Semigradient-Based Cooperative Caching Algorithm for Mobile Social Networks

Wu, Yecheng; Yao, Sha; Yang, Yang; Hu, Zeming; Wang, Cheng-Xiang

doi:10.1109/glocom.2016.7842260

Cited by 14 publications

(25 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering that the tastes of different users are not identical, caching policies were optimized to minimize the average delay of cache-enabled D2D communications in [25] and to maximize the cache hit rate of mobile social networks in [26], by assuming user preferences as Zipf distributions with different ranks. However, both works assume that all users have the same active level, do not validate the assumption for user preference, and do not show the gain over caching with popularity.…”

Section: B Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Caching Policy for Cache-Enabled D2D Communications by Learning User Preference

Chen

Yang

2018

IEEE Trans. Commun.

107

View full text Add to dashboard Cite

Prior works in designing caching policy do not distinguish content popularity with user preference.In this paper, we illustrate the caching gain by exploiting individual user behavior in sending requests.After showing the connection between the two concepts, we provide a model for synthesizing user preference from content popularity. We then optimize the caching policy with the knowledge of user preference and active level to maximize the offloading probability for cache-enabled device-to-device communications, and develop a low-complexity algorithm to find the solution. In order to learn user preference, we model the user request behavior resorting to probabilistic latent semantic analysis, and learn the model parameters by expectation maximization algorithm. By analyzing a Movielens dataset, we find that the user preferences are less similar, and the active level and topic preference of each user change slowly over time. Based on this observation, we introduce a prior knowledge based learning algorithm for user preference, which can shorten the learning time. Simulation results show remarkable performance gain of the caching policy with user preference over existing policy with content popularity, both with realistic dataset and synthetic data validated by the real dataset.

show abstract

Section: B Related Workmentioning

confidence: 99%

“…These priori works assume known user preference [25][26][27]. To facilitate proactive caching, user preference needs to be predicted, which is a key task in recommendation problem.…”

Section: B Related Workmentioning

confidence: 99%

Caching Policy for Cache-Enabled D2D Communications by Learning User Preference

Chen

Yang

2018

IEEE Trans. Commun.

107

View full text Add to dashboard Cite

show abstract

“…Results on caching video files and their benefits are presented in [11] - [13], while the advantages of data caching and content distribution in device-to-device (D2D) communications are studied in [14] - [16]. In [17], proactive caching is shown to increase the energy efficiency of D2D communications, while the advantages of caching on mobile social networks is reported in [18].…”

Section: Introductionmentioning

confidence: 99%

Caching With Time-Varying Popularity Profiles: A Learning-Theoretic Perspective

Bharath

Nagananda

Gündüz

et al. 2018

IEEE Trans. Commun.

View full text Add to dashboard Cite

Content caching at the small-cell base stations (sBSs) in a heterogeneous wireless network is considered. A cost function is proposed that captures the backhaul link load called the "offloading loss", which measures the fraction of the requested files that are not available in the sBS caches.As opposed to the previous approaches that consider time-invariant and perfectly known popularity profile, caching with non-stationary and statistically dependent popularity profiles (assumed unknown, and hence, estimated) is studied from a learning-theoretic perspective. A probably approximately correct result is derived, which presents a high probability bound on the offloading loss difference, i.e., the error between the estimated and the optimal offloading loss. The difference is a function of the Rademacher complexity, the β−mixing coefficient, the number of time slots, and a measure of discrepancy between the estimated and true popularity profiles. A cache update algorithm is proposed, and simulation results are presented to show its superiority over periodic updates. The performance analyses for Bernoulli and Poisson request models are also presented. Index TermsCaching; time-varying popularity profiles; probably approximately correct (PAC) learning.

show abstract

“…On the other hand, the importance of personalized file preferences in content-centric networks was studied in [6], [14], [15], [16]. In [6], a low-complexity semigradient-based cooperative caching scheme was designed in mobile social networks by incorporating probabilistic modeling of user mobility and heterogeneous interest patterns. It was assumed in [6] that all users have the same activity level (i.e., the same number of requests generated by each user) and the user file preferences are known, which may be hardly realistic.…”

mentioning

confidence: 99%

“…In [6], a low-complexity semigradient-based cooperative caching scheme was designed in mobile social networks by incorporating probabilistic modeling of user mobility and heterogeneous interest patterns. It was assumed in [6] that all users have the same activity level (i.e., the same number of requests generated by each user) and the user file preferences are known, which may be hardly realistic. Later, the optimal caching policy via a greedy algorithm for cache-enabled device-to-device (D2D) communications was presented in [14] by modeling the behavior of user requests resorting to probabilistic latent semantic analysis.…”

mentioning

confidence: 99%

A Personalized Preference Learning Framework for Caching in Mobile Networks

Malik

Kim

et al. 2021

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

This paper comprehensively studies a content-centric mobile network based on a preference learning framework, where each mobile user is equipped with a finite-size cache. We consider a practical scenario where each user requests a content file according to its own preferences, which is motivated by the existence of heterogeneity in file preferences among different users. Under our model, we consider a single-hop-based device-to-device (D2D) content delivery protocol and characterize the average hit ratio for the following two file preference cases: the personalized file preferences and the common file preferences. By assuming that the model parameters such as user activity levels, user file preferences, and file popularity are unknown and thus need to be inferred, we present a collaborative filtering (CF)-based approach to learn these parameters. Then, we reformulate the hit ratio maximization problems into a submodular function maximization and propose two computationally efficient algorithms including a greedy approach to efficiently solve the cache allocation problems. We analyze the computational complexity of each algorithm. Moreover, we analyze the corresponding level of the approximation that our greedy algorithm can achieve compared to the optimal solution. Using a real-world dataset, we demonstrate that the proposed framework employing the personalized file preferences brings substantial gains over its counterpart for various system parameters. Index TermsCaching, collaborative filtering, learning, mobile network, personalized file preferences. ✦ INTRODUCTIONT He growing trend in mobile data traffic drives a need of a new wireless communication technology paradigm in which radios are capable of learning and decision making in order to autonomously determine the optimal system configurations. In this context, equipping the communication functionality with machine learning-based or data-driven algorithms has received a considerable attention both in academia as well as in industrial communities. Prior WorkCache-enabled (or content-centric) wireless systems are equipped with finite storage capacity, which restricts us from storing the entire content library at the local cache. In order to bring content objects closer to requesting users, deciding which content needs to be cached at which user's or helper's cache plays a crucial role on the overall performance of content-centric wirelss networks. This cache placement problem has recently attracted a wide attention. In general, the prior work on caching can be divided into two categories: caching at small-cell base stations (or helper nodes) [1], [2], [3], [4] and caching at users (or devices) [5], [6], [7], [8]. Since the optimal caching problems in [1], [2], [3], [4], [5], [6] cannot be solvable in polynomial time, approximate solutions with performance guarantees were presented. It was shown in [1],[5] that the optimal cache placement problem fell in the category of monotone submodular maximization over a matroid constraint [9], and a proposed time-efficie...

show abstract

Semigradient-Based Cooperative Caching Algorithm for Mobile Social Networks

Cited by 14 publications

References 13 publications

Caching Policy for Cache-Enabled D2D Communications by Learning User Preference

Caching Policy for Cache-Enabled D2D Communications by Learning User Preference

Caching With Time-Varying Popularity Profiles: A Learning-Theoretic Perspective

A Personalized Preference Learning Framework for Caching in Mobile Networks

Contact Info

Product

Resources

About