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Optimizing TTL Caches under Heavy-Tailed Demands

Published: 14 June 2016 Publication History

Abstract

In this paper we analyze the hit performance of cache systems that receive file requests with general arrival distributions and different popularities. We consider timer-based (TTL) policies, with differentiated timers over which we optimize. The optimal policy is shown to be related to the monotonicity of the hazard rate function of the inter-arrival distribution. In particular for decreasing hazard rates, timer policies outperform the static policy of caching the most popular contents. We provide explicit solutions for the optimal policy in the case of Pareto-distributed inter-request times and a Zipf distribution of file popularities, including a compact fluid characterization in the limit of a large number of files. We compare it through simulation with classical policies, such as least-recently-used and discuss its performance. Finally, we analyze extensions of the optimization framework to a line network of caches.

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cover image ACM Conferences
SIGMETRICS '16: Proceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science
June 2016
434 pages
ISBN:9781450342667
DOI:10.1145/2896377
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 14 June 2016

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Author Tags

  1. caching algorithms
  2. heavy-tails
  3. performance evaluation

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SIGMETRICS '16 Paper Acceptance Rate 28 of 208 submissions, 13%;
Overall Acceptance Rate 459 of 2,691 submissions, 17%

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  • (2023)Time-to-Live Caching With Network Delays: Exact Analysis and Computable ApproximationsIEEE/ACM Transactions on Networking10.1109/TNET.2022.320791431:3(1087-1100)Online publication date: Jun-2023
  • (2023)Measurement of a Large-Scale Short-Video Service Over Mobile and Wireless NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2021.313989322:6(3472-3488)Online publication date: 1-Jun-2023
  • (2023)Optimized Dynamic Cache Instantiation and Accurate LRU Approximations Under Time-Varying Request VolumeIEEE Transactions on Cloud Computing10.1109/TCC.2021.311595911:1(779-797)Online publication date: 1-Jan-2023
  • (2021)Selfish Caching Games on Directed GraphsIEEE/ACM Transactions on Networking10.1109/TNET.2020.304794029:2(709-722)Online publication date: Apr-2021
  • (2021)A TTL-based Approach for Content Placement in Edge NetworksPerformance Evaluation Methodologies and Tools10.1007/978-3-030-92511-6_1(1-21)Online publication date: 8-Dec-2021
  • (2020)AutoSight: Distributed Edge Caching in Short Video NetworkIEEE Network10.1109/MNET.001.190034534:3(194-199)Online publication date: May-2020
  • (2019)A TTL-based Approach for Data Aggregation in Geo-distributed Streaming AnalyticsProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/3341617.33261443:2(1-27)Online publication date: 19-Jun-2019
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  • (2019)A Utility Optimization Approach to Network Cache DesignIEEE/ACM Transactions on Networking10.1109/TNET.2019.291367727:3(1013-1027)Online publication date: 1-Jun-2019
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