Numerous scheduling algorithms are used by load balancers to determine which back-end server to send a request to. Simple algorithms include random choice or round robin. More sophisticated load balancers may take additional factors into account, such as a server's reported load, least response
times, up/down status (determined by a monitoring poll of some kind), number of active connections, geographic location, capabilities, or how much traffic it has recently been assigned.
An important issue when operating a load-balanced service is how to handle information that must be kept across the multiple requests in a user's session. If this information is stored locally on one backend server, then subsequent requests going to different backend servers would not be able to find it. This might be cached information that can be recomputed, in which case load-balancing a request to a different backend server just introduces a performance issue.
Ideally the cluster of servers behind the load balancer should be session-aware, so that if a client connects to any backend server at any time the user experience is unaffected. This is usually achieved with a shared database or an in-memory session database, for example Memcached.
One basic solution to the session data issue is to send all requests in a user session consistently to the same backend server. This is known as persistence or stickiness. A significant downside to this technique is its lack of automatic failover: if a backend server goes down, its per-session information becomes inaccessible, and any sessions depending on it are lost. The same problem is usually relevant to central database servers; even if web servers are "stateless" and not "sticky", the central database is (see below).
Assignment to a particular server might be based on a username, client IP address, or be random. Because of changes of the client's perceived address resulting from DHCP, network address translation, and web proxies this method may be unreliable. Random assignments must be remembered by the load balancer, which creates a burden on storage. If the load balancer is replaced or fails, this information may be lost, and assignments may need to be deleted after a timeout period or during periods of high load to avoid exceeding the space available for the assignment table. The random assignment method also requires that clients maintain some state, which can be a problem, for example when a web browser has disabled storage of cookies. Sophisticated load balancers use multiple persistence techniques to avoid some of the shortcomings of any one method.
Another solution is to keep the per-session data in a database. Generally this is bad for performance because it increases the load on the database: the database is best used to store information less transient than per-session data. To prevent a database from becoming a single point of failure, and to improve scalability, the database is often replicated across multiple machines, and load balancing is used to spread the query load across those replicas. Microsoft's ASP.net State Server technology is an example of a session database. All servers in a web farm store their session data on State Server and any server in the farm can retrieve the data.
In the very common case where the client is a web browser, a simple but efficient approach is to store the per-session data in the browser itself. One way to achieve this is to use a browser cookie, suitably time-stamped and encrypted. Another isURL rewriting. Storing session data on the client is generally the preferred solution: then the load balancer is free to pick any backend server to handle a request. However, this method of state-data handling is poorly suited to some complex business logic scenarios, where session state payload is big and recomputing it with every request on a server is not feasible. URL rewriting has major security issues, because the end-user can easily alter the submitted URL and thus change session streams.
Yet another solution to storing persistent data is to associate a name with each block of data, and use a distributed hash table to pseudo-randomly assign that name to one of the available servers, and then store that block of data in the assigned server.
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