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• W3091858434 startingPage "2275" @default.
• W3091858434 abstract "In network function virtualization, a service function chain (SFC) specifies a sequence of virtual network functions that user traffic has to traverse to realize a network service. The problem of SFC orchestration has been extensively studied in the literature. However, most existing works assume deterministic demands and resort to costly runtime resource reprovisioning to deal with dynamic demands. In this work, we formulate the deadline-aware co-located and geo-distributed SFC orchestration with demand uncertainty as robust optimization problems and develop exact and approximate algorithms to solve them. A key feature of our formulation is the consideration of end-to-end delay in service chains by carefully modeling load-independent propagation delay as well as load-dependent queueing and processing delays. To avoid frequent resource reprovisioning, our algorithms utilize uncertain demand knowledge to compute proactive SFC orchestrations that can withstand fluctuations in dynamic service demands. Extensive simulations are conducted to evaluate the performance of our algorithms in terms of ability to cope with demand fluctuations, scalability, and relative performance against other recent algorithms." @default.
• W3091858434 created "2020-10-15" @default.
• W3091858434 creator A5037170357 @default.
• W3091858434 creator A5052330252 @default.
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• W3091858434 date "2020-12-01" @default.
• W3091858434 modified "2023-10-11" @default.
• W3091858434 title "Deadline-Aware SFC Orchestration Under Demand Uncertainty" @default.
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• W3091858434 doi "https://doi.org/10.1109/tnsm.2020.3029749" @default.
• W3091858434 hasPublicationYear "2020" @default.
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