A Petri Net-based framework for modeling and simulation of resource scheduling policies in Edge Cloud Continuum

Abstract

The emergence of new technologies of 5G/6G networks and the Internet of Things (IoT) drives the transition from traditional Cloud Computing systems to the Edge Cloud Continuum — an interconnected distributed computing environment. Deploying modern applications in such a complex setting poses significant challenges for efficient dynamic resource management. Besides their several benefits, current orchestration platforms disregard aspects such as the dynamic behavior of applications’ demands, heterogeneity of the infrastructure’s resources, and the overall complexity when dealing with interdependent resource allocation decision parameters. The Digital Twin concept envisions assisting application deployments not only by providing offline simulations for experimental assessment in multi-cluster settings but also by actively guiding the orchestration process. In this paper, we aim to provide a modeling and simulation framework to optimize the performance of the underlying infrastructure in terms of resilience and sustainability. We investigate the application of automata theory to model such systems by analyzing their possible states, specifically, using Petri Nets, a mathematical framework for representing discrete event systems, as the primary modeling tool. Therefore, a comprehensive modeling approach is presented to simulate the resource scheduling decisions of an established multi-cluster framework, namely Karmada. Moreover, through this Petri Net modeling approach, we can efficiently optimize the performance of the orchestration process considering the power consumption and workload load balancing of a multi-cluster topology. Extensive evaluation indicates the efficacy of the proposed framework in accurately approximating Karmada’s behavior for various scheduling policies. Also, the proposed framework is capable of assessing the performance of several scheduling policies and guiding the system towards efficient resource management in complex scenarios, exploiting the polynomial complexity of the Petri Net to identify scheduling states.