A Pareto-Efficient Algorithm for Data Stream Processing at Network Edges

Data stream processing has received considerable attention from both research community and industry over the last years. Since latency is a key issue in data stream processing environments, the majority of the works existing in the literature focus on minimizing the latency experienced by the users. The aforementioned minimization takes place by assigning the data stream processing components close to data sources. Server consolidation is also a key issue for drastically reducing energy consumption in computing systems. Unfortunately, energy consumption and latency are two objective functions that may be in conflict with each other. Therefore, when the target function is to minimize energy consumption, the delay experienced by users may be considerable high, and the opposite. For the above reason there is a dire need to design strategies such that by targeting the minimization of energy consumption, there is a graceful degradation in latency, as well as the opposite. To achieve the above, we propose a Pareto-efficient algorithm that tackles the problem of data processing tasks placement simultaneously in both dimensions regarding the energy consumption and latency. The proposed algorithm outputs a set of solutions that are not dominated by any solution within the set regarding energy consumption and latency. The experimental results show that the proposed approach is superior against single-solution approaches because by targeting one objective function the other one can be gracefully degraded by choosing the appropriate solution.