3.1 MB - Evernote

14
Fundamental Concepts
event execution in some LPs can lead to frequent and cascaded rollbacks and result in a
degradation of the effective processing rate of events. Therefore research has focused on
combining the advantages of conservative and optimistic algorithms creating so-called
hybrid algorithms and on controlling the optimism in Time Warp. Such attempts to limit
the optimism in Time Warp can be grouped into non-adaptive algorithms, adaptive
algorithms with local state and adaptive algorithms with global state. Carl Tropper [34]
and Samir R. Das [5] both give a good overview on algorithms in these categories.
The group of non-adaptive algorithms for instance contains algorithms that use time
windows in order to limit how far ahead of the current GVT single LPs can process their
events, which limits the frequency and length of rollbacks. Other algorithms in this
group add conservative ideas to Time Warp. One example for this is the Breathing Time
Buckets algorithm (also known as SPEEDES algorithm) [30]. Like Time Warp this
algorithm executes all local events immediately and performs local rollbacks if required
but it only sends events to other LPs that have been guaranteed by the current GVT and
by doing so avoids cascaded rollbacks. The problem of all these algorithms is that either
the effectiveness depends on finding the optimum value for static parameters like the
window size or conservative aspects of the algorithm limit its effectiveness for models
with certain characteristics. Finding the optimum value for such control parameters can
be difficult for simulation modellers and many simulation models show a very dynamic
behaviour of their characteristics, which would require different parameters at different
times of the simulation.
Adaptive algorithms solve this problem by dynamically adapting the control parameters
of the synchronisation algorithm according to “selected aspects of the state of the
simulation” [24]. Some of these algorithms use mainly global state information like the
Adaptive Memory Management algorithm [6], which uses the total amount of memory
used by all LPs or the Near Perfect State Information algorithms [28] that are based on
the availability of a reduced information set that almost perfectly describes the current
global state of the simulation. Adaptive algorithms based on local state use only local
information available to each LP in order to change the control parameters. They collect
historic local state information and from these try to predict future local states and the
required control parameter. Some examples for adaptive algorithms using local state