Web Computing is a variant of parallel computing where the idle times of PCs
donated by worldwide distributed users are employed to execute parallel programs.
The PUB-Web library developed by us supports this kind of usage of computing
resources. A major problem for the efficient execution of such parallel programs
is load balancing. In the Web Computing context, this problem becomes more
difficult because of the dynamic behavior of the underlying "parallel computer":
the set of available processors (donated PCs) as well as their availability
(idle times) change over time in an unpredictable fashion. 

In this paper, we experimentally evaluate and compare load balancing algorithms
in this scenario, namely a variant of the well-established Work Stealing algorithm
and strategies based on a heterogeneous version of distributed hash-tables (DHHTs)
introduced recently. In order to run a meaningful experimental evaluation, we
employ, in addition to our Web Computing library PUB-Web, realistic data sets for
the job input streams and for the dynamics of the availability of the resources.

Our experimental evaluations suggest that Work Stealing is the better strategy
if the number of processes ready to run matches the number of available processors.
But a suitable variant of DHHTs outperforms Work Stealing if there are significantly
more processes ready to run than available processors.