Risk-Aware Social Cloud Computing Based on Serverless Computing Model

In this paper, a flexible resource sharing paradigm is introduced, to enable the allocation of users' computing tasks in a social cloud computing system offering both Virtual Machines (VMs) and Serverless Computing (SC) functions. VMs are treated as a safe computing resource, while SC due to the uncertainty introduced by its shared nature, is treated as a common pool resource, being susceptible to potential over-exploitation. These computing options are differentiated based on the potential satisfaction perceived by the user, as well as their corresponding pricing, while taking into account the social interactions among the users. Considering the inherent uncertainty of the considered computing environment, Prospect Theory and the theory of the Tragedy of the Commons are adopted to properly reflect the users' behavioral characteristics, i.e., gain-seeking or loss-averse behavior, as well as to formulate appropriate prospect- theoretic utility functions, embodying the social- aware and risk-aware user's perceived satisfaction. A distributed maximization problem of each user's expected prospect-theoretic utility is formulated as a non-cooperative game among the users and the corresponding Pure Nash Equilibrium (PNE), i.e., optimal computing jobs offloading to the VMs and the SC, is determined, while a distributed low-complexity algorithm that converges to the PNE is introduced. The performance and key principles of the proposed framework are demonstrated through modeling and simulation.