The increasing reliance upon cloud services entails more flexible networks that are realized by virtualized network equipment and functions. When such advanced network systems face a massive failure by natural disasters or attacks, the recovery of the entire systems may be conducted in a progressive way due to limited repair resources. The prioritization of network equipment in the recovery phase influences the interim computation and communication capability of systems, since the systems are operated under partial functionality. Hence, finding the best recovery order is a critical problem, which is further complicated by virtualization due to dependency among network nodes and layers. This paper deals with a progressive recovery problem under limited resources in networks with VNFs, where some dependent network layers exist. We prove the NP-hardness of the progressive recovery problem and approach the optimum solution by introducing DeepPR, a progressive recovery technique based on deep reinforcement learning. Our simulation results indicate that DeepPR can obtain 98.4 of the theoretical optimum in certain networks. The results suggest the applicability of Deep RL to more general progressive recovery problems and similar intractable resource allocation problems.
The fragility induced by dependency between network layers has been pointed out in the context of interdependent network research @cite_14 @cite_4 @cite_25 . In particular, the interdependency between virtualized nodes and physical nodes in optical networks is considered in @cite_14 . A similar dependency caused by VNF orchestration is discussed in @cite_4 .
@cite_14 @cite_4 @cite_25
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Software-defined networking (SDN) has been proposed as a next-generation control and management framework, facilitating network programmability to address emerging dynamic application requirements. The separation of control and data planes in SDN demands the synergistic operation of the two entities for globally optimized performance. We identify the collaboration of the control plane and the data plane in software-defined optical transmission systems as a cyber-physical interdependency where the "physical" fiber network provides the “cyber” control network with means to distribute control and signaling messages and in turn is itself operated by these "cyber" control messages. We focus on the cyber-physical interdependency in SDN optical transmission from a network robustness perspective and characterize cascading failure behaviors. Our analysis suggests that topological properties pose a significant impact on failure extensibility. We further evaluate the effectiveness of optical layer reconfigurability in improving the resilience of SDN controlled optical transmission systems. The concept of NFV appears to be a promising direction to save cellular network service providers from endlessly increasing capital investment, given the fast evolving mobile broadband communication techniques and unprecedented consumer demand for quality of service and quality of experience in mobile access. Meanwhile, given the deployment of NFV, the virtualized network functions and the physical hardware resources are still vulnerable to natural disasters and malicious attacks. We present in this article the first framework for reliability evaluation of NFV deployment and specific algorithms to efficiently determine the key set of physical or logical nodes there. Modern systems are increasingly dependent upon and interacting with each other, and become interdependent networks. These interdependent networks may exhibit some interesting and even surprising behaviors due to the interdependency and the interplay between the constituent systems. In this article we focus on two important phenomena, namely cascading failure in cyber-physical systems (CPS) and information cascade in coupled social networks. Specifically, cascading failures may occur in CPS that exhibit functional interdependency between two constituent systems (e.g. smart grid); information cascade may happen in multiple social networks that are coupled together by so-called multi-membership individuals. This article explores these two types of cascading effects in interdependent networks by reviewing existing studies in the literature. We review different models in the literature to study the two types of cascading effects in interdependent networks, and highlight the key findings from these studies.