You can find plenty of solutions based on given restrictions via google.
For League Of Legends it can be quite simple: using some health-check find server with lowest amount of load and stick (kinda like sticky sessions) current game to this server - until the game is finished any computations for particular game are made there. You could use any kind of caching mechanism to store game - server relation for subsequent requests on gateway side.
Another, a bit more complicated example could be data storage for statistics for particular game - it's usually solved via sharding which is a usual consequence of distributed computing. It could be solved this way: use some kind of hashing function (for example, modulo) with game ID as parameter to calculate server number. For example 18283 mod 15 = 13 for game ID = 18283 and 15 available shards - so 13th server should store/serve this data.
Main problem here would be "rebalancing" - adding/remove a shard from cluster, for example.
Those are just two examples, you can google more of them using appropriate keywords. Just keep in mind that all of this is just a subset of problems of distributed computing.
Related
Assume there is a system that accepts millions of simultaneous WebSocket connections from client applications. I was wondering if there is a way to route WebSocket connections to a specific instance behind a load balancer (or IP/Domain/etc) if clients provide some form of metadata, such as hash key, instance name, etc.
For instance, let's say each WebSocket client of the above system will always belong to a group (e.g. max group size of 100), and it will attempt to communicate with 99 other clients using the above system as a message gateway.
So the system's responsibility is to relay messages sent from clients in a group to other 99 clients in the same group. Clients won't ever need to communicate with other clients who belong to different groups.
Of course, one way to tackle this problem is to use Pubsub system, such that regardless of which instance clients are connected to, the server can simply publish the message to the Pubsub system with a group identifier and other clients can subscribe to the messages with a group identifier.
However, the Pubsub system can potentially encounter scaling challenges, excessive resource usage (single message getting published to thousands of instances), management overhead, latency increase, cost, and etc.
If it is possible to guarantee that WebSocket clients in a group will all be connected to the instance behind LB, we can skip using the Pubsub system and make things simpler, lower latency, and etc.
Would this be something that is possible to do, and if it isn't, what would be the best option?
(I am using Kubernetes in one of the cloud service providers if that matters.)
Routing in HTTP is generally based on the hostname and/or URL path. Sometimes to a lesser degree on other headers like cookies. But in this case it would mean that each group should have it's own unique URL.
But that part is easy, what I think you're really asking is "given arbitrary URLs, how can I get consistent routing?" which is much, much more complicated. The base concept is "consistent hashing", you hash the URL and use that to pick which endpoint to talk to. But then how to do deal with adding or removing replicas without scrambling the mapping entirely. That usually means using a hash ring and assigning portions of the hash space to specific replicas. Unfortunately this is the point where off-the-shelf tools aren't enough. These kinds of systems require deep knowledge of your protocol and system specifics so you'll probably need to rig this up yourself.
I m studying Microservices architecture and I m actually wondering something.
I m quite okay with the fact of using (back) service discovery to make request able on REST based microservices. I need to know where's the service (or at least the front of the server cluster) to make requests. So it make sense to be able to discover an ip:port in that case.
But I was wondering what could be the aim of using service registry / discovery when dealing with AMQP (based only, without HTTP possible calls) ?
I mean, using AMQP is just like "I need that, and I expect somebody to answer me", I dont have to know who's the server that sent me back the response.
So what is the aim of using service registry / discovery with AMQP based microservice ?
Thanks for your help
AMQP (any MOM, actually) provides a way for processes to communicate without having to mind about actual IP addresses, communication security, routing, among other concerns. That does not necessarily means that any process can trust or even has any information about the processes it communicates with.
Message queues do solve half of the process: how to reach the remote service. But they do not solve the other half: which service is the right one for me. In other words, which service:
has the resources I need
can be trusted (is hosted on a reliable server, has a satisfactory service implementation, is located in a country where the local laws are compatible with your requirements, etc)
charges what you want to pay (although people rarely discuss cost when it comes to microservices)
will be there during the whole time window needed to process your service -- keep in mind that servers are becoming more and more volatile. Some servers are actually containers that can last for a couple minutes.
Those two problems are almost linearly independent. To solve the second kind of problems, you have resource brokers in Grid computing. There is also resource allocation in order to make sure that the last item above is correctly managed.
There are some alternative strategies such as multicasting the intention to use a service and waiting for replies with offers. You may have reverse auction in such a case, for instance.
In short, the rule of thumb is that if you do not have an a priori knowledge about which service you are going to use (hardcoded or in some configuration file), your agent will have to negotiate, which includes dynamic service discovery.
The server consists of several services with which a user interacts: profiles, game logics, physics.
I heard that it's a bad practice to have multiple client connections to the same server.
I'm not sure whether I will use UDP or TCP.
The services are realtime, they should reply as fast as possible so I don't want to include any additional rerouting if there are no really important reasons. So are there any reasons to rerote traffic through one external endpoint service to specific internal services in my case?
This seems to be multiple questions in one package. I will try to answer the ones I can identify as separate...
UDP vs TCP: You're saying "real-time", this usually means UDP is the right choice. However, that means having to deal with lost packets and possible re-ordering of packets. But, using UDP leaves a couple of possible delay-decreasing tricks open.
Multiple connections from a single client to a single server: This consumes resources (end-points, as it were) on both the client (probably ignorable) and on the server (possibly a problem, possibly ignorable). The advantage of using separate connections for separate concerns (profiles, physics, ...) is that when you need to separate these onto separate servers (or server farms), you don't need to update the clients, they just need to connect to other end-points, using code that's already tested.
"Re-router" (or "load balancer") needed: Probably not going to be an issue initially. However, it will probably become an issue later. Depending on your overall design and server OS, using UDP may actually become an asset here. UDP packet arrives at the load balancer, dispatched to the right backend and that could then in theory send back a reply with the source IP of the load balancer.
An alternative would be to have a "session broker". The client makes an initial connection to a well-known endpoint, says "I am a client, tell me where my profile, physics, what-have0-you servers are", the broker considers the current load, possibly the location of the client and other things that may make sense and the client then connects to the relevant backends on its own. The downside of this is that it's harder (not impossible, but harder) to silently migrate an ongoing session to a new backend, when there's a load-balancer in the way, this can be done essentially-transparently.
Recently I've added some load-balancing capabilities to a piece of software that I wrote. It is a networked application that does some data crunching based on input coming from a SQL database. Since the crunching can be pretty intensive I've added the capability to have multiple instances of this application running on different servers to split the load but as it is now the load balancing is a manual act. A user must specify which instances take which portion of the input domain.
I would like to take that to the next level and program the instances to automatically negotiate the diving up of the input data and to recognize if one of them "disappears" (has crashed or has been powered down) so that the remaining instances can take on the failed instance's workload.
In order to implement this I'm considering using a simple heartbeat protocol between the instances to determine who's online and who isn't and while this is not terribly complicated I'd like to know if there are any established heartbeat network protocols (based on UDP, TCP or both).
Obviously this happens a lot in the networking world with clustering, fail-over and high-availability technologies so I guess in the end I'd like to know if maybe there are any established protocols or algorithms that I should be aware of or implement.
EDIT
It seems, based on the answers, that either there are no well established heart-beat protocols or that nobody knows about them (which would imply that they aren't so well established after all) in which case I'm just going to roll my own.
While none of the answers offered what I was looking for specifically I'm going to vote for Matt Davis's answer since it was the closest and he pointed out a good idea to use multicast.
Thank you all for your time~
Distribued Interactive Simulation (DIS), which is defined under IEEE Standard 1278, uses a default heartbeat of 5 seconds via UDP broadcast. A DIS heartbeat is essentially an Entity State PDU, which fully defines the state, including the position, of the given entity. Due to its application within the simulation community, DIS also uses a concept referred to as dead-reckoning to provide higher frequency heartbeats when the actual position, for example, is outside a given threshold of its predicted position.
In your case, a DIS Entity State PDU would be overkill. I only mention it to make note of the fact that heartbeats can vary in frequency depending on the circumstances. I don't know that you'd need something like this for the application you described, but you never know.
For heartbeats, use UDP, not TCP. A heartbeat is, by nature, a connectionless contrivance, so it goes that UDP (connectionless) is more relevant here than TCP (connection-oriented).
The thing to keep in mind about UDP broadcasts is that a broadcast message is confined to the broadcast domain. In short, if you have computers that are separated by a layer 3 device, e.g., a router, then broadcasts are not going to work because the router will not transmit broadcast messages from one broadcast domain to another. In this case, I would recommend using multicast since it will span the broadcast domains, providing the time-to-live (TTL) value is set high enough. It's also a more automated approach than directed unicast, which would require the sender to know the IP address of the receiver in order to send the message.
Broadcast a heartbeat every t using UDP; if you haven't heard from a machine in more than k*t, then it's assumed down. Be careful that the aggregate bandwidth used isn't a drain on resources. You can use IP broadcast addresses, or keep a list of specific IPs you're doing work for.
Make sure the heartbeat includes a "reboot count" as well as "machine ID" so that you know previous server state isn't around.
I'd recommend using MapReduce if it fits. It would save a lot of work.
I'm not sure this will answer the question but you might be interested by the way Weblogic Server clustering work under the hood. From the book Mastering BEA WebLogic Server:
[...] WebLogic Server clustering provides a loose coupling of the servers in the cluster. Each server in the cluster is independent and does not rely on any other server for any fundamental operations. Even if contact with every other server is lost, each server will continue to run and be able to process the requests it receives. Each server in the cluster maintains its own list of other servers in the cluster through periodic heartbeat messages. Every 10 seconds, each server sends a heartbeat message to the other servers in the cluster to let them know it is still alive. Heartbeat messages are sent using IP multicast technology built into the JVM, making this mechanism efficient and scalable as the number of servers in the cluster gets large. Each server receives these heartbeat messages from other servers and uses them to maintain its current cluster membership list. If a server misses receiving three heartbeat messages in a row from any other server, it takes that server out of its membership list until it receives another heartbeat message from that server. This heartbeat technology allows servers to be dynamically added and dropped from the cluster with no impact on the existing servers’ configurations.
Cisco content switches are a hardware solution for this problem. They implement a virtual IP address as a front end to multiple real servers, whose real IP addresses are known to the switch. The switch periodically sends HTTP HEAD requests to the web servers, to verify they are still running (which the switch software calls a "keepalive", although this doesn't keep the server itself alive). The Cisco switch accepts traffic on the virtual IP and forwards it to the actual web servers, using configurable load balancing such as round-robin, or user-defined load balancing.
These switches retail in the $3-10K range, although my business partner picked one up on eBay for about $300 a year ago. If you can afford one, they do represent a proven hardware solution to the question of how to have a service spread transparently across multiple servers. Redhat includes a built-in port configuration so that you could implement your own Cisco switch using a cheap RedHat box. Google for "virtual ip address" and "cisco content router" for more information.
In addition to trying hardware load-balancers, you can also try a free-open-source load-balancing software application such as HAProxy, available for Linux and the BSDs.
Is it possible to multiplex sa ocket connection?
I need to establish multiple connections to yahoo messenger and i am looking for a way to do this efficiently without having to hold a socket open for each client connection.
so far i have to use one socket for each client and this does not scale well above 50,000 connections.
oh, my solution is for a TELCO, so i need to at least hit 250,000 to 500,000 connections
i'm planing to bind multiple IP addresses to a single NIC to beat the 65k port restriction per IP address.
Please i would any help, insight i can get.
**most of my other questions on this site have gone un-answered :) **
Thanks
This is an interesting question about scaling in a serious situation.
You are essentially asking, "How do I establish N connections to an internet service, where N is >= 250,000".
The only way to do this effectively and efficiently is to cluster. You cannot do this on a single host, so you will need to be able to fragment and partition your client base into a number of different servers, so that each is only handling a subset.
The idea would be for a single server to hold open as few connections as possible (spreading out the connectivity evenly) while holding enough connections to make whatever service you're hosting viable by keeping inter-server communication to a minimum level. This will mean that any two connections that are related (such as two accounts that talk to each other a lot) will have to be on the same host.
You will need servers and network infrastructure that can handle this. You will need a subnet of ip addresses, each server will have to have stateless communication with the internet (i.e. your router will not be doing any NAT in order to not have to track 250,000+ connections).
You will have to talk to AOL. There is no way that AOL will be able to handle this level of connectivity without considering cutting your connection off. Any service of this scale would have to be negotiated with AOL so both you and they would be able to handle the connectivity.
There are i/o multiplexing technologies that you should investigate. Kqueue and epoll come to mind.
In order to write this massively concurrent and teleco grade solution, I would recommend investigating erlang. Erlang is designed for situations such as these (multi-server, massively-multi-client, massively-multithreaded telecommunications grade software). It is currently used for running Ericsson telephone exchanges.
While you can listen on a socket for multiple incoming connection requests, when the connection is established, it connects a unique port on the server to a unique port on the client. In order to multiplex a connection, you need to control both ends of the pipe and have a protocol that allows you to switch contexts from one virtual connection to another or use a stateless protocol that doesn't care about the client's identity. In the former case you'd need to implement it in the application layer so that you could reuse existing connections. In the latter case you could get by using a proxy that keeps track of which server response goes to which client. Since you're connecting to Yahoo Messenger, I don't think you'll be able to do this since it requires an authenticated connection and it assumes that each connection corresponds to a single user.
You can only multiplex multiple connections over a single socket if the other end supports such an operation.
In other words it's a function protocol - sockets don't have any native support for it.
I doubt yahoo messenger protocol has any support for it.
An alternative (to multiple IPs on a single NIC) is to design your own multiplexing protocol and have satellite servers that convert from the multiplex protocol to the yahoo protocol.
I'll trow in another approach you could consider (depending on how desperate you are).
Note that operating system TCP/IP implementations need to be general purpose, but you are only interested in a very specific use-case. So it might make sense to implement a cut-down version of TCP/IP (which only handles your use-case, but does that very well) in your application code.
For example, if you are using Linux, you could route a couple of IP addresses to a tun interface and have your application handle the IP packets for that tun interface. That way you can implement TCP/IP (optimised for your use-case) entirely in your application and avoid any operating system restriction on the number of open connections.
Of course, it's quite a bit of work doing the TCP/IP yourself, but it really depends on how desperate you are - i.e. how much hardware can you afford to throw at the problem.
500,000 arbitrary yahoo messenger connections - is your telco doing this on behalf of Yahoo? It seems like whatever solution has been in place for many years now should be scalable with the help of Moore's Law - and as far as I know all the IM clients have been pretty effective for a long time, and there's no pressing increase in demand that I can think of.
Why isn't this a reasonable problem to address with hardware plus traditional solutions?