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1. STRCACHEML: A Machine Learning-Assisted Content Caching Policy for Streaming Services

   Mahara, A; Fuentes, J; Poellabauer, C; Rishe, Naphtali D (February 2024, Research Gate)

   Content caching is vital for enhancing web server efficiency and reducing network congestion, particularly in platforms predicting user actions Despite many studies conducted to improve cache replacement strategies , there remains space for improvement. This paper introduces STRCacheML, a Machine Learning (ML) assisted Content Caching Policy. STRCacheML leverages available attributes within a platform to make intelligent cache replacement decisions offline. We have t ested various Machine Learning and Deep Learning algorithms to adapt the one with the highest accuracy; we have integrated that algorithm into our cache replacement policy. This selected ML algorithm was employed to estimate the likelihood of cache objects being requested again, an essential factor in cache eviction scenarios. The IMDb dataset, constituting numerous videos with corresponding attributes, was utilized to conduct our experiment. The experimental section highlights our model’s efficacy, present ing comparative results compared to the established approaches based on raw cache hits and cache hit rates. 

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2. STRCacheML: A Machine Learning-Assisted Content Caching Policy for Streaming Services

   https://doi.org/10.5121/csit.2024.140409  

   Mahara, Arpan; Fuentes, Jose; Poellabauer, Christian; D_Rishe, Naphtali (February 2024, Academy & Industry Research Collaboration Center)

   Content caching is vital for enhancing web server efficiency and reducing network congestion, particularly in platforms predicting user actions. Despite many studies conducted toimprove cache replacement strategies, there remains space for improvement. This paper introduces STRCacheML, a Machine Learning (ML) assisted Content Caching Policy. STRCacheML leverages available attributes within a platform to make intelligent cache replacement decisions offline. We have tested various Machine Learning and Deep Learning algorithms to adapt the one with the highest accuracy; we have integrated that algorithm into our cache replacement policy. This selected ML algorithm was employed to estimate the likelihood of cache objects being requested again, an essential factor in cache eviction scenarios. The IMDb dataset, constituting numerous videos with corresponding attributes, was utilized to conduct our experiment. The experimental section highlights our model’s efficacy, presenting comparative results compared to the established approaches based on raw cache hits and cache hit rates. 

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3. STRCACHEML: A Machine Learning-Assisted Content Caching Policy for Streaming Services

   Mahara, A; Fuentes, J; Poellabauer, C; Rishe, Naphtali D (February 2024, CS & IT - CSCP 2024)

   Wyld, David C (Ed.)

   Content caching is vital for enhancing web server efficiency and reducing network congestion, particularly in platforms predicting user actions. Despite many studies conducted toimprove cache replacement strategies, there remains space for improvement. This paper introduces STRCacheML, a Machine Learning (ML) assisted Content Caching Policy. STRCacheML leverages available attributes within a platform to make intelligent cache replacement decisions offline. We have tested various Machine Learning and Deep Learning algorithms to adapt the one with the highest accuracy; we have integrated that algorithm into our cache replacement policy. This selected ML algorithm was employed to estimate the likelihood of cache objects being requested again, an essential factor in cache eviction scenarios. The IMDb dataset, constituting numerous videos with corresponding attributes, was utilized to conduct our experiment. The experimental section highlights our model’s efficacy, presenting comparative results compared to the established approaches based on raw cache hits and cache hit rates. 

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4. Cache on Track (CoT): Decentralized Elastic Caches for Cloud Environments

   Zakhary, V.; Lim, L.; Agrawal, D.; El Abbadi, A. (March 2021, International Conference on Extending Database Technology)

   Velegrakis, Y.; Zeinalipour-Yazti, D.; Chrysanthis, P.K.; Guerra, F. (Ed.)

   Distributed caches are widely deployed to serve social networks and web applications at billion-user scales. This paper presents Cache-on-Track (CoT), a decentralized, elastic, and predictive caching framework for cloud environments. CoT proposes a new cache replacement policy specifically tailored for small front-end caches that serve skewed workloads with small update percentage. Small front-end caches are mainly used to mitigate the load-imbalance across servers in the distributed caching layer. Front-end servers use a heavy hitter tracking algorithm to continuously track the top-k hot keys. CoT dynamically caches the top-C hot keys out of the tracked keys. CoT’s main advantage over other replacement policies is its ability to dynamically adapt its tracker and cache sizes in response to workload distribution changes. Our experiments show that CoT’s replacement policy consistently outperforms the hit-rates of LRU, LFU, and ARC for the same cache size on different skewed workloads. Also, CoT slightly outperforms the hit-rate of LRU-2 when both policies are configured with the same tracking (history) size. CoT achieves server size load-balance with 50% to 93.75% less front-end cache in comparison to other replacement policies. Finally, experiments show that CoT’s resizing algorithm successfully auto-configures the tracker and cache sizes to achieve back-end load-balance in the presence of workload distribution changes. 

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5. Reinforcement Learning for Dynamic Dimensioning of Cloud Caches: A Restless Bandit Approach

   https://doi.org/10.1109/TNET.2023.3235480  

   Xiong, Guojun; Wang, Shufan; Yan, Gang; Li, Jian (January 2023, IEEE/ACM Transactions on Networking)

   We study the dynamic cache dimensioning problem, where the objective is to decide how much storage to place in the cache to minimize the total costs with respect to the storage and content delivery latency. We formulate this problem as a Markov decision process, which turns out to be a restless multi-armed bandit problem and is provably hard to solve. For given dimensioning decisions, it is possible to develop solutions based on the celebrated Whittle index policy. However, Whittle index policy has not been studied for dynamic cache dimensioning, mainly because cache dimensioning needs to be repeatedly solved and jointly optimized with content caching. To overcome this difficulty, we propose a low-complexity fluid Whittle index policy, which jointly determines dimensioning and content caching. We show that this policy is asymptotically optimal. We further develop a lightweight reinforcement learning augmented algorithm dubbed fW-UCB when the content request and delivery rates are unavailable. fW-UCB is shown to achieve a sub-linear regret as it fully exploits the structure of the near-optimal fluid Whittle index policy and hence can be easily implemented. Extensive simulations using real traces support our theoretical results. 

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