Abstract
Cloud-computing services are provided to consumers through a network of servers and network equipment. Cloud-network (CN) providers virtualize resources [e.g., virtual machine (VM) and virtual network (VN)] for efficient and secure resource allocation. Disasters are one of the worst threats for CNs as they can cause massive disruptions and CN disconnection. A disaster may also induce post-disaster correlated, cascading failures which can disconnect more CNs. Survivable virtual-network embedding (SVNE) approaches have been studied to protect VNs against single physical-link/-node and dual physical-link failures in communication infrastructure, but massive disruptions due to a disaster and their consequences can make SVNE approaches insufficient to guarantee cloud-computing survivability. In this work, we study the problem of survivable CN mapping from disaster. We consider risk assessment, VM backup location, and post-disaster survivability to reduce the risk of failure and probability of CN disconnection and the penalty paid by operators due to loss of capacity. We formulate the proposed approach as an integer linear program and study two scenarios: a natural disaster, e.g., earthquake and a human-made disaster, e.g., weapons-of-mass-destruction attack. Our illustrative examples show that our approach reduces the risk of CN disconnection and penalty up to 90 % compared with a baseline CN mapping approach and increases the CN survivability up to 100 % in both scenarios.
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Notes
Since we are using a probabilistic model, this variable only indicates if a cloud network can be affected by a disaster or not. The actual probability of disconnection will depend on the disaster intensity.
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This work has been supported by Defense Threat Reduction Agency (DTRA) Grant No. HDTRA1-10-1-0011. A preliminary version of this work was presented in [1].
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Colman-Meixner, C., Dikbiyik, F., Habib, M.F. et al. Disaster-survivable cloud-network mapping. Photon Netw Commun 27, 141–153 (2014). https://doi.org/10.1007/s11107-014-0434-6
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DOI: https://doi.org/10.1007/s11107-014-0434-6