Orquestração de recursos para a oferta de serviços, em infraestruturas híbridas de computação de borda e nuvem, com foco em aplicações de realidade mista

Abstract

Efficient resource allocation in hybrid edge-cloud computing environments is becoming increasingly important due to the growing adoption of mixed reality applications and the widespread use of devices with limited energy, processing, and memory resources. A welldesigned allocation strategy not only ensures compliance with the quality of service (QoS) requirements of these applications but also promotes optimized use of computational and network infrastructure, resulting in lower operational costs. In this work, we propose a model based on Integer Linear Programming (ILP) aimed at maximizing the fulfillment of demand generated by user devices, while minimizing the cost associated with the use of virtual machines responsible for processing. We evaluate the complexity of the model and propose structural simplifications, in addition to developing a heuristic designed to reduce solution generation time. Finally, we introduce a proactive approach based on a predictive model that anticipates resource usage patterns, contributing to more accurate decisions compared to reactive strategies. Experimental results demonstrate significant improvements in the volume of demand served when compared to other approaches in the literature, as well as highlight the benefits of adopting proactive strategies for resource allocation.