CSC 456 Fall 2013/4a bc

Load Balancing

In multi-processor systems, load-balancing is used to break-up and distribute the work load to individual processors in order to make effective use of processor time. When the work load is divided up at compile-time, the balance is said to be statically balanced. Dividing the work load up during run-time is dynamically balancing the load. Static load balancing has reduced overhead as the work is divided before run time. Dynamic load balancing assigns work as processors become idle, so there is greater overhead. However, dynamic balancing can lead to increased performance of load balancing due to being able to assign work to a processor when it does become idle, reducing the overall idle time of processors.

Static Vs. Dynamic Techniques

Static Load balancing

Round Robin

Round robin is a load balancing technique which evenly distributes tasks across available processors. Each processor is lined up, and given a task one after the other until it loops around again back to the first processor. Visualize a dealer in a casino passing out cards to each player in a circle, one at a time. The advantage is that this is a very simple load balancing technique to implement, with very little overhead. A disadvantage is that there is no care given to the job size or performance. This can create problems if a processor is unlucky and is continually assigned large tasks, causing it to fall behind.

Random

Random load balancing relies on the hope that over the course of enough time, workloads are evenly spread by random chance. Random is fairly easy to implement with little overheard. Generating good "random" values is one challenge, because the function is called so many times that any bias will have a large affect. Random suffers from the same drawbacks as round robin though. There is always the chance that a certain processor is randomly picked in an unusually frequent fashion, leading to wait times for other processors. Random could also sparingly assign multiple large tasks to a single processor, which would also lead to uneven load balancing.

Examples of Load Balancing in action

Server Load balancing pseudocode

server_load_vec_desc = sort_descending(server_load_vec);
server_load_vec_asc = sort_ascending(server_load_vec);
while (server_load_vec_desc[0].deviation > DEVIATION_THRESHOLD) {
  populate_range_load_vector(server_load_vec_desc[0].server_name);
  sort descending range_load_vec;
  i=0;
  while (server_load_vec_desc[0].deviation > DEVIATION_THRESHOLD &&
            i < range_load_vec.size()) {
    if (moving range_load_vec[i] from server_load_vec_desc[0] to server_load_vec_asc[0] reduces deviation) {
       add range_load_vec[i] to balance plan
       partial_deviation = range_load_vec[i].loadestimate * loadavg_per_loadestimate;
       server_load_vec_desc[0].loadavg -= partial_deviation;
       server_load_vec_desc[0].deviation -= partial_deviation;
       server_load_vec_asc[0].loadavg += partial_deviation;
       server_load_vec_asc[0].deviation += partial_deviation;
       server_load_vec_asc = sort_ascending(server_load_vec_asc); 
    }
    i++;
  }
  if (i == range_load_vec.size())
    remove server_load_vec_desc[0] and corresponding entry in server_load_vec_asc  
  server_load_vec_desc = sort_descending(server_load_vec_desc);
}




Sources

1. Load Balancing PseudoCode and other information