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


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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.
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Key words
Cloud computing,multi-layer cloud computing,workload scheduling,task offloading,service reconfiguration,Lyapunov optimization
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