Proactive Retention-Aware Caching With Multi-Path Routing for Wireless Edge Networks

Shukla, Samta; Bhardwaj, Onkar; Abouzeid, Alhussein A.; Salonidis, Theodoros; He, Ting

doi:10.1109/jsac.2018.2844999

Cited by 47 publications

(23 citation statements)

References 25 publications

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“…The study includes both hop-by-hop routing and source-routing decisions, and proposes distributed and online solution algorithms that achieve constant approximation ratio in polynomial time. Finally, in [28] the femtocaching problem is being extended in the setting where files can be stored for a limited duration at a cache and delivered with multicast. The authors provide performance guarantees for a greedy algorithm that selects jointly caching retention times and routing decisions.…”

Section: E Content Caching and Delivery Techniquesmentioning

confidence: 99%

The Role of Caching in Future Communication Systems and Networks

Paschos

Iosifidis

Tao

et al. 2018

IEEE J. Select. Areas Commun.

238

160

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This paper has the following ambitious goal: to convince the reader that content caching is an exciting research topic for the future communication systems and networks. Caching has been studied for more than 40 years, and has recently received increased attention from industry and academia. Novel caching techniques promise to push the network performance to unprecedented limits, but also pose significant technical challenges. This tutorial provides a brief overview of existing caching solutions, discusses seminal papers that open new directions in caching, and presents the contributions of this Special Issue. We analyze the challenges that caching needs to address today, considering also an industry perspective, and identify bottleneck issues that must be resolved to unleash the full potential of this promising technique.

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Section: E Content Caching and Delivery Techniquesmentioning

confidence: 99%

The Role of Caching in Future Communication Systems and Networks

Paschos

Iosifidis

Tao

et al. 2018

IEEE J. Select. Areas Commun.

238

160

View full text Add to dashboard Cite

show abstract

“…However, a fundamental difference between data files and service programs is that data files can be partitioned in arbitrary ways without affecting the cache efficiency. Therefore, existing work on data caching usually considers identically sized files/contents [25], [26], which is inadequate for the placement of service programs. Mathematically, the data caching problem has been extended to consider nonpartitionable files of different sizes in [27], which requires that the cost (opposite of the reward) is related to distances between nodes defined on a metric space.…”

Section: Related Workmentioning

confidence: 99%

Service Placement with Provable Guarantees in Heterogeneous Edge Computing Systems

Pasteris

Wang

Herbster

et al. 2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

Self Cite

136

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Mobile edge computing (MEC) is a promising technique for providing low-latency access to services at the network edge. The services are hosted at various types of edge nodes with both computation and communication capabilities. Due to the heterogeneity of edge node characteristics and user locations, the performance of MEC varies depending on where the service is hosted. In this paper, we consider such a heterogeneous MEC system, and focus on the problem of placing multiple services in the system to maximize the total reward. We show that the problem is NP-hard via reduction from the set cover problem, and propose a deterministic approximation algorithm to solve the problem, which has an approximation ratio that is not worse than 1 − e −1 /4. The proposed algorithm is based on two subroutines that are suitable for small and arbitrarily sized services, respectively. The algorithm is designed using a novel way of partitioning each edge node into multiple slots, where each slot contains one service. The approximation guarantee is obtained via a specialization of the method of conditional expectations, which uses a randomized procedure as an intermediate step. In addition to theoretical guarantees, simulation results also show that the proposed algorithm outperforms other state-of-the-art approaches.

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“…Note that (12) does not depend on the particular set K and is the same for all K of the same size. Furthermore, (12) follows from (11) ifλ c = 1/m for all c ∈ C. Thus, the unif policy treats all contents to be equally popular. It is easy to implement this policy in practice since it does not require the knowledge of the popularities of the contents.…”

Section: Replication Algorithmsmentioning

confidence: 99%