Mobee: Mobility-Aware Energy-Efficient Coded Caching in Cloud Radio Access Networks

Tran, Tuyen X.; Kazemi, Fatemeh; Karimi, Esmaeil; Pompili, Dario

doi:10.1109/mass.2017.53

Cited by 15 publications

(9 citation statements)

References 15 publications

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“…Assume that there exists an integral solution with n = 5. Summing the inequalities over all hyperplanes H i containing v 1 = e 3 , i.e., (7), (8), and (10), and dividing by two gives j∈[ℓ]\{1} n j ≥ 3.5, so that n 1 ≤ 1. By symmetry, we also conclude n 2 , n 4 ≤ 1.…”

Section: B Using Geometric Approach To Derive Bounds On N(r)mentioning

confidence: 99%

“…Although replication has been typically preferred due its simplicity, it can be expensive in terms of storage. Erasure codes have been shown to be effective in achieving various goals such as providing reliability against node failures (see e.g., [1]- [4]), ensuring availability of stored content during high demand (see e.g., [5]- [8]), enabling the recovery of a data object from multiple disjoint groups of nodes (see e.g., [9]- [11]), and providing fast content download (see e.g., [12]- [20]).…”

Section: Introductionmentioning

confidence: 99%

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Efficient Storage Schemes for Desired Service Rate Regions

Kazemi¹,

Kurz²,

Soljanin³

et al. 2020

Preprint

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A major concern in cloud/edge storage systems is serving a large number of users simultaneously.The service rate region is introduced recently as an important performance metric for coded distributed systems, which is defined as the set of all data access requests that can be simultaneously handled by the system. This paper studies the problem of designing a coded distributed storage system storing k files where a desired service rate region R of the system is given and the goal is 1) to determine the minimum number of storage nodes n(R) (or a lower bound on n(R)) for serving all demand vectors inside the set R and 2) to design the most storage-efficient redundancy scheme with the service rate region covering R. Towards this goal, we propose three general lower bounds for n(R). Also, for k = 2, we characterize n(R), i.e., we show that the proposed lower bounds are tight via designing a novel storage-efficient redundancy scheme with n(R) storage nodes and the service rate region covering R.

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Section: B Using Geometric Approach To Derive Bounds On N(r)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Storage Schemes for Desired Service Rate Regions

Kazemi¹,

Kurz²,

Soljanin³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Few works have investigated caching schemes specifically taking into account user mobility. [24] proposes a mobilityaware cache placement strategy for Cloud Radio Access Networks. The authors leverage on the user-mobility pattern to analytically estimate the content-request rate in different cells and minimize the network energy consumption.…”

Section: Related Workmentioning

confidence: 99%

The RICH Prefetching in Edge Caches for In-Order Delivery to Connected Cars

Mahmood

Casetti²,

Chiasserini

et al. 2019

IEEE Trans. Veh. Technol.

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Content caching on the edge of 5G networks is an emerging and critical feature to quench the thirst for content of future connected cars. However, the tight packaging of 5G cells, the finite storage capacity at the edge, and the need for content availability while driving motivate the need to develop smart edge caching strategies adapted to the mobility characteristics of connected cars. In this paper, we propose a scheme called RICH (RoadsIde CacHe), which optimally caches content at edge nodes where connected vehicles require it most. In particular, our scheme is designed to ensure in-order delivery of content chunks to end users. Unlike blind popularity decisions, the probabilistic caching used by RICH accounts for the user mobility information that the system can realistically acquire. Furthermore, we provide a complete system architecture and define the protocols through which the different system entities can interact. We assess the performance of our approach against state-of-the-art solutions, under realistic mobility datasets and system scenarios. Our RICH edge caching scheme improves significantly the content availability at the caches and reduces the required backhaul bandwidth, with beneficial effects for both the end users and the network operators.

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“…In [26], a low-complexity greedy algorithm is proposed to minimize the content delivering delay in cooperative caching C-RANs. Energy efficiency (EE) of cache-assisted networks are analysed in [27], [28].…”

mentioning

confidence: 99%

“…Our method is fundamentally different from [12] which studies the latency limit from information-theoretic perspectives. Compared with [26], which studies only uncoded caching at higher layers, we consider both caching strategies jointly with the signal transmission.…”

mentioning

confidence: 99%

Edge-Caching Wireless Networks: Performance Analysis and Optimization

Chatzinotas

Ottersten

2018

IEEE Trans. Wireless Commun.

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Edge-caching has received much attention as an efficient technique to reduce delivery latency and network congestion during peak-traffic times by bringing data closer to end users. Existing works usually design caching algorithms separately from physical layer design. In this paper, we analyse edge-caching wireless networks by taking into account the caching capability when designing the signal transmission.Particularly, we investigate multi-layer caching where both base station (BS) and users are capable of storing content data in their local cache and analyse the performance of edge-caching wireless networks under two notable uncoded and coded caching strategies. Firstly, we calculate backhaul and access throughputs of the two caching strategies for arbitrary values of cache size. The required backhaul and access throughputs are derived as a function of the BS and user cache sizes. Secondly, closedform expressions for the system energy efficiency (EE) corresponding to the two caching methods are derived. Based on the derived formulas, the system EE is maximized via precoding vectors design and optimization while satisfying a predefined user request rate. Thirdly, two optimization problems are proposed to minimize the content delivery time for the two caching strategies. Finally, numerical results are presented to verify the effectiveness of the two caching methods.

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Mobee: Mobility-Aware Energy-Efficient Coded Caching in Cloud Radio Access Networks

Cited by 15 publications

References 15 publications

Efficient Storage Schemes for Desired Service Rate Regions

Efficient Storage Schemes for Desired Service Rate Regions

The RICH Prefetching in Edge Caches for In-Order Delivery to Connected Cars

Edge-Caching Wireless Networks: Performance Analysis and Optimization

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