2013/04/21 by John Tadrous, Atilla Eryılmaz, Tadrous, John +4 · 4 citations
Computer Science · Engineering · Mathematics · #Caching and Content Delivery #Computer network #Computer science #Cost reduction #Download #FOS: Computer and information sciences #Flexibility (engineering) #Green IT and Sustainability #Information Theory (cs.IT) #Mobile device #Networking and Internet Architecture (cs.NI) #Opportunistic and Delay-Tolerant Networks #Predictability #World Wide Web #cs.IT #cs.NI #math.IT
paper · pdf · doi:10.48550/arxiv.1304.5745
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2013/04/21 · arxiv created 2014/12/28 · arxiv updated 2014/12/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this work, we propose and study optimal proactive resource allocation and demand shaping for data networks. Motivated by the recent findings on the predictability of human behavior patterns in data networks, and the emergence of highly capable handheld devices, our design aims to smooth out the network traffic over time and minimize the data delivery costs. Our framework utilizes proactive data services as well as smart content recommendation schemes for shaping the demand. Proactive data services take place during the off-peak hours based on a statistical prediction of a demand profile for each user, whereas smart content recommendation assigns modified valuations to data items so as to render the users' demand less uncertain. Hence, our recommendation scheme aims to boost the performance of proactive services within the allowed flexibility of user requirements. We conduct theoretical performance analysis that quantifies the leveraged cost reduction through the proposed framework. We show that the cost reduction scales at the same rate as the cost function scales with the number of users. Further, we prove that demand shaping through smart recommendation strictly reduces the incurred cost even below that of proactive downloads without recommendation.