Decomposition Theory Meets Reliability Analysis: Processing of Computation-Intensive Dependent Tasks Over Vehicular Clouds With Dynamic Resources

Vehicular cloud (VC) is a promising technology for processing computation-intensive applications (CI-Apps) on smart vehicles. Implementing VCs over the network edge faces two key challenges: (C1) On-board computing resources of single vehicle are often insufficient to process CI-App; (C2) The dynamics available resources, caused by vehicles’ mobility, hinder reliable CI-App processing. This work among first jointly address and (C2), while considering common graph representations, directed acyclic (DAG) undirected (UG). To (C1), we consider partitioning with $m$ dependent (sub-)tasks into notation="LaTeX">$k\le m$ groups, which dispersed across introduce generalized reliability metric called conditional mean time failure (C-MTTF). Subsequently, increase C-MTTF sub-tasks via introducing general framework redundancy-based semi-dynamic ( RP-VC). We demonstrate that RP-VC can be modeled as non-trivial semi-Markov (SMP). analyze this SMP model its reliability, develop novel mathematical framework, xmlns:xlink="http://www.w3.org/1999/xlink">event stochastic algebra notation="LaTeX">$\langle e\rangle$ -algebra). Based -algebra, propose xmlns:xlink="http://www.w3.org/1999/xlink">decomposition theorem (DT) transform presented decomposed (D-SMP). subsequently calculate our methodology. -algebra DT tools used other cloud-based networks. Simulation results reveal exactness analytical efficiency methodology in terms acceptance success rates