DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

to machines (network topology) and a sequence of components for each machine (resource
allocation). However, our problem is different especially because a component in our networks
can have multiple tasks to process, i.e., a component can process tasks in parallel with its
successors or predecessors. The easiest multiprocessor scheduling problem is when components
are independent, i.e., there is no task flow between components. However, this problem is known
as NP-complete [14][15]. Considering that the task flow structure of our networks is arbitrary
and each component can have multiple tasks to process, our scheduling problem is even harder.
In this context, the method designed in this paper is a heuristic which is applicable to the
cases where the number of tasks to be processed by each component is large. Though the
increase of the number of tasks adds more complexity, it can give us great opportunity to
develop an efficient heuristic. Also, our method addresses resource reservation. When different
applications share resources together, their performance can be guaranteed through the resource
reservation. The method quantifies the minimal completion time by incorporating the resource
reservations of other applications and also enables to make the resource reservations for the
service network under consideration.
The organization of this paper is as follows. In section 2 we formally define the problem in
detail. After designing the method in Sections 3, we show empirical results in Section 4. Finally,
we discuss implications and possible extensions of our work in Section 5.
2. Problem statement
In this section we formally define the problem by detailing component-based service network,
network topology, and resource allocation. We focus on computational CPU resources assuming
that the system is computation-bounded.
4