This paper presents a control-theoretic approach for discrete polytopic linear parameter varying (PLPV) systems using event-triggered control (ETC) and self-triggered control (STC) strategies. The approaches guarantee the desired performance while reducing the frequency of controller updates. First, the paper presents an ETC for discrete PLPV systems with exogenous input using a parameter-varying Lyapunov function. The gains of ETC and disturbance attenuation are solved using multi-objective linear matrix inequalities (LMIs). Moreover, to address the issue of periodic sampling and the additional hardware required in ETC, this paper proposes a control-theoretic approach using STC for a discrete PLPV system. Finally, the developed approaches are experimentally validated on a distributed fog computing platform for auto-scaling the compute nodes for mobile robot vision applications.