Caching decisions by default seek to maximize some notion of social welfare: the content to be cached is determined so that the maximum possible aggregate demand over all users served by the cache is satisfied. Recommendation systems, on the contrary, are oriented towards user individual preferences: the recommended content should be most appealing to the user so as to elicit further content consumption. In our paper we explore how these, phenomenically conflicting, objectives can be jointly addressed. To this end, we depart radically from current practice with recommender systems, and we approach them as network traffic engineering tools that can actively shape content demand towards optimizing user- and network-centric performance objectives. We formulate the resulting joint theoretical optimization problem of deciding on the cached content and the recommendations to each user so that the cache hit ratio is maximized subject to a maximum tolerable distortion that the recommendation should undergo. We conclude on its complexity, and we propose a practical algorithm for its solution. The algorithm is essentially a form of lightweight control over the user recommendations so that the recommended content is both appealing to the end user and more friendly to the caching system and the network resources.

Caching-aware recommendations: Nudging user preferences towards better caching performance

Caching decisions typically seek to cache content that satisfies the maximum possible demand aggregated over all users. Recommendation systems, on the contrary, focus on individual users and recommend to them appealing content in order to elicit further content consumption. In our paper, we explore how these, phenomenally conflicting, objectives can be jointly addressed. First, we formulate an optimization problem for the joint caching and recommendation decisions, aiming to maximize the cache hit ratio under minimal controllable distortion of the inherent user content preferences by the issued recommendations. Then, we prove that the problem is NP-complete and that its objective function lacks those monotonicity and submodularity properties that would guarantee its approximability. Hence, we proceed to introduce a simpler heuristic algorithm that essentially serves as a form of lightweight control over recommendations so that they are both appealing to end-users and friendly to network resources. Finally, we draw on both analysis and simulations with real and synthetic datasets to evaluate the performance of the algorithm. We point out its fundamental properties, provide bounds for the achieved cache hit ratio, and study its sensitivity to its own as well as system-level parameters.

Jointly Optimizing Content Caching and Recommendations in Small Cell Networks

Today increasingly large volumes of TV and video are distributed over IP-networks and over the Internet. It is therefore essential for traffic and cache management to understand TV program popularity and access patterns in real networks. In this paper we study access patterns in a large TV-on-Demand system over four months. We study user behaviour and program popularity and its impact on caching. The demand varies a lot in daily and weekly cycles. There are large peaks in demand, especially on Friday and Saturday evenings, that need to be handled. We see that the cacheability, the share of requests that are not first-time requests, is very high. Furthermore, there is a small set of programs that account for a large fraction of the requests. We also find that the share of requests for the top most popular programs grows during prime time, and the change rate among them decreases. This is important for caching. The cache hit ratio increases during prime time when the demand is the highest, and aching makes the biggest difference when it matters most. We also study the popularity (in terms of number of requests and rank) of individual programs and how that changes over time. Also, we see that the type of programs offered determines what the access pattern will look like.

Program popularity and viewer behaviour in a large TV-on-demand system

https://biblio.ugent.be/publication/3254441/file/3254472.pdf

Towards a predictive cache replacement strategy for multimedia content

The energy consumption of VoD services arising from storage and transport of video contents stored in different content placement locations is investigated Our results provide insight into content placement strategies that improve energy efficiency

Energy efficient content distribution for VoD services

Due to its native return channel and its ability to easily address each user individually an IPTV system is very well suited to offer on-demand services. Those services are becoming more popular as there is an undeniable trend that users want to watch the offered content when and where it suits them best. Because multicast can no longer be relied upon for such services, as was the case when offering linear-programming TV, this trend risks to increase the traffic unwieldy over some parts of the IPTV network unless caches are deployed in strategic places within it. Since caches are limited in size and the popularity of on-demand content is volatile (i.e., changing over time), it is not straightforward to decide which objects to cache at which moment in time. This paper introduces and studies a caching algorithm that tracks the popularity of objects to make intelligent caching decisions. We will show that when its parameters are set equal or close to their optimal values this algorithm outperforms traditional algorithms as LRU (least-recently used) and LFU (least-frequently used). After a generic study of the algorithm fed by a user demand model that takes the volatility of the objects into account we will discuss two particular cases of an on-demand service, video-on-demand and catch-up TV, for each of which we give guidelines on how to dimension their associated caches.

K. Laevens

Papers

Performance of Caching Algorithms for IPTV On-Demand Services