From: Distributed systems

Load-balancing policy falls into two broad groups: static and dynamic.
Static policies use algorithms which operate without regard to
run-time loads across a system, while dynamic policies use the
run-time performance of various parts of a system in order to make
more `informed' decisions about balancing.

[92-11-06-12-53.57] A dynamic load-balancing policy is one which uses
run-time state information in making scheduling decisions.

There are two kinds of dynamic policies: adaptive and non-adaptive.
The latter always use the same (fixed, load-dependent) policy; the
former may adjust policy parameters in order to gradually improve
their performance.

The key point is that while non-adaptive policies use only the
information about the run-time state, adaptive policies use, in
addition to this, information about current performance.

In adaptive policies, the rules for adjusting policy parameters may be
static or dynamic.  An example of the former might be: `shift to a
conservative migration rule when system-wide load patterns are varying
too rapidly'.  An example of the latter could be: `increase
sender-side threshold when migrated jobs cause slowdown rather than
speedup'.  Some researchers refer to the performance-driven adaptation
exhibited by the second policy as `learning'.

Since both non-adaptive policies and adaptive policies with static
rules really use only load information, it is confusing to distinguish
between them.  One way to avoid such confusion is to restrict the use
of the word `adaptive' to policies that use performance feedback in
order to drive their adjustment of policy parameters.