Analysis and Optimization of Caching and Multicasting in Large-Scale Cache-Enabled Information-Centric Networks

Cui, Ying; Wu, Yueping; Jiang, Dongdong

doi:10.1109/glocom.2014.7417114

Cited by 3 publications

(5 citation statements)

References 10 publications

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“…The above framework can be further extended to MEC systems with multiple servers where each server can serve multiple users and each user can offload computation to multiple edge servers. The fundamental problem is similar to that of the cache-enabled HetNets [170], that is, how to spatially distribute the database over heterogeneous edge servers under both storage and computation-load constraints on each of them, for increasing network-wide revenue. Intuitively, for each MEC server, it is desirable to spare more storage to cache the database of the most popular applications in its cell, and it also needs to utilize partial storage to accommodate less popular ones, whose computation performance will be further improved by cooperative caching in different MEC servers.…”

Section: B Cache-enabled Mecmentioning

confidence: 99%

A Survey on Mobile Edge Computing: The Communication Perspective

Mao

You

Zhang

et al. 2017

IEEE Commun. Surv. Tutorials

4,222

1,998

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Abstract-Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discusse a set of issues, challenges and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

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Section: B Cache-enabled Mecmentioning

confidence: 99%

A Survey on Mobile Edge Computing: The Communication Perspective

Mao

You

Zhang

et al. 2017

IEEE Commun. Surv. Tutorials

4,222

1,998

View full text Add to dashboard Cite

show abstract

“…where (6a) is equivalent to the cache capacity constraint (i.e., the number of cached file cannot exceed the cache capacity) as proved in [4], and (6b) is the probability constraint. This problem is not concave in general, because the active probability of helper p a,2 in the objective function is a complicated function of q f,2 as shown in (5), which makes the global optimal solution hard to obtain.…”

Section: Optimal Caching Policymentioning

confidence: 99%

“…However, the optimal caching probability is not obtained with closed-form, which makes it hard to gain useful insights into the impacts of various system parameters. In [5], the optimal caching probability maximizing the successful transmission probability in a homogeneous network was obtained in closedform when user density approaches infinity. In [6], content placement was optimized to maximize the traffic offloaded to helpers and cache-enabled users, but the links among helpers and users are assumed interference-free.…”

Section: Introductionmentioning

confidence: 99%

Optimal Content Placement for Offloading in Cache-Enabled Heterogeneous Wireless Networks

Liu

Yang

2016

2016 IEEE Global Communications Conference (GLOBECOM)

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Abstract-Caching at base stations (BSs) is a promising way to offload traffic and eliminate backhaul bottleneck in heterogeneous networks (HetNets). In this paper, we investigate the optimal content placement maximizing the successful offloading probability in a cache-enabled HetNet where a tier of multiantenna macro BSs (MBSs) is overlaid with a tier of helpers with caches. Based on probabilistic caching framework, we resort to stochastic geometry theory to derive the closed-form successful offloading probability and formulate the caching probability optimization problem, which is not concave in general. In two extreme cases with high and low user-to-helper density ratios, we obtain the optimal caching probability and analyze the impacts of BS density and transmit power of the two tiers and the signal-tointerference-plus-noise ratio (SINR) threshold. In general case, we obtain the optimal caching probability that maximizes the lower bound of successful offloading probability and analyze the impact of user density. Simulation and numerical results show that when the ratios of MBS-to-helper density, MBS-tohelper transmit power and user-to-helper density, and the SINR threshold are large, the optimal caching policy tends to cache the most popular files everywhere.

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“…However, the work in [5]- [7] considers a single channel system in a fully loaded SBS scenario, which contradicts with the typical multi-channel capabilities of cellular networks and thus leads to a pessimistic performance assessment. A joint optimal file placement and multicasting in a multi-channel system is proposed in [8]. However, [8] follows a rigid frequency reuse scheme, which is well-known to underutilize the spectrum resources [9].…”

Section: Introductionmentioning

confidence: 99%

“…A joint optimal file placement and multicasting in a multi-channel system is proposed in [8]. However, [8] follows a rigid frequency reuse scheme, which is well-known to underutilize the spectrum resources [9]. In [10], we analyzed the performance of cache-enabled networks with opportunistic spectrum access in a multi-channel environment.…”

Section: Introductionmentioning

confidence: 99%

Optimal Caching in Multicast 5G Networks with Opportunistic Spectrum Access

Emara

ElSawy

Sorour

et al. 2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

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Cache-enabled small base station (SBS) densification is foreseen as a key component of 5G cellular networks. This architecture enables storing popular files at the network edge (i.e., SBS caches), which empowers local communication and alleviates traffic congestions at the core/backhaul network. This paper develops a mathematical framework, based on stochastic geometry, to characterize the hit probability of a cache-enabled multicast 5G network with SBS multi-channel capabilities and opportunistic spectrum access. To this end, we first derive the hit probability by characterizing opportunistic spectrum access success probabilities, service distance distributions, and coverage probabilities. The optimal caching distribution to maximize the hit probability is then computed. The performance and trade-offs of the derived optimal caching distributions are then assessed and compared with two widely employed caching distribution schemes, namely uniform and Zipf caching, through numerical results and extensive simulations. It is shown that the Zipf caching almost optimal only in scenarios with large number of available channels and large cache sizes.

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Analysis and Optimization of Caching and Multicasting in Large-Scale Cache-Enabled Information-Centric Networks

Cited by 3 publications

References 10 publications

A Survey on Mobile Edge Computing: The Communication Perspective

A Survey on Mobile Edge Computing: The Communication Perspective

Optimal Content Placement for Offloading in Cache-Enabled Heterogeneous Wireless Networks

Optimal Caching in Multicast 5G Networks with Opportunistic Spectrum Access

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