# Mobility-Aware Hybrid Flow Rule Cache Scheme in Software-Defined Access Networks
## 2 Related Work  
The rule placement problem was researched in the SDN field for two main reasons: capacity constraint and signaling overhead. For flow-based fine-granular traffic engineering, numerous flow rules are required. For example, a typical enterprise network requires up to 8 million rules [25]. As mentioned above, flow rules are usually cached in the TCAM on the forwarding nodes, which enables very high lookup performance. However, the TCAM is 400 times more expensive than traditional memory and has limited capacity [26]. This results in approximately 20,000 flow rules for the commercial off-the-shelf switches, which is much less than the number of required rules for efficient traffic engineering [27]. In addition, as explained above, the flow rule caching requires the signaling exchange between the forwarding nodes and SDN controller. Consequently, the SDN controller can be overloaded when there are excessive flow rule request messages. This situation becomes severe in the access networks due to the dynamic mobility feature, which results in numerous mobile flows (i.e., handover events). To handle the rule placement problem, there have been efforts to reduce the number of flow rules in the core network devices. [PERSON] et al. [7] utilized the compression of the flow rules by means of wildcard expressions and logic minimization to save the flow entries without much compression time. [PERSON] et al. [8] provided the linear programming model for the flow rule allocation problem to satisfy the endpoint network policy while relaxing the routing policy. [PERSON] et al. [9] presented an optimization scheme to minimize the energy consumption of the routers and guarantee the QoS requirements based on the centralized network view. [PERSON] et al. [10] formulated an optimization problem for the rule caching method to minimize the end-to-end delay and provided a heuristic algorithm to solve it. However, these works only considered the static scenarios in the core network. In other words, they only assumed that the new flows which required the flow rule installation were newly generated flows to the devices without consideration for the moved flows from other devices due to the handover. Since the requirements between the newly generated and moved flows can be different in the access networks [11; 12],  
Figure 1: Motivating example. (**a**) Two flows of the MN are delivered through AD1. (**b**) MN moves and starts to communicate with AD2. an efficient method for flow rule installation that considers the different requirements is required. Note that this paper also covers the differentiation between the newly generated and moved flows. Compared with these works, which focused on the static scenarios in the core networks, there have been several works which considered mobile scenarios in access networks [6; 11; 12; 18; 19; 20; 21; 22; 23; 24]. Since there can be numerous mobile flows (i.e., handover events), they may generate multitudinous flow rule requests and necessitate a fast response for flow rule installation. To meet these challenges, mobility has been considered a main feature of the flow rule placement. As an initial work, [PERSON] et al. [6] used duplicated rules according to the user mobility while minimizing the rules' space occupation and guaranteeing the satisfaction ratio. However, this work just focused on the constraint of flow rules without consideration of the flow rule installation procedure between the devices and controller. [PERSON] et al. [11] proactively cached the flow rules into a new path which would be utilized for the computed task download according to the user's mobility. [PERSON] et al. [12] predicted the next locations of the end users based on the Markov predictor and determined the access points (APs) to minimize the delay and the number of active APs. Similarly, [PERSON] et al. [18] provided prefetching algorithms which optimized the least recently used approach, considering the forwarding paths in the access networks and user positions to increase the cache hit ratio.