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An increasing number of cloud services are operated globally, where the service data are frequently replicated across geographically distributed datacenters to improve service quality and reliability. Such replication generates many one-to-many bulk data transfers over inter-datacenter networks from one datacenter to many receiver datacenters. To provide end-users with guaranteed services, these data transfers are usually required to be completed within designated deadlines. Despite the exponential growth in data demand, there has been little work on guaranteeing deadlines for one-to-many transfers, which is the subject of this paper. This paper proposes a centralized admission control coupled with a scheduling algorithm, named deAdline-Guaranteed transfEr (AGE), to guarantee the deadline of admitted data transfers and utilize the network capacity efficiently. The key idea is to flexibly select the source datacenter for receiver datacenters and allow the remaining receivers to obtain a replica from either the original source or the other receivers that have already received a copy. By jointly allocating the source for receivers and the bandwidth and routing paths for every data transfer, AGE maximizes the number of deadline-satisfied transfers. Our simulations show that compared to the state-of-the-art, AGE guarantees the deadline for up to 70% more transfers, achieves at least 2× higher network throughput, and reduces the completion time up to 80%.

NFV is promising to lower the network operator’s capital expenditure and operational expenditure by replacing proprietary hardware-based network equipment with software-based VNFs that can be consolidated into telecom clouds. In particular, NFV provides an efficient way to deploy network services using SFCs that consist of a set of VNFs interconnected by virtual links. A practical but theoretically challenging problem related to NFV management and orchestration is how to jointly optimize the topology design and mapping of multiple SFCs such that the TBC is minimized, which is called the JTDM problem. In this article, we propose a novel heuristic algorithm, Closed-Loop with Critical Mapping Feedback, to efficiently address the JTDM problem. While minimizing the TBC, we also propose scalable and reliable JTDM strategies that can significantly reduce the network reconfigurations and enhance the service reliability, respectively.