# Optimal Proactive Caching for Multi-View Streaming Mobile Augmented Reality
## 4 Numerical Investigations
### Parameterization  
The same as in [8], a typical tree-like network topology as shown by Figure 5 is applied with 20 ECs in total with four to eight ECs being activated for the current MAR service, and 20 to 40 requests are sent by MAR devices. The remaining available resources allocated for MAR support of an EC are assumed to be CPUs with four to eight cores and 100 to 400 MBytes of cache memory [8]. Each request requires a single free unit for each service function (for example, a VM) [3]. There are 10 to 14 available VMs in each EC, with equal splitting of the available CPU resources [8]. Up to four different view streams of the video content per user can be cached and are similar to each other. All target AROs must be embedded within the corresponding view stream before being streamed to an end user  
Figure 4: A Toy Example of the LSTM-based Model.  
Figure 3: Working Process of the LSTM-based Model.  
based on a matched result. The user could trigger the MAR service by a certain behavior (e.g., clicking an AR label), and pointers such as a name or index are transferred to the EC to identify the ARO [10]. The size of such pointers used for matching are only a few bytes in size, and hence, their transmission and processing latency is neglected in the simulations. The bandwidth of a nominal 5G access point is set to 20 MHz, its transmission power is assumed to be 20 dBm with the maximum of 100 resource blocks, and we assume, without loss of generality, each user can utilize only one resource block [31; 32; 33]. The noise power is \(10^{-11}\) W and the path loss exponent is 4 [31]. The location of the users is randomly generated as well as the potential destinations. Furthermore, we assume that each cell has a radius as 250 m in the 5G wireless network [31]. For the LSTM network, 90% of 300 route vectors with corresponding optimal decisions are utilized for training, while the remaining 10% are used for testing and validation. The initial learning rate is set to 0.005, the maximum number of epochs is 160 and the dropout probability is 5% to avoid over-fitting. Key simulation parameters are summarized in the following Table 2.  
To appreciate the quality of the proposed set of solutions, a number of baseline schemes for caching MAR content are also included in the investigations. The Random Selection Scheme (RandS) allocates MAR content on randomly picked ECs. The Closest First Scheme (CFS) directly chooses the closest edge cloud to the user's initial location and accepts the second closest one as a backup choice [34]. The utilization based scheme (UTIL) selects the closest edge cloud as the CFS scheme but takes the least loaded EC as its backup choice and rejects any more cache or execution decisions if an EC exceeds its acceptable operational state (for example over 80% occupied VMs in the EC) [35]. Note that all such baseline schemes assign view streams and embedded AROs according to the above mentioned policies and hence neglect the user mobility (i.e., they are mobility oblivious). Furthermore, they will also suffer from a penalty cost if their target ECs (including backup EC choices) are all fully occupied.  
Figure 5: Typical tree-like designed network topology.