docs: refine README

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

diff --git a/src/pools/selection-strategies/README.md b/src/pools/selection-strategies/README.md
index 9e409cdf4207d9b7d13acfeebd5750cde5de6174..6796a12f9f85a1f5d2261ef4915f995989cfad3a 100644 (file)
--- a/src/pools/selection-strategies/README.md
+++ b/src/pools/selection-strategies/README.md
@@ -5,14 +5,18 @@
 ### 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 event loop utilization (ELU) active time instead.
+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 default worker weights are the same for all workers and is computed given the CPU cores speed and theirs numbers.
+
 ### Interleaved weighted round robin
 
 ## 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.