# A Systematic Literature Review of Reliable Provisioning for Virtual Network Function Chaining
## 4 Results of the Systematic Literature Review
### Different Strategies Used for Reliable Service Function
#### 4.1.1 Reliable Provisioning by a Protection Scheme or Recovery Plans  
Reliable service provisioning can be provided in various ways. Generally, existing work can be categorized into two groups: ones that increase the service availability by protection schemes to decrease the failure in the network and the ones that have recovery plans to cause the service to be available with no or less failure time. Most of the existing literature introduces backup models to provide reliable provisioning.  
In [18], an availability model is introduced that considers both physical devices and VNF failures when evaluating the SFC availability. On the other hand, Joint Path-VNF (JPV), which considers both path backup and VNF backup is proposed. The availability model is used with the defined backup model, Joint Path-VNF (JPV), which, along with the Affinity-Based Algorithm (ABA), decreases the physical link consumption during VNF mapping and improves the availability. A protection mechanism is also proposed in [37] that combines VNF replicas and backup path protection for SFC availability improvement. In addition, [38] introduced another joint protection scheme that focuses on path backup and uses one physical node for the two physical nodes' backup. Different protection schemes can be followed to avoid the failure of the SC [22]. Three different protection schemes (Virtual-Node protection, Virtual-Link protection, and End-to-End protection) are introduced in [22] to discard the SC failure. The proposed heuristic algorithm for each of them considers the dynamic provisioning of the SC to address the issue. The simulation results confirm the priority of the End-to-End approach in latency requirement while the Virtual Link protection algorithm requires less network and computational resources [22]. A deep reinforcement learning (DRL)-based online SFC placement approach called DDQP (Double Deep Q-networks Placement) is proposed to provide a fault-tolerance VNF chain placement [25]. The DDQP allows deploying active (main SC) and standby (backup) instances in real-time to increase the model's fault tolerance. The main purpose of the algorithm is to ensure service reliability while managing resource usage for various requests. Therefore, five different schemes for resource reservation are proposed to address different customers' requests. The simulation results of the DDQP present a rapid response to the requests and near-optimal performance. In every protection scheme, a primary SC provides the requested service in normal conditions while a backup SC is considered for each SC in the case of failure.  
Another reliable approach for increasing availability is to recover the failed component as quickly as possible without reserving extra resources. In [39], the VNF placement and chaining are initially protected through a decision tree approach, reducing the complexity compared to the existing approaches. Moreover, using a decision tree eases the search process for a replacement of a failure through a reliable algorithm called R-SFC-MCTS [39]. The proposed recovery algorithm initially chooses reliable components to avoid failure and re-map to a fault-tolerant one if a failure happens. The reported results confirm a higher acceptance rate while decreasing the penalties. Failure recovery approaches are categorized into proactive failure recovery (PFR) and reactive failure recovery (RFR) schemes [40; 41; 42]. In PFR, an algorithm predicts the chance of failure and starts the recovery procedure to reduce the recovery time. On the other hand, RFR approaches initiate the recovery process when a failure is reported. In [10], a deep reinforcement learning (DRL)-based Proactive Failure Recovery framework named ZT-PFR is proposed to simultaneously reduce the recovery delay and the resource cost. In Table 2, the key features of the protection scheme and recovery plans are presented.