Update automatic-rebalancing.md

docs/architecture/concepts/automatic-rebalancing.md

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 Automatic rebalancing is a fundamental feature of distributed data storage systems designed to maintain an even
-distribution of data across storage nodes. This process ensures optimal performance, prevents resource overutilization,
+distribution of data and performance across storage nodes. This process ensures optimal performance, prevents resource under-utilization,
 and enhances system resilience by dynamically redistributing data in response to changes in cluster topology or workload
 patterns.
 
-In a distributed storage system, data is typically spread across multiple storage nodes for redundancy, scalability, and
-performance. Over time, various factors can lead to an imbalance in data distribution, such as:
+In Simplyblock, re-balancing concerns the re-balancing of back storage (for rebalancing of front storage or volume "docking points", see 
+[volume migration](volume-migration.md) for details). It's behaviour depends on weather local node affinity is turned on.
 
-- The addition of new storage nodes, which initially lack any data.
-- The removal or failure of existing nodes, requiring data redistribution to maintain availability.
-- The equal distribution of data across storage nodes.
+If local node affinity is turned on, primary data chunks are re-balanced with a preference to the node at which also the front storage resides.
+If local node affinity is turned off, all data and parity chunks are distributed with the target to achieve absolute equal (in a relative sense) utilization
+levels across all NVMe devices in the cluster under consideration of failure domains and data protection rules.
 
-Automatic rebalancing addresses these issues by dynamically redistributing data across the cluster. This process is
-driven by an algorithm that continuously monitors data distribution and redistributes data when imbalances are detected.
-The goal is to achieve uniform data placement while minimizing performance overhead during the rebalancing process.
+Data re-balancing uses three important principles:
+- always try to move the longest contigous segments of data to minimize random-access IOPS
+- do not use more than 20% of the cluster performance capacity, this is guaranteed by internal QoS
+- use maximum parallelism (during migration, load all devices in the cluster equally to maximize migration speed within the 20%)
+
+Rebalancing is used in the following scenarios:
+
+- After temporary node outages (planned or unplanned)
+- When expanding the cluster (adding nodes / devices)
+- When "failing" devices or entire nodes (rebuild to restablish full redundancy without the devices)