Task Offloading Control and Customized Workload Scheduling in Multi-Layer Cloud Networks

Recent advances in Cloud Computing have shown great power in enhancing intelligent devices to support various applications. Nevertheless, conventional Cloud Computing fails to keep up with the ever-advancing requirements of efficient task execution, mainly resulting from its drawbacks in communication delay. To this end, multi-layer cloud computing with local, edge, and remote data centers has gained high interest yet remains challenging because of the inherent complexity of cross-layer orchestration. In particular, with more participants involved, it is nontrivial to achieve customized service provision while guaranteeing system stability. Hence, we address the workload scheduling issue in the multi-layer cloud paradigm in this paper, with task offloading and service reconfiguration considered jointly. We first formulate it as a stochastic optimization problem, where statistical service requirements are imposed on queue lengths. Then, we divide the original optimization into three individual low-complex sub-problems with optimal solutions provided. To improve system performance, we introduce a request-rejecting mechanism that augments our approach with delay-optimality. Theoretical analysis confirms that our approaches can guarantee system stability and are asymptotically optimal within a small gap from the optimum. Finally, we validate the efficiency of our approaches through extensive simulation results in performance guarantees and customized workload scheduling.