|Abstract of Paper|
Scheduling and Traffic Allocation for Tasks with Bounded Splittability
by Piotr Krysta and Peter Sanders and Berthold Voecking
We investigate variants of the well studied problem of scheduling tasks on uniformly related machines to minimize the makespan. In the $k$-splittable scheduling problem each task can be broken into at most $k \ge 2$ pieces each of which has to be assigned to a different machine. In the slightly more general SAC problem each task $j$ comes with its own splittability parameter $k_j$, where we assume $k_j \ge 2$. These problems are known to be NP-hard and, hence, previous research mainly focuses on approximation algorithms. Our motivation to study these scheduling problems is traffic allocation for server farms based on a variant of the Internet Domain Name Service (DNS) that uses a stochastic splitting of request streams. Optimal solutions for the $k$-splittable scheduling problem yield optimal solutions for this traffic allocation problem. Approximation ratios, however, do not translate from one problem to the other because of non-linear latency functions. In fact, we can prove that the traffic allocation problem with standard latency functions from Queueing Theory cannot be approximated in polynomial time within any finite factor because of the extreme behavior of these functions. Because of the inapproximability, we turn our attention to fixed-parameter tractable algorithms. Our main result is a polynomial time algorithm computing an exact solution for the $k$-splittable scheduling problem as well as the SAC problem for any fixed number of machines. The running time of our algorithm increases exponentially with the number of machines but is only linear in the number of tasks. This result is the first proof that bounded splittability reduces the complexity of scheduling as the unsplittable scheduling is known to be NP-hard already for two machines. Furthermore, since our algorithm solves the scheduling problem exactly, it also solves the traffic allocation problem that motivated our study.