Erik Svedäng, the winner of IGF 2009, explains the high-level concepts behind networking games.
This article will try to explain a handful of fundamental concepts that all are central to how networked games work. It does not contain any code examples and tries to not delve into minor details. Instead, its goal is to prepare someone new to the field for thinking about networking from a high-level perspective; what problems can arise and how they are commonly solved. The information in here is very useful for understanding the coherence SDK, but it should also be general enough to be applicable to any other similar networking library.
Generally speaking, there are two main ways to synchronize state; we can either send inputs, or the updated data itself. It is also possible to mix these approaches in various ways. We will now discuss each of the options briefly.
It is usually possible to enumerate a number of predefined inputs that the players of the game are allowed to perform (e.g. "jump", "run", "activate"). When an input is applied to the local game state, we can also make sure it is simultaneously sent to every other player in the session. If we make sure that each player starts the game in exactly the same state, and make sure that everyone applies exactly the same inputs as everyone else, the game state will appear in sync for each player. For certain types of games, this can save a lot of data from having to be transferred.
It is noteworthy that sending inputs doesn't necessarily require a server; thus it is a great model to be used in a peer-to-peer setting.
A second approach is to send the updated data itself. This can often be more costly in terms if data transfer (a single player action can change a lot of local data, which in turn has to be transmitted to the other players). It leads to some nice benefits though; most importantly that game states are allowed to diverge slightly, as long as they have a chance to catch up.
Since it's the clients that run the simulation locally and then send the updated game state to the server, this setup can be referred to as client-authoritative.
It is also worth noting that you can combine client-authoritative simulation with inputs in interesting and useful ways. For example, it is possible to let players simulate some less-critical parts of the game state locally, while still sending inputs for their characters to a central server to be processed.
As stated before, a game contains a lot of data and it is not feasible to send all of it over the network in a real-time fashion. While using inputs is often the most lightweight choice in terms of data usage, it is common to have to send updates to the game state - both from the client to the server, and vice versa. In both those cases we have to use some optimizations. Here are the most important ones.
By keeping track of what the other players know about the state of your game, it is often possible to avoid a lot of data transfer. For example, a player might drop some game object on the ground and send the new location of it to each other participant. Unless that object moves, it is unnecessary to keep sending the same position over and over. This simple idea is used pervasively in coherence (and other similar networking solutions) to great effect.
It's important to acknowledge that a game sometimes generates many changes in a short timeframe. In such a situation, it is useful to prioritize changes based on how important they are for the particular game in question, while also factoring in how long it has been on hold. This means that an "old" change that doesn't get sent will build up importance and relative priority compared to other changes, eventually getting sent.
This modular approach where various tasks are performed by different programs, potentially on different machines or from different physical locations, can help with the scaling of a game if it has many users.
Most people who play computer games versus other people online want it to be fair, with equal conditions for each player.
If your game is client-authoritative, with clients sending updates of the game state to the server, we can't verify the validity of such an update and it becomes a problem. It would be quite feasible for a savvy player to modify their game and remove certain limitations put there by the game developer.
As an example, a game client could send an update that sets the health of each enemy to 0. To prevent such blatant cheating, it is useful to introduce the concept of authority (also often called "ownership"). This means that the Replication Server keeps track of which client has the rights to update each entity in the game. If an unauthorized update is sent to the server, it is rejected and will not get sent to any other participant.
For an input-based game, the cheating problem is slightly different. Since inputs will have to be applied in the right situation to have any effect, it is much harder to simply set the game state to illegal values. The role of authority in this case is to make sure that no player sends inputs for a game object they shouldn't be able to control.
This design does not work well for fast-paced games, since their simulations run at many frames per second. By the time a lost network message has been resent and finally made it to its final address, the information in it will have a high chance of already being outdated.
Sending data from one computer to another takes time, and there's no way around that. As a programmer of a networked game, it is important to embrace this fact and recognize that it changes how you must think about your game logic. When programming a single-player game (especially if it only runs on a single processor thread) we can assume that any change to the game state is immediate. In a networked game, this is not true.
This means that each player of a networked game is playing in their own "parallel universe", which affect each other at a distance. Updates to data that you don't have authority over will appear in an irregular and unpredictable way. Because of this it is beneficial to use a defensive coding style that tries to correct for out-of-order updates, and other unexpected circumstances.