Intelligent Caching Algorithms in Heterogeneous Wireless Networks with Uncertainty

Hu, Bingshan; Chen, Yunjin; Huang, Zhiming; Mehta, Nishant A.; Pan, Jianping

doi:10.1109/icdcs.2019.00154

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…Hopefully, in [92], two effective self-adapting algorithms were proposed. Two constraints like traffic congestion and radio frequency have to be met in response to users' demands.…”

Section: Content Request Analysismentioning

confidence: 99%

A Survey on Caching in Mobile Edge Computing

Zhao

Zhang

Zhou

et al. 2021

Wireless Communications and Mobile Computing

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Boosted by the visions of 5G technology, along with the proliferation of IoT devices, we are lucky to bear witness of a tremendous shift within the Internet, mobile computing, and ubiquitous applications pervading people’s daily life. To catch up with the speed of this evolution, new architectures, which enable us to decentralize and focus more on the edge of the network, have to show up. To effectively address the record-breaking growth of data traffic, we also have to come up with new caching mechanisms in coping with users’ demanding QoE along with other aspects, such as data privacy and energy efficiency. In this paper, we intend to begin with reviews on edge caching. First, we make a detailed overview of mobile edge caching. Then, we move on to the QoS and QoE part, discussing the related literature concerning these topics. We then start to address factors concerning edge caching and QoE. Four cutting-edge applications utilizing these technologies with challenges posed by current network architectures are made clear. Our paper closes with some future research directions.

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“…Hopefully, in [92], two effective self-adapting algorithms were proposed. Two constraints like traffic congestion and radio frequency have to be met in response to users' demands.…”

Section: Content Request Analysismentioning

confidence: 99%

A Survey on Caching in Mobile Edge Computing

Zhao

Zhang

Zhou

et al. 2021

Wireless Communications and Mobile Computing

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“…However, in practice, some of the arms may not be available in some rounds, for example, some items for online sales are out of stock temporarily. Therefore, a bunch of literature studied the setting of MAB with sleeping arms (SMAB) [Kleinberg et al, 2010;Chatterjee et al, 2017;Hu et al, 2019a;Kale et al, 2016;Neu and Valko, 2014]. In the SMAB setting, the set of available arms for each round, i.e., the availability set, can vary.…”

Section: Related Workmentioning

confidence: 99%

“…Regarding the combinatorial SMAB setting (CSMAB), some negative results are shown in [Kale et al, 2016], i.e., efficient no-regret learning algorithms sometimes are computationally hard. However, for some settings such as stochastic availability and stochastic reward, it is shown that it is still possible to devise efficient learning algorithms with good theoretical guarantees [Hu et al, 2019a;Li et al, 2019]. More importantly, in the work of [Li et al, 2019], they considered a new variant called the combinatorial sleeping MAB with long-term fairness constraints (CSMAB-F).…”

Section: Related Workmentioning

confidence: 99%

Thompson Sampling for Combinatorial Semi-bandits with Sleeping Arms and Long-Term Fairness Constraints

Huang,

Xu,

et al. 2020

Preprint

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We study the combinatorial sleeping multi-armed semi-bandit problem with long-term fairness constraints (CSMAB-F). To address the problem, we adopt Thompson Sampling (TS) to maximize the total rewards and use virtual queue techniques to handle the fairness constraints, and design an algorithm called TS with beta priors and Bernoulli likelihoods for CSMAB-F (TSCSF-B). Further, we prove TSCSF-B can satisfy the fairness constraints, and the time-averaged regret is upper bounded by, where N is the total number of arms, m is the maximum number of arms that can be pulled simultaneously in each round (the cardinality constraint) and η is the parameter trading off fairness for rewards. By relaxing the fairness constraints (i.e., let η → ∞), the bound boils down to the first problem-independent bound of TS algorithms for combinatorial sleeping multi-armed semi-bandit problems. Finally, we perform numerical experiments and use a high-rating movie recommendation application to show the effectiveness and efficiency of the proposed algorithm.

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