Optimal Device-Aware Caching

Shukla, Samta; Abouzeid, Alhussein A.

doi:10.1109/tmc.2016.2610978

Cited by 18 publications

(10 citation statements)

References 29 publications

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“…For example, using a Markov decision process, Bahat and Makowski proved that the optimal content replacement policy is a Markov stationary policy under the independent reference model [44]. In their recent work, Shukla and Abouzeid modeled content replacement using a Markov decision process and found the optimal content retention time to jointly minimize content retrieval delay and cache wearout [45]. Our focus here, however, is the design of content replacement that can converge to a target stationary point in the case when the instantaneous content popularity is time-invariant and adapt to the variations in the case when the instantaneous content popularity is time-varying.…”

Section: The Content Replacement Markov Chainmentioning

confidence: 99%

The Design of Dynamic Probabilistic Caching with Time-Varying Content Popularity

Gao

Zhang

Zhao

et al. 2021

IEEE Trans. on Mobile Comput.

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In this paper, we design dynamic probabilistic caching for the scenario when the instantaneous content popularity may vary with time while it is possible to predict the average content popularity over a time window. Based on the average content popularity, optimal content caching probabilities can be found, e.g., from solving optimization problems, and existing results in the literature can implement the optimal caching probabilities via static content placement. The objective of this work is to design dynamic probabilistic caching that: i) converge (in distribution) to the optimal content caching probabilities under time-invariant content popularity, and ii) adapt to the time-varying instantaneous content popularity under time-varying content popularity. Achieving the above objective requires a novel design of dynamic content replacement because static caching cannot adapt to varying content popularity while classic dynamic replacement policies, such as LRU, cannot converge to target caching probabilities (as they do not exploit any content popularity information). We model the design of dynamic probabilistic replacement policy as the problem of finding the state transition probability matrix of a Markov chain and propose a method to generate and refine the transition probability matrix. Extensive numerical results are provided to validate the effectiveness of the proposed design.

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Section: The Content Replacement Markov Chainmentioning

confidence: 99%

The Design of Dynamic Probabilistic Caching with Time-Varying Content Popularity

Gao

Zhang

Zhao

et al. 2021

IEEE Trans. on Mobile Comput.

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“…The only prior lower bound is an infinitely large cache [24,1,48], which is very conservative and gives no sense of how OPT changes at different cache sizes. Belady variants (e.g., Belady-Size in Section 2.4) are widely used as an upper bound [48,61,46,77], despite offering no guarantees of optimality. While these offline bounds are easy to compute, we will show that they are in fact far from OPT.…”

Section: The Problem: Finding the Offline Optimalmentioning

confidence: 99%

Practical Bounds on Optimal Caching with Variable Object Sizes

Berger

Beckmann

2018

Proc. ACM Meas. Anal. Comput. Syst.

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Many recent caching systems aim to improve miss ratios, but there is no good sense among practitioners of how much further miss ratios can be improved. In other words, should the systems community continue working on this problem?Currently, there is no principled answer to this question. In practice, object sizes often vary by several orders of magnitude, where computing the optimal miss ratio (OPT) is known to be NP-hard. The few known results on caching with variable object sizes provide very weak bounds and are impractical to compute on traces of realistic length.We propose a new method to compute upper and lower bounds on OPT. Our key insight is to represent caching as a min-cost flow problem, hence we call our method the flow-based offline optimal (FOO). We prove that, under simple independence assumptions, FOO's bounds become tight as the number of objects goes to infinity. Indeed, FOO's error over 10M requests of production CDN and storage traces is negligible: at most 0.3%. FOO thus reveals, for the first time, the limits of caching with variable object sizes.While FOO is very accurate, it is computationally impractical on traces with hundreds of millions of requests. We therefore extend FOO to obtain more efficient bounds on OPT, which we call practical flow-based offline optimal (PFOO). We evaluate PFOO on several full production traces and use it to compare OPT to prior online policies. This analysis shows that current caching systems are in fact still far from optimal, suffering 11-43% more cache misses than OPT, whereas the best prior offline bounds suggest that there is essentially no room for improvement.

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“…Denote by f (t) the storage cost due to storing a content in a helper's cache in slot t. A longer retention time needs a higher threshold voltage, which results in a higher memory damage and consequently gives a higher storage cost, for more detailed discussions, see [2]. Motivated by this, we assume that f (t) is an increasing function.…”

Section: B Cost Modelmentioning

confidence: 99%

“…However, storing a content may be subject to a cost as well. A storage cost may be due to flash rental cost incurred by cloud service providers or flash damage caused by writing a content to the memory device [2]. In both cases, the storage cost typically depends on the time duration of storage, hereafter referred to as the retention time.…”

Section: Introductionmentioning

confidence: 99%

On Optimal Proactive and Retention-Aware Caching with User Mobility

Ahani

Yuan

2018

2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)

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Caching popular contents at edge devices is an effective solution to alleviate the burden of the backhaul networks. Earlier investigations commonly neglected the storage cost in caching. More recently, retention-aware caching, where both the downloading cost and storage cost are accounted for, is attracting attention. Motivated by this, we address proactive and retention-aware caching problem with the presence of user mobility, optimizing the sum of the two types of costs. More precisely, a cost-optimal caching problem for vehicle-to-vehicle networks is formulated with joint consideration of the impact of the number of vehicles, cache size, storage cost, and content request probability. This is a combinatorial optimization problem. However, we derive a stream of analytical results and they together lead to an algorithm that guarantees global optimum with polynomial-time complexity. Numerical results show significant improvements in comparison to popular caching and random caching.

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Optimal Device-Aware Caching

Cited by 18 publications

References 29 publications

The Design of Dynamic Probabilistic Caching with Time-Varying Content Popularity

The Design of Dynamic Probabilistic Caching with Time-Varying Content Popularity

Practical Bounds on Optimal Caching with Variable Object Sizes

On Optimal Proactive and Retention-Aware Caching with User Mobility

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