# Enhancing Cache Robustness in Information-Centric Networks: Per-Face Popularity Approaches
## 6 Summary, Conclusions, and Future Work  
Throughout this work, we have explored the effects of normalizing contributions toward caching influence across faces, yielding our Per-Face Popularity, or PFP schemes. The original PFP scheme showed promise but was not scalable due to the overly large (unbounded) lists that needed to be maintained of content object rankings for each face. To mitigate this issue and to also add a sense of recency to our PFP scheme, we developed PFP-DA. PFP-DA employs a dynamic aging characteristic to ensure that content items that were ranked for a given face do not remain there longer than they should due to their extreme popularity at one time. Many of our tests involved PFP-DA. The results clearly demonstrate that PFP-DA performs better than LFU-DA and LRU in nearly all circumstances that were tested. This was especially true in terms of the ability of PFP-DA to maintain the most popular items in the cache, thus yielding much higher hit rates.  
To further explore the PFP-DA scheme, a \(\beta\) parameter was added, which, when sufficiently small, reduces the numeric distance between the rank contribution of the top-ranked item and any subsequent item. We can consider our regular PFP-DA as a special case of PFP-\(\beta\) with \(\beta=1.0\). The PFP-\(\beta\) tests yielded interesting results. For smaller values of \(\beta\), the cache pollution tended to be less (even 0%), and hit rates tended to be greater (nearly 100% for the top 100 and top 50 items), but only if the percentage of attackers was relatively small.  
In conclusion, we have created a new approach to increasing cache robustness by means of normalizing influence on caching decisions using a per-face fairness technique called Per-Face Popularity, or PFP. We have improved the original algorithm substantially and created many additional opportunities for future research as well.  
For our future research endeavors related to this work, we will explore the applicability of our PFP techniques to current caching technologies, such as those used by CDNs. Even though our original work is geared toward content-centric approaches, specifically NDN, it is possible that our research can yield more immediate benefit to the industry, even if used in application overlays.  
Further comparisons with other caching algorithms, such as the Name Popularity Algorithm [25] and CaDaCa [26], are needed. It is possible that the PFP-based algorithms could enhance or be enhanced with these additional caching techniques in a hybrid strategy.  
Furthermore, we will continue to refine our techniques and attempt to arrive at precise mathematical or statistical models to better predict and describe the behavior of our schemes.  
**Author Contributions:** Conceptualization, J.B. and J.G.; Coding, J.B.; Supervision, [PERSON] All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Zip files of the code and raw data are available at [[https://github.com/profjpbaugh/jpb-jg-pfp-2023.git](https://github.com/profjpbaugh/jpb-jg-pfp-2023.git)]([https://github.com/profjpbaugh/jpb-jg-pfp-2023.git](https://github.com/profjpbaugh/jpb-jg-pfp-2023.git)) (accessed on 27 July 2023).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\# Attackers** & **\% Attacker** & **Pollution \%** & **Hit \%** & **Top 100** & **Top 50** \\ \hline
1 & 5\% & 0.00\% & 22.76\% & 87.98\% & 98.83\% \\
2 & 10\% & 0.00\% & 22.55\% & 89.60\% & 97.96\% \\
3 & 15\% & 8.00\% & 21.00\% & 81.99\% & 95.86\% \\
4 & 20\% & 26.00\% & 19.25\% & 81.59\% & 95.45\% \\
5 & 25\% & 52.00\% & 15.43\% & 68.14\% & 87.85\% \\
6 & 30\% & 63.00\% & 13.10\% & 60.38\% & 80.39\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: Attacker frequency of 720, \(\beta\) value of 0.1, cache size of 100, and varying number of attackers.