Cloud-Native Applications' Workload Placement over the Edge-Cloud Continuum

The evolution of virtualization technologies and of distributed computing architectures has inspired the so-called cloud native applications development approach. A cornerstone of this approach is the decomposition of a monolithic application into small and loosely coupled components (i.e., microservices). In this way, application's performance, flexibility, and robustness can be improved. However, most orchestration algorithms assume generic application workloads that cannot serve efficiently the specific requirements posed by the applications, regarding latency and low communication delays between their dependent microservices. In this work, we develop advanced mechanisms for automating the allocation of computing resources, in order to optimize the service of cloud-native applications in a layered edge-cloud continuum. We initially present the Mixed Integer Linear Programming formulation of the problem. As the execution time can be prohibitively large for real-size problems, we develop a fast heuristic algorithm. To efficiently exploit the performance-execution time trade-off, we employ a novel multi-agent Rollout, the simplest and most reliable among the Reinforcement Learning methods, that leverages the heuristic's decisions to further optimize the final solution. We evaluate the results through extensive simulations under various inputs that demonstrate the quality of the generated sub-optimal solutions.