  Virtual network functions (VNFs) have been widely deployed in mobile edge
computing (MEC) to flexibly and efficiently serve end users running
resource-intensive applications, which can be further serialized to form
service function chains (SFCs), providing customized networking services. To
ensure the availability of SFCs, it turns out to be effective to place
redundant SFC backups at the edge for quickly recovering from any failures. The
existing research largely overlooks the influences of SFC popularity, backup
completeness and failure rate on the optimal deployment of SFC backups on edge
servers. In this paper, we comprehensively consider from the perspectives of
both the end users and edge system to backup SFCs for providing popular
services with the lowest latency. To overcome the challenges resulted from
unknown SFC popularity and failure rate, as well as the known system parameter
constraints, we take advantage of the online bandit learning technique to cope
with the uncertainty issue. Combining the Prim-inspired method with the greedy
strategy, we propose a Real-Time Selection and Deployment(RTSD) algorithm.
Extensive simulation experiments are conducted to demonstrate the superiority
of our proposed algorithms.
