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STAGE 2 routing path

possible path

Cell incarnations

process process
2,1 2,n(2)

Figure 14.6 Logical interconnection between levels of the parallel simulator.


the null-messages paradigm, whereas communications within Stage 2 are based on an op-
timistic scheme [30].
The implementation is based on an object-oriented methodology using C++ as the pro-
gramming language, which makes it easy to maintain and flexible to any changes. Every
MH object is constructed as a base class with parameters that are generalized to all mobile
hosts. The four classes of mobiles (workers, static users, wanderers, and travelers) are de-
rived from the base MH class. MH objects are created in Stage 1 container processes,
whereas cell objects are created in Stage 2 container processes.
Three levels in the SWiMNet simulator have been defined, in which logical activities
of objects are elaborated:

1. Movement precomputation (Level 1) is composed of Nmb mobile host incarnations.
2. Event sorting (Level 2) is composed of Ncells event sorters.
3. Channel allocation simulation (Level 3) is composed of Ncells cell incarnations.

The communications within and between the two stages are shown in Figure 14.6.
Every MH incarnation process generates events for each MH it maintains. Then, those
events are sent to the event sorter process (ES) for the cell where the event takes place.
The MH incarnations are independent of each other. Thus, activities of Level 1 are com-
pletely parallelizable. Similarly, event sorters are independent of each other. However, cell
incarnations, where channel management simulation takes place, are mutually dependent.
In SWiMNet, the optimism lies in the following: every time a move-in event is simulat-
ed at any cell incarnation, the latter optimistically assumes that the call is still on, unless it
already received notification that the call was blocked by means of a call blocked mes-
sage. In the case in which a late notification is received, that is, a call blocked message is
received after the corresponding move-in event has been simulated, a rollback is per-
formed that retracts and corrects the simulations that follow and include the move-in
event. A call blocked message is sent by any cell incarnation whenever a move-out event
is simulated, if the simulation of the corresponding channel request event in the event cou-
ple did not actually result in a channel being allocated. This implies that from Level 1, it is
necessary to keep track of at least the event that follows every move-out event in the se-
quence of events for the same mobile host. This information may then be used to con-
struct a call blocked message.
Rolling back the computation might result in the need for retracting an incorrectly sent
call blocked message by means of a call allocated message. For instance, let us assume
that a call was notified as blocked because of channel unavailability after a channel was
allocated to another call. If the allocated call turns out to be already blocked in a previous
cell, then the call that was notified as blocked can indeed be allocated. A call blocked and
a call allocated message relative to the same call correspond to a pair of antimessages in
Time Warp simulations [30, 31], hence a sign can be associated with these messages. In a
Time Warp simulator, a call blocked message has a positive sign, whereas a call allocated
message has a negative sign.
Due to the optimistic assumption, the elaboration of a simulation message whose cor-
responding precomputed event was already elaborated always causes a rollback. However,
the way in which data are stored in the simulator allows rollbacks to be optimized, i.e.,
only a portion of precomputed events need to be involved in the rollback, and such a por-
tion is exactly computed without the need to inspect any additional event.

The simulator is automatically generated by the master process, which reads the de-
scription file of the simulation model and partitions the model into two stages. The de-
scription file includes the mobility model, the call arrival model, the cellular system map,
the system architecture, the experiment seed, and the simulation time of termination. The
main tasks of the master are (1) generating parameters for every single logical entity, i.e.,
positions, speeds, movement times, etc. for each mobile host incarnation, according to the
general description of the mobility model; and (2) deciding the number of processes per
stage and mapping the processes to processors. Process mapping is an important factor in
improving the efficiency of parallel simulation protocols. Currently, a simple static map-
ping discipline has been adopted: given a number of processors allocated to the simula-
tion, half of the processors are used for Stage 1 and half for Stage 2.
Performance analysis of SWiMNet applied to wireless and mobile system can be found
in [7, 9]. Further work is underway to evaluate its performance for mobile ad hoc networks.


This chapter focuses on several challenging design and modeling aspects of wireless, mo-
bile, and ad hoc networks. We presented a discussion of modeling issues related to physi-
cal transmission and interference, topology, mobility, workload, and performance figures
for mobile ad hoc networks simulation.
Modeling and simulation are traditional methods to evaluate large-scale wireless and
multihop network designs. However, modeling is often intractable with today™s large and
complex mobility and traffic patterns in wireless and multihop systems. Thus, researchers
have turned increasingly to the use of simulation studies of these systems. Though, de-
tailed simulations of large-scale, wireless, mobile, and ad hoc networks require enormous
execution time and large amounts of memory due to the complexity involved in the simu-
lation and mobility models. Even on high-performance workstations, the execution time is
in the order of days and memory requirements on the order of gigabytes, which impose
restrictions on the type of systems that can be simulated. Parallel and distributed simula-
tion (PDES) could be exploited to overcome these problems.
In this chapter, both sequential and parallel simulation tools for wireless mobile and ad
hoc networks have been reviewed. We have also presented some recent examples of simu-
lation methodologies to improve the simulation run time of these networks using PDES


This work was supported by the Canada Research Program and Canada Foundation for In-
novation (AB), and the Italian MIUR FIRB-PERF “Performance Evaluation of Complex
Systems: Techniques, Methodologies and Tools” project funds (LB).


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