  In this paper we analyze the problem of optimal task scheduling for data
centers. Given the available resources and tasks, we propose a fast distributed
iterative algorithm which operates over a large scale network of nodes and
allows each of the interconnected nodes to reach agreement to an optimal
solution in a finite number of time steps. More specifically, the algorithm (i)
is guaranteed to converge to the exact optimal scheduling plan in a finite
number of time steps and, (ii) once the goal of task scheduling is achieved, it
exhibits distributed stopping capabilities (i.e., it allows the nodes to
distributely determine whether they can terminate the operation of the
algorithm). Furthermore, the proposed algorithm operates exclusively with
quantized values (i.e., the information stored, processed and exchanged between
neighboring agents is subject to deterministic uniform quantization) and relies
on event-driven updates (e.g., to reduce energy consumption, communication
bandwidth, network congestion, and/or processor usage). We also provide
examples to illustrate the operation, performance, and potential advantages of
the proposed algorithm. Finally, by using extensive empirical evaluations
through simulations we show that the proposed algorithm exhibits
state-of-the-art performance.
