Proactive scheduling for content pre-fetching in mobile networks

Shoukry, Omar K.; ElMohsen, Mohamed A. Abd; Tadrous, John; Gamal, Hesham El; ElBatt, Tamer; Wanas, Nayer; ElNakieb, Yaser; Khairy, Mohamed M.

doi:10.1109/icc.2014.6883756

Cited by 14 publications

(10 citation statements)

References 13 publications

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“…Inspired by the growing body of evidence that human behavior is highly predictable [3]- [5], and our practical results on proactive WiFi offloading [18], we assume that end users can learn and predict their future consumption and WiFi connection, and consequently apply proactive content download decisions during the off-peak time so as to minimize their daily expected payments by utilizing cheap network prices. In particular, we assume that the peak hour load is decomposed of a number of requests to M uncorrelated data sources (e.g., YouTube, CNN, Netflix, etc.…”

Section: A Proactive Content Downloadmentioning

confidence: 99%

Can carriers make more profit while users save money?

Tadrous

Gamal

Eryılmaz

2014

2014 IEEE International Symposium on Information Theory

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In this work, we investigate the profit maximization problem for wireless network carriers and payment minimization for end users. Motivated by our recent findings on proactive resource allocation, we focus on the scenario whereby end users harness predictable demand and WiFi connectivity in proactive data downloads, to minimize their expected payments. Carriers, on the other hand, utilize smart pricing schemes to differentiate between the off-peak and peak hour prices so as to reduce peak costs and maximize their profit. We formulate the tension between the carrier and end user as a two-player Stackelberg game in which the carrier assigns prices first, then the end user responds with optimized proactive downloads. We explore the equilibrium points under maximum and average price constraints, and study the impact of WiFi availability on the system's performance. In particular, we compare the new equilibria with the baseline scenario of flat pricing and no proactive downloads. Despite the potential uncertainty about future demand, and the freshness of proactively downloaded content, we characterize new equilibria points that yield win-win situation with respect to the baseline equilibrium.

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Section: A Proactive Content Downloadmentioning

confidence: 99%

Can carriers make more profit while users save money?

Tadrous

Gamal

Eryılmaz

2014

2014 IEEE International Symposium on Information Theory

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“…In [6] and [7], a pre-caching scheduler was proposed at social networks given the device profiles. The scheduler decides what contents and when to transmit based on the device profiles.…”

Section: Related Workmentioning

confidence: 99%

Joint optimization for social content delivery in wireless networks

Weng

Baras

2016

2016 IEEE International Conference on Communications (ICC)

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“…Furthermore, it is also arduous for users to find desired data among a mass of data available in the Internet [4]. In general, the special data which catches the interests of users can be provided faster and easier with data recommendation system, since the recommendation strategy is usually welldesigned based on the preferences of users explicitly [5], [6]. Compared to search engines, push is more convenient for less action required, and the quality of data pushed does not rely on the skills and knowledge of the users [4].…”

Section: Introductionmentioning

confidence: 99%

Matching Users’ Preference under Target Revenue Constraints in Data Recommendation Systems

Liu

Dong

Fan

et al. 2019

Entropy

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This paper focuses on the problem of finding a particular data recommendation strategy based on the user preferences and a system expected revenue. To this end, we formulate this problem as an optimization by designing the recommendation mechanism as close to the user behavior as possible with a certain revenue constraint. In fact, the optimal recommendation distribution is the one that is the closest to the utility distribution in the sense of relative entropy and satisfies expected revenue. We show that the optimal recommendation distribution follows the same form as the message importance measure (MIM) if the target revenue is reasonable, i.e., neither too small nor too large. Therefore, the optimal recommendation distribution can be regarded as the normalized MIM, where the parameter, called importance coefficient, presents the concern of the system and switches the attention of the system over data sets with different occurring probability. By adjusting the importance coefficient, our MIM based framework of data recommendation can then be applied to system with various system requirements and data distributions. Therefore, the obtained results illustrate the physical meaning of MIM from the data recommendation perspective and validate the rationality of MIM in one aspect.

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Proactive scheduling for content pre-fetching in mobile networks

Cited by 14 publications

References 13 publications

Can carriers make more profit while users save money?

Can carriers make more profit while users save money?

Joint optimization for social content delivery in wireless networks

Matching Users’ Preference under Target Revenue Constraints in Data Recommendation Systems

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