Proactive retention aware caching

Shukla, Samta; Abouzeid, Alhussein A.

doi:10.1109/infocom.2017.8057029

Cited by 31 publications

(14 citation statements)

References 17 publications

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“…For the simulation setup, we set T = 24 where each time slot has a length of one hour [38], [39]. Similar to the works in [17], [22], we set U = 600 and F = 200 where the sizes of contents are uniformly generated within interval [1,10].…”

Section: A Simulation Setupmentioning

confidence: 99%

Optimal Scheduling of Content Caching Subject to Deadline

Ahani

Yuan

2020

IEEE Open J. Commun. Soc.

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Content caching at the edge of network is a promising technique to alleviate the burden of backhaul networks. In this paper, we consider content caching along time in a base station with limited cache capacity. As the popularity of contents may vary over time, the contents of cache need to be updated accordingly. In addition, a requested content may have a delivery deadline within which the content needs to be obtained. Motivated by these, we address optimal scheduling of content caching in a time-slotted system under delivery deadline and cache capacity constraints. The objective is to minimize a cost function that captures the load of backhaul links. For our optimization problem, we prove its NP-hardness via a reduction from the Partition problem. For problem solving, via a mathematical reformulation, we develop a solution approach based on repeatedly applying a column generation algorithm and a problem-tailored rounding algorithm. In addition, two greedy algorithms are developed based on existing algorithms from the literature. Finally, we present extensive simulations that verify the effectiveness of our solution approach in obtaining near-tooptimal solutions in comparison to the greedy algorithms. The solutions obtained from our solution approach are within 1.6% from global optimality.

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Section: A Simulation Setupmentioning

confidence: 99%

Optimal Scheduling of Content Caching Subject to Deadline

Ahani

Yuan

2020

IEEE Open J. Commun. Soc.

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“…Therefore, an appropriate frequency should be determined to balance the tradeoff between the performance improvement and the overhead, which is one of our future works. 4 After receiving the policy table, SCs decide which contents are cached in their storages and whether to sleep or not by following the distributed policy table. To obtain the optimal policy table, we formulate MDP which will be elaborated in Section 5.…”

Section: Energy-efficient Proactive Edge Caching With Sleep Schedulinmentioning

confidence: 99%

“…1 Proactive content caching is demonstrated to further boost the caching efficiency compared to reactive data caching [2][3][4]. 2 We assume that contents are cached in only SCs (not cached in MC) to maximize the effectiveness of edge caching.…”

Section: Endnotesmentioning

confidence: 99%

“…To alleviate this problem, the content caching where contents are stored in intermediate nodes of networks has received a high attention by lots of network operators. By amplifying the caching efficiency, contents can be proactively stored on the network edge (e.g., small cells (SCs)) [2][3][4][5]. 1 Specifically, the energy consumption for content transmission over the backhaul link can be significantly reduced by means of proactive edge caching in SCs.…”

Section: Introductionmentioning

confidence: 99%

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Energy-efficient proactive edge caching with sleep scheduling for green networks

Lee

Pack

2018

J Wireless Com Network

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Proactive content caching in small cells (SCs) (i.e., proactive edge caching) can significantly reduce energy consumption of networks. However, the sleep mode of SCs can make the cached contents in SCs unavailable. Therefore, a joint optimization of edge caching strategy and sleep scheduling should be conducted to maximize the effectiveness of edge caching. In this paper, we propose an energy-efficient proactive edge caching with sleep scheduling (E3CS) where the controller jointly adjusts the caching strategy and the sleep scheduling of SCs by considering the content popularity dynamics. To optimize the performance of E3CS (i.e., minimize the overall energy consumption), we formulate a Markov decision process (MDP) and the joint optimal policy on the caching strategy and sleep scheduling is obtained by a value iteration algorithm. Evaluation results demonstrate that E3CS with the optimal policy outperforms the comparison schemes in terms of the overall energy consumption.

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“…e ← e − b * (k, e) 21: return x * We use a Zipf distribution with shape parameter γ to characterize the content request probability for any requester. Thus, w rc = c −γ k∈C k −γ , r ∈ R. Same as [8], the time period is set to 24 hours, and the duration of each time slot (δ) is 1 hour. The storage cost is simulated using f (t) = t 2 .…”

Section: Performance Evaluationmentioning

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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Proactive retention aware caching

Cited by 31 publications

References 17 publications

Optimal Scheduling of Content Caching Subject to Deadline

Optimal Scheduling of Content Caching Subject to Deadline

Energy-efficient proactive edge caching with sleep scheduling for green networks

On Optimal Proactive and Retention-Aware Caching with User Mobility

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