Temporal Locality in Today's Content Caching: Why it Matters and How to Model it
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Abstract (arXiv)
The dimensioning of caching systems represents a difficult task in the design of infrastructures for content distribution in the current Internet. This paper addresses the problem of defining a realistic arrival process for the content requests generated by users, due its critical importance for both analytical and simulative evaluations of the performance of caching systems. First, with the aid of YouTube traces collected inside operational residential networks, we identify the characteristics of real traffic that need to be considered or can be safely neglected in order to accurately predict the performance of a cache. Second, we propose a new parsimonious traffic model, named the Shot Noise Model (SNM), that enables users to natively capture the dynamics of content popularity, whilst still being sufficiently simple to be employed effectively for both analytical and scalable simulative studies of caching systems. Finally, our results show that the SNM presents a much better solution to account for the temporal locality observed in real traffic compared to existing approaches.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 May 2013.
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Citing papers per year: 2021: 4; 2022: 5; 2023: 1; 2024: 0; 2025: 2; 2026: 1.
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- 2021No-Regret Caching via Online Mirror Descentcited by 12
- 2021Online Caching with Optimal Switching Regretcited by 11
- 2022Optimistic No-regret Algorithms for Discrete Cachingcited by 10
- 2022Online Caching with Optimistic Learningcited by 7
- 2021Cocktail Edge Caching: Ride Dynamic Trends of Content Popularity with Ensemble Learningcited by 5
- 2022Online Caching with no Regret: Optimistic Learning via Recommendationscited by 4
- 2025A Plan Reuse Mechanism for LLM-Driven Agentcited by 3
- 2023No-Regret Caching with Noisy Request Estimatescited by 1
- 2025Efficient and Optimal No-Regret Caching under Partial Observationcited by 1
- 2021A Survey on Reinforcement Learning-Aided Caching in Mobile Edge Networkscited by 0
- 2022MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environmentscited by 0
- 2022Caching Contents with Varying Popularity using Restless Banditscited by 0
1 more
- 2026No-Regret Mixing of LRU and LFU with Optimal Switching Cost7 Sep 2026
- 2025A Plan Reuse Mechanism for LLM-Driven Agent24 Dec 2025
- 2025Efficient and Optimal No-Regret Caching under Partial Observation4 Mar 2025
- 2023No-Regret Caching with Noisy Request Estimates5 Sep 2023
- 2022Caching Contents with Varying Popularity using Restless Bandits31 Oct 2022
- 2022Optimistic No-regret Algorithms for Discrete Caching15 Aug 2022
- 2022MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environments21 May 2022
- 2022Online Caching with no Regret: Optimistic Learning via Recommendations20 Apr 2022
- 2022Online Caching with Optimistic Learning22 Feb 2022
- 2021A Survey on Reinforcement Learning-Aided Caching in Mobile Edge Networks12 May 2021
- 2021No-Regret Caching via Online Mirror Descent29 Jan 2021
- 2021Online Caching with Optimal Switching Regret18 Jan 2021
- 2025A Plan Reuse Mechanism for LLM-Driven Agentcited by 3
- 2025A Plan Reuse Mechanism for LLM-Driven Agent24 Dec 2025
- 2021No-Regret Caching via Online Mirror Descentcited by 12
- 2021Online Caching with Optimal Switching Regretcited by 11
- 2022Optimistic No-regret Algorithms for Discrete Cachingcited by 10
- 2022Online Caching with Optimistic Learningcited by 7
- 2021Cocktail Edge Caching: Ride Dynamic Trends of Content Popularity with Ensemble Learningcited by 5
- 2022Online Caching with no Regret: Optimistic Learning via Recommendationscited by 4
- 2023No-Regret Caching with Noisy Request Estimatescited by 1
- 2025Efficient and Optimal No-Regret Caching under Partial Observationcited by 1
- 2021A Survey on Reinforcement Learning-Aided Caching in Mobile Edge Networkscited by 0
- 2022MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environmentscited by 0
- 2022Caching Contents with Varying Popularity using Restless Banditscited by 0
- 2026No-Regret Mixing of LRU and LFU with Optimal Switching Costcited by 0
- 2026No-Regret Mixing of LRU and LFU with Optimal Switching Cost7 Sep 2026
- 2025Efficient and Optimal No-Regret Caching under Partial Observation4 Mar 2025
- 2023No-Regret Caching with Noisy Request Estimates5 Sep 2023
- 2022Caching Contents with Varying Popularity using Restless Bandits31 Oct 2022
- 2022Optimistic No-regret Algorithms for Discrete Caching15 Aug 2022
- 2022MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environments21 May 2022
- 2022Online Caching with no Regret: Optimistic Learning via Recommendations20 Apr 2022
- 2022Online Caching with Optimistic Learning22 Feb 2022
- 2021A Survey on Reinforcement Learning-Aided Caching in Mobile Edge Networks12 May 2021
- 2021No-Regret Caching via Online Mirror Descent29 Jan 2021
- 2021Online Caching with Optimal Switching Regret18 Jan 2021
- 2021Cocktail Edge Caching: Ride Dynamic Trends of Content Popularity with Ensemble Learning14 Jan 2021