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   https://doi.org/10.1109/SecDev45635.2020.00029  

   Kim, Sungkeun ; Mahmud, Farabi ; Huang, Jiayi ; Majumder, Pritam ; Christou, Neophytos ; Muzahid, Abdullah ; Tsai, Chia-Che ; Kim, Eun Jung ( September 2020 , IEEE Secure Development (SecDev))
2. Cache-Aided Interference Management using Hypercube Combinatorial Cache Designs

   https://doi.org/10.1109/ICC.2019.8761238  

   Zhang, Xiang ; Woolsey, Nicholas ; Ji, Mingyue ( May 2019 , ICC 2019 - 2019 IEEE International Conference on Communications (ICC))
3. Cache Shaping: An Effective Defense Against Cache-Based Website Fingerprinting

   https://doi.org/10.1145/3508398.3511500  

   Li, Haipeng ; Niu, Nan ; Wang, Boyang ( April 2022 , CODASPY '22: Proceedings of the Twelfth ACM Conference on Data and Application Security and Privacy)
4. RL-Cache: Learning-Based Cache Admission for Content Delivery

   https://doi.org/10.1109/JSAC.2020.3000415  

   Kirilin, Vadim ; Sundarrajan, Aditya ; Gorinsky, Sergey ; Sitaraman, Ramesh K. ( June 2020 , IEEE Journal on Selected Areas in Communications)

   Content delivery networks (CDNs) distribute much of the Internet content by caching and serving the objects requested by users. A major goal of a CDN is to maximize the hit rates of its caches, thereby enabling faster content downloads to the users. Content caching involves two components: an admission algorithm to decide whether to cache an object and an eviction algorithm to determine which object to evict from the cache when it is full. In this paper, we focus on cache admission and propose a novel algorithm called RL-Cache that uses model-free reinforcement learning (RL) to decide whether or not to admit a requested object into the CDN’s cache. Unlike prior approaches that use a small set of criteria for decision making, RL-Cache weights a large set of features that include the object size, recency, and frequency of access. We develop a publicly available implementation of RL-Cache and perform an evaluation using production traces for the image, video, and web traffic classes from Akamai’s CDN. The evaluation shows that RL-Cache improves the hit rate in comparison with the state of the art and imposes only a modest resource overhead on the CDN servers. Further, RL-Cache is robust enough that it can be trained in one location and executed on request traces of the same or different traffic classes in other locations of the same geographic region. The paper also reports extensive analyses of the RL-Cache sensitivity to its features and hyperparameter values. The analyses validate the made design choices and reveal interesting insights into the RL-Cache behavior. 

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5. R-Cache: A Highly Set-Associative In-Package Cache Using Memristive Arrays

   https://doi.org/10.1109/ICCD.2018.00070  

   Behnam, Payman ; Pal Chowdhury, Arjun ; Nazm Bojnordi, Mahdi ( October 2018 , 2018 IEEE 36th International Conference on Computer Design (ICCD))

   Over the past decade, three-dimensional die stacking technology has been considered for building large-scale in-package memory systems. In particular, in-package DRAM cache has been considered as a promising solution for high bandwidth and large-scale cache architectures. There are, however, significant challenges such as limited energy efficiency, costly tag management, and physical limitations for scalability that need to be effectively addressed before one can adopt in-package caches in the real-world applications. This paper proposes R-Cache, an in-package cache made by 3D die stacking of memristive memory arrays to alleviate the above-mentioned challenges. Our simulation results on a set of memory intensive parallel applications indicate that R-Cache outperforms the state-of-the-art proposals for in-package caches. R-Cache improves performance by 38% and 27% over the state-of-the-art direct mapped and set associative cache architectures, respectively. Moreover, R-Cache results in averages of 40% and 27% energy reductions as compared to the direct mapped and set-associative cache systems. 

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