[poolifier.git] / src / pools / selection-strategies / README.md

# Worker choice strategies

## Strategies

### Fair share

Its goal is to distribute the load evenly across all workers. To achieve this, the strategy keeps track of the average task execution time for each worker and assigns the next task to the worker with the lowest task end prediction time: `task_end_prediction = max(current_time, task_end_prediction) + average_task_execution_time`.  
By default, the strategy uses the average task execution time for each worker but it can be configured to use the task event loop utilization (ELU) active time instead.

### Weighted round robin

The worker weights are maximum tasks execution time, once the worker has reached its maximum tasks execution time, the next task is assigned to the next worker. The worker default weights are the same for all workers and is computed given the CPU cores speed and theirs numbers.

### Interleaved weighted round robin

The worker weights are maximum tasks execution time. The rounds are the deduplicated worker weights.  
During a round, if worker weight is inferior to the current round, the next task is assigned to the next worker. Once the all workers have been assigned a task, the next round starts.  
The worker default weights are the same for all workers and is computed given the CPU cores speed and theirs numbers. So the default rounds consists of a unique worker weight.

## Statistics

Worker choice strategies enable only the statistics that are needed to choose the next worker to avoid unnecessary overhead.

### Median

Strategies using the average task execution time for each worker can use the median instead. Median is more robust to outliers and can be used to avoid assigning tasks to workers that are currently overloaded. Median usage introduces a small overhead: measurement history must be kept for each worker and the median must be recomputed each time a new task has finished.
Commit	Line	Data
9e2ab226 JB	1	# Worker choice strategies
	2
	3	## Strategies
	4
	5	### Fair share
	6
	7	Its goal is to distribute the load evenly across all workers. To achieve this, the strategy keeps track of the average task execution time for each worker and assigns the next task to the worker with the lowest task end prediction time: `task_end_prediction = max(current_time, task_end_prediction) + average_task_execution_time`.
f1c06930	8	By default, the strategy uses the average task execution time for each worker but it can be configured to use the task event loop utilization (ELU) active time instead.
9e2ab226 JB	9
	10	### Weighted round robin
	11
251f0d71	12	The worker weights are maximum tasks execution time, once the worker has reached its maximum tasks execution time, the next task is assigned to the next worker. The worker default weights are the same for all workers and is computed given the CPU cores speed and theirs numbers.
afe102b2	13
9e2ab226 JB	14	### Interleaved weighted round robin
9e2ab226 JB	15
251f0d71	16	The worker weights are maximum tasks execution time. The rounds are the deduplicated worker weights.
931d43e2	17	During a round, if worker weight is inferior to the current round, the next task is assigned to the next worker. Once the all workers have been assigned a task, the next round starts.
251f0d71 JB	18	The worker default weights are the same for all workers and is computed given the CPU cores speed and theirs numbers. So the default rounds consists of a unique worker weight.
251f0d71 JB	19
9e2ab226 JB	20	## Statistics
9e2ab226 JB	21
afe102b2 JB	22	Worker choice strategies enable only the statistics that are needed to choose the next worker to avoid unnecessary overhead.
afe102b2 JB	23
9e2ab226 JB	24	### Median
	25
	26	Strategies using the average task execution time for each worker can use the median instead. Median is more robust to outliers and can be used to avoid assigning tasks to workers that are currently overloaded. Median usage introduces a small overhead: measurement history must be kept for each worker and the median must be recomputed each time a new task has finished.