Multi-Objective Optimization of Application Deployment Strategies in Integrated Cloud-Fog Computing Environments

In the domain of Cloud computing, Fog computing is integrated with the Cloud to offer a balanced approach that combines Cloud's scalability with Fog's low latency, enabling efficient software application deployment. However, many current studies overlook the unpredictability of future user requests, such as assuming all requests are known beforehand. User requests often arrive dynamically and may have different quality of service (QoS) preferences. Therefore we need effective methods to handle dynamic application deployment with multiple objectives. This paper tackles this gap by modeling a multi-objective application deployment problem that considers dynamically arriving users' requests on application deployment in a Cloud-Fog environment. We further introduce a multi-objective Genetic Programming Hyper-Heuristic based approach to automatically generate a set of deployment rules that can be chosen according to users' QoS preferences. These rules are generated with different trade-offs of two optimization objectives, i.e., minimizing cost and latency, which can be used for deploying applications dynamically. Our experimental evaluation using real-world data demonstrates that our GPHH approach can generate effective heuristics for deploying applications in an integrated Cloud-Fog environment.