A Robust Game Approach for On Spot Price Cloud Markets in Microservice-Based Applications

The evolution and widespread adoption of virtualization, service-oriented architectures, autonomic computing, and utility computing have converged, giving rise to Cloud Computing, which enables the on-demand delivery of software, hardware, and data as services. As Cloud-based services become more numerous and dynamic, developing efficient service provisioning policies is increasingly challenging due to the impact of estimation errors in the operating parameters of applications such as execution time or request rate for each application and for each service. In this paper, we take the perspective of Software as a Service (SaaS) providers which host their applications at an Infrastructure as a Service (IaaS) provider. Each SaaS needs to comply with quality of service requirements, specified in Service Level Agreement (SLA) contracts with the end-users, which determine the revenues and penalties based on the achieved performance level. SaaS providers should maximize their revenue and minimize their cost by deciding how many VMs for each class (reserved, on-demand or spot) are to be used, while IaaS must decide the price of VMs in order to maximize his revenue. The problem is modeled using a Generalized Nash Equilibrium Problem (GNEP). The uncertainty in the application parameters, that are not known until run time, is specifically addressed in this paper using a cardinality-constrained robust optimization approach to formulate a robust version of the SaaS problem. Furthermore, we propose a best-reply algorithm to identify a robust GNE, ensuring that the solution remains feasible even under the worst-case scenarios of uncertain parameters. An analytical model is developed to derive the equilibrium. For validation, the analytical model results are compared with simulations that reflect real situations, characterized by more realistic parameter distributions and limited buffer capacities.