Distributed Cost-Optimized Placement for Latency-Critical Applications in Heterogeneous Environments

Mobile Edge Clouds (MECs) with 5G will create new opportunities to develop latency-critical applications in domains such as intelligent transportation systems, process automation, and smart grids. However, it is not clear how one can cost-efficiently deploy and manage a large number of such applications given the heterogeneity of devices, application performance requirements, and workloads. This work explores cost and performance dynamics for IoT applications, and proposes distributed algorithms for automatic deployment of IoT applications in heterogeneous environments. Placement algorithms were evaluated with respect to metrics including number of required runtimes, applications' slowdown, and the number of iterations used to place an application. Iterative search-based distributed algorithms such as Size Interval Actor Assignment in Groups (SIAA_G) outperformed random and bin packing algorithms, and are therefore recommended for this purpose. Size Interval Actor Assignment in Groups at Least Utilized Runtime (SIAA_G_LUR) algorithm is also recommended when minimizing the number of iterations is important. The tradeoff of using SIAA_G algorithms is a few extra runtimes compared to bin packing algorithms.