Federate Migration Decision-Making Methods for HLA-Based Distributed Simulations

HLA-based distributed simulations tend to suffer from load imbalances and degradation in performance as a result of running on distributed environment. High-Level Architecture (HLA) is a general purpose framework that eases the implementation of distributed simulations on top of dedicated resources without worrying about the computing infrastructure. Due to the high cost of hardware and other factors, some companies have ditched the concept of dedicated resources and shifted towards shared ones which revealed some HLA weaknesses, out of which, dynamic reaction to load imbalances and managing federates on the shared resources. Therefore, different efforts have proposed numerous dynamic load balancing systems to offer a balancing feature to running distributed simulations. In order to perform the load balancing task, these proposed systems gather and make use of a number of simulation and load metrics. Load prediction is a metric that is computed to provide load projections and prevent any prospective load imbalances by migrating federates from an overloaded shared resource to an underloaded shared resource. This work touches the federate migration decision-making process, which is the last step of the balancing task. The proposed federate migration decision-making methods are to overcome the dependency on predefined thresholds in previous work and offer dynamic decisions to migrate federates.