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A Utility Optimization Approach to Network Cache Design
Networking and Internet Architecture · Submitted 25 Jan 2016
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim In this paper, we propose utility-driven caching, where we associate with each content a utility, which is a function of the corresponding content hit probability.
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The abstract, sentence by sentence
In any caching system, the admission and eviction policies determine which contents are added and removed from a cache when a miss occurs. Usually, these policies are devised so as to mitigate staleness and increase the hit probability. Nonetheless, the utility of having a high hit probability can vary across contents. This occurs, for instance, when service level agreements must be met, or if certain contents are more difficult to obtain than others. In this paper, we propose utility-driven caching, where we associate with each content a utility, which is a function of the corresponding content hit probability. We formulate optimization problems where the objectives are to maximize the sum of utilities over all contents. These problems differ according to the stringency of the cache capacity constraint. Our framework enables us to reverse engineer classical replacement policies such as LRU and FIFO, by computing the utility functions that they maximize. We also develop online algorithms that can be used by service providers to implement various caching policies based on arbitrary utility functions.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In any caching system, the admission and eviction policies determine which contents are added and removed from a cache when a miss occurs. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | Usually, these policies are devised so as to mitigate staleness and increase the hit probability. | background 1.00 | 0.04 | 0.02 | asserted 0.87 |
| s2 | Nonetheless, the utility of having a high hit probability can vary across contents. | background 0.57 | 0.09 | 0.03 | asserted 0.97 |
| s3 | This occurs, for instance, when service level agreements must be met, or if certain contents are more difficult to obtain than others. | background 0.95 | 0.05 | 0.02 | asserted 0.61 |
| s4 | In this paper, we propose utility-driven caching, where we associate with each content a utility, which is a function of the corresponding content hit probability. | contribution 0.93 | 0.93 | 0.23 | asserted 1.00 |
| s5 | We formulate optimization problems where the objectives are to maximize the sum of utilities over all contents. | method 0.57 | 0.91 | 0.17 | asserted 0.99 |
| s6 | These problems differ according to the stringency of the cache capacity constraint. | method 0.75 | 0.61 | 0.04 | asserted 0.99 |
| s7 | Our framework enables us to reverse engineer classical replacement policies such as LRU and FIFO, by computing the utility functions that they maximize. | contribution 0.62 | 0.93 | 0.18 | asserted 0.94 |
| s8 | We also develop online algorithms that can be used by service providers to implement various caching policies based on arbitrary utility functions. | contribution 0.93 | 0.95 | 0.20 | asserted 0.97 |
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Original abstract
In any caching system, the admission and eviction policies determine which contents are added and removed from a cache when a miss occurs. Usually, these policies are devised so as to mitigate staleness and increase the hit probability. Nonetheless, the utility of having a high hit probability can vary across contents. This occurs, for instance, when service level agreements must be met, or if certain contents are more difficult to obtain than others. In this paper, we propose utility-driven caching, where we associate with each content a utility, which is a function of the corresponding content hit probability. We formulate optimization problems where the objectives are to maximize the sum of utilities over all contents. These problems differ according to the stringency of the cache capacity constraint. Our framework enables us to reverse engineer classical replacement policies such as LRU and FIFO, by computing the utility functions that they maximize. We also develop online algorithms that can be used by service providers to implement various caching policies based on arbitrary utility functions.
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