Abstract

Fault-Prone Mobile Edge Cloud (FP-MEC) is a new type of distributed network composed of mobile edge computing and network function virtualization, where virtual network services can be provided in the form of service function chains (SFCs) that are a sequence of virtual network functions (VNFs) on-demand deployed on resource-limited edge servers. FP-MEC has a characteristic that the fault probability of each VNF is dynamic and fluctuates with time and workloads, making SFCs temporarily unreliable. To increase the reliabilities, redundant Backup VNFs (BVNFs) need to be deployed near the VNFs and activated when they experience faults. Different mobile users would request different SFCs with reliability and service time demands to process their data. However, workloads of VNFs are dynamic and unpredictable in FP-MEC due to random arrival of user requests. How to optimally deploy VNFs and corresponding BVNFs on a set of edge servers to form expected SFCs that have higher reliabilities than user demand values, meanwhile throughput of receiving requests is maximized while receiving cost is minimized in real-time, is a challenging problem. The receiving cost is composed of deployment cost of instantiating VNFs and BVNFs, and communication cost of routing data among users, VNFs and BVNFs. In this paper, the long-term provisioning problem is first formulated as an integer linear program and proved to be NP-hard. Then, it is discretized into a sequence of one-slot optimization problems to handle practical time-varying fault probability, where a set of SFC requests are given at each time slot, and receiving or rejecting decisions are executed immediately without any future information. Finally, an online approximation scheme with a constant approximation ratio is proposed to solve the one-slot problems in polynomial time. Theoretical analyses and experiments based on real network topology of CERNET in China demonstrate that the scheme is promising compared to existing works.

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