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1. GRADES: Gradient Descent for Similarity Caching

   https://doi.org/10.1109/INFOCOM42981.2021.9488757  

   Sabnis, Anirudh; Salem, Tareq Si; Neglia, Giovanni; Garetto, Michele; Leonardi, Emilio; Sitaraman, Ramesh K. (May 2021, IEEE Conference on Computer Communications (INFOCOM '21))

   null (Ed.)

   A similarity cache can reply to a query for an object with similar objects stored locally. In some applications of similarity caches, queries and objects are naturally represented as pointsinacontinuousspace.Examplesinclude360◦ videoswhere user’s head orientation—expressed in spherical coordinates— determines what part of the video needs to be retrieved, and recommendation systems where the objects are embedded in a finite-dimensional space with a distance metric to capture content dissimilarity. Existing similarity caching policies are simple modifications of classic policies like LRU, LFU, and qLRU and ignore the continuous nature of the space where objects are embedded. In this paper, we propose GRADES, a new similarity caching policy that uses gradient descent to navigate the continuous space and find the optimal objects to store in the cache. We provide theoretical convergence guarantees and show GRADES increases the similarity of the objects served by the cache in both applications mentioned above. 

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2. Keep-alive caching for the Hawkes process

   Narayana, Sushirdeep; Kash, Ian A (July 2023, PMLR)

   We study the design of caching policies in applications such as serverless computing where there is not a fixed size cache to be filled, but rather there is a cost associated with the time an item stays in the cache. We present a model for such caching policies which captures the trade-off between this cost and the cost of cache misses. We characterize optimal caching policies in general and apply this characterization by deriving a closed form for Hawkes processes. Since optimal policies for Hawkes processes depend on the history of arrivals, we also develop history-independent policies which achieve near-optimal average performance. We evaluate the performances of the optimal policy and approximate polices using simulations and a data trace of Azure Functions, Microsoft's FaaS (Function as a Service) platform for serverless computing. 

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3. TRAGEN: a synthetic trace generator for realistic cache simulations

   https://doi.org/10.1145/3487552.3487845  

   Sabnis, Anirudh; Sitaraman, Ramesh K. (November 2021, Proceedings of the 21st ACM Internet Measurement Conference (IMC'21))

   Traces from production caching systems of users accessing con- tent are seldom made available to the public as they are considered private and proprietary. The dearth of realistic trace data makes it difficult for system designers and researchers to test and validate new caching algorithms and architectures. To address this key problem, we present TRAGEN, a tool that can generate a synthetic trace that is “similar” to an original trace from the production system in the sense that the two traces would result in similar hit rates in a cache simulation. We validate TRAGEN by first proving that the synthetic trace is similar to the original trace for caches of arbitrary size when the Least-Recently-Used (LRU) policy is used. Next, we empirically validate the similarity of the synthetic trace and original trace for caches that use a broad set of commonly-used caching policies that include LRU, SLRU, FIFO, RANDOM, MARKERS, CLOCK and PLRU. For our empirical validation, we use original request traces drawn from four different traffic classes from the world’s largest CDN, each trace consisting of hundreds of millions of requests for tens of millions of objects. TRAGEN is publicly available and can be used to generate synthetic traces that are similar to actual pro- duction traces for a number of traffic classes such as videos, social media, web, and software downloads. Since the synthetic traces are similar to the original production ones, cache simulations performed using the synthetic traces will yield similar results to what might be attained in a production setting, making TRAGEN a key tool for cache system developers and researchers. 

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4. 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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5. 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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