Efficient Operator Placement for Distributed Data Stream Processing Applications

In the last few years, a large number of real-time analytics applications rely on the Data Stream Processing (DSP) so to extract, in a timely manner, valuable information from distributed sources. Moreover, to efficiently handle the increasing amount of data, recent trends exploit the emerging presence of edge/Fog computing resources so to decentralize the execution of DSP applications. Since determining the Optimal DSP Placement (for short, ODP) is an NP-hard problem, we need efficient heuristics that can identify a good application placement on the computing infrastructure in a feasible amount of time, even for large problem instances. In this paper, we present several DSP placement heuristics that consider the heterogeneity of computing and network resources; we divide them in two main groups: model-based and model-free. The former employ different strategies for efficiently solving the ODP model. The latter implement, for the problem at hand, some of the well-known meta-heuristics, namely greedy first-fit, local search, and tabu search. By leveraging on ODP, we conduct a thorough experimental evaluation, aimed to assess the heuristics' efficiency and efficacy under different configurations of infrastructure size, application topology, and optimization objectives.