Scheduling in data gathering networks with background communications

In this work, we study scheduling in star data gathering networks with background communications. The worker nodes of the network hold datasets that have to be transferred directly to the base station. The communication speed of each link changes with time, due to other applications using the network, independently of the speeds of other links. Our goal is to gather all data within the minimum time. We show that while the preemptive version of this problem can be solved in polynomial time, the non-preemptive variant is strongly NP-hard. We propose for it an exact exponential algorithm based on dynamic programming, several polynomial time heuristics and local search algorithms. Theoretical performance guarantees and the existence of dominance relations between the heuristics are studied. The performance of the proposed algorithms is also tested in a series of computational experiments.