All one needs to know about fog computing and related edge computing paradigms: A complete survey

Edge side caching assisted device-to-device (D2D) communication has been acknowledged as a promising technique to alleviate the heavy burden of backhaul transmission link and to reduce the network latency. However, the effectiveness of caching strategies at the network edge is highly dependent on the distribution of individual user’s content preference. To fully attain the benefits of edge caching, some proactive mechanisms shall be considered. Among which, recommendation performs noticeably well due to its capability of reshaping the content request probabilities of different users, which in turn affects the cache decision significantly. In this work, we quantitatively investigate how recommendation can be applied to enhance the caching efficiency of D2D enabled wireless content caching networks. And for that, the cache hit ratio maximization problem for a generic network model is formulated taking into account the requirements of each user’s personalized recommendation quality, recommendation quantity and cache capacity. Then, we show that the optimal recommendation and caching policies which jointly maximize the cache efficiency is NP-hard to compute. Further, a time-efficient sub-optimal algorithm is designed, which works in an iterative manner and has provable convergence guarantee as well as polynomial time complexity. Monte-Carlo simulation results demonstrate the convergence performance of our proposed joint decision algorithm and its cache efficiency improvements compared to extensive benchmarks.

Caching Efficiency Maximization for Device-to-Device Communication Networks: A Recommend to Cache Approach

Caching refers to the act of replicating information at a faster (or closer) medium with the purpose of improving performance This deceptively simple idea has given rise to some of the hardest optimization problems in the fields of computer systems, networking, and the Internet; many of which remain unsolved several years after their conception While a wealth of research contributions exists from the topics of memory systems, data centers, Internet traffic, CDNs, and recently wireless networks, the literature is dispersed and overlapping at times In this monograph, the authors focus on the fundamental underlying mathematical models, into a powerful framework for performing optimization of caching systems In doing so they the present the reader with a solid background for the anticipated explosion in caching research, and provide a didactic view into how engineers have managed to infuse mathematical models into the study of caching over the last 40 years Written by leading researchers from academia and industry, this monograph provides students, researchers and practicing engineers with a concise introduction to challenges and solutions for implementing caching in modern computing systems

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Cache Optimization Models and Algorithms

In this paper, we investigate the performance gains that are achievable when jointly controlling (i) in which Small-cell Base Stations (SBSs) mobile users are associated to, (ii) which content items are stored at SBS co-located caches and (iii) which content items are recommended to the mobile users who are associated to different SBSs. We first establish a framework for the joint user association, content caching and recommendations problem, by specifying a set of necessary conditions for all three component functions of the system. Then, we provide a concrete formulation of the joint problem when the objective is to maximize the total hit ratio over all caches. We analyze the problems that emerge as special cases of the joint problem, when one of the three functions is carried out independently, and use them to characterize its complexity. Finally, we propose a heuristic that tackles the joint problem. Proof-of-concept simulations demonstrate that even this simple heuristic outperforms an optimal algorithm that takes only caching and recommendation decisions into account and provide evidence of the achievable performance gains when decisions over all three functions are jointly optimized.

Joint User Association, Content Caching and Recommendations in Wireless Edge Networks

In this paper, we exploit a behavior-shaping proactive mechanism, namely, recommendation, in cache-assisted non-orthogonal multiple access (NOMA) networks, aiming at minimizing the average system’s latency. Thereof, the considered latency consists of two parts, i.e., the backhaul link transmission delay and the content delivery latency. Towards this end, we first examine the expression of system latency, demonstrating how it is critically determined by content cache placement, personalized recommendation, and delivery associated NOMA user pairing and power control strategies. Thereafter, we formulate the minimization problem mathematically taking into account the cache capacity budget, the recommendation-oriented requirements, and the total transmit power constraint, which is a non-convex, multi-timescale, and mixed-integer programming problem. To facilitate the process, we put forth an entirely new paradigm named divide-and-rule. Specifically, we first solve the short-term optimization problem regarding user pairing as well as power allocation and the long-term decision-making problem with respect to recommendation and caching, respectively. On this basis, an iterative algorithm is developed to optimize all the optimization variables alternately. Particularly, for solving the short-timescale problem, graph theory enabled NOMA user grouping and efficient inter-group power control manners are invoked. Meanwhile, a dynamic programming approach and a complexity-controllable swap-then-compare method with convergence insurance are designed to derive the caching and recommendation policies, respectively. From Monte-Carlo simulation, we show the superiority of the proposed joint optimization method in terms of both system latency and cache hit ratio when compared to extensive benchmark strategies.

Joint Content Caching, Recommendation, and Transmission Optimization for Next Generation Multiple Access Networks

Joint caching and recommendation has been recently proposed for increasing the efficiency of mobile edge caching. While previous works assume collaboration between mobile network operators and content providers (who control the recommendation systems), this might be challenging in today's economic ecosystem, with existing protocols and architectures. In this paper, we propose an approach that enables cache-aware recommendations without requiring a network and content provider collaboration. We leverage information provided publicly by the recommendation system, and build a system that provides cache-friendly and high-quality recommendations. We apply our approach to the YouTube service, and conduct measurements on YouTube video recommendations and experiments with video requests, to evaluate the potential gains in the cache hit ratio. Finally, we analytically study the problem of caching optimization under our approach. Our results show that significant caching gains can be achieved in practice; 8 to 10 times increase in the cache hit ratio from cache-aware recommendations, and an extra 2 times increase from caching optimization.

https://dl.acm.org/doi/pdf/10.1145/3229556.3229563

CABaRet: Leveraging Recommendation Systems for Mobile Edge Caching

In this paper we propose that recommendation systems (RSs) for multimedia services should be "QoS-aware", i.e., take into account the expected QoS with which a content can be delivered, to increase the user satisfaction. Network-aware recommendations have been very recently proposed as a promising solution to improve network performance. However, the idea of QoS-aware RSs has been studied from the network perspective. Its feasibility and performance performance advantages for the content-provider or user perspective have only been speculated. Hence, in this paper we aim to provide initial answers for the feasibility of the concept of QoS-aware RS, by investigating its impact on real user experience. To this end, we conduct experiments with real users on a testbed, and present initial experimental results. Our analysis demonstrates the potential of the idea: QoS-aware RSs could be beneficial for both the users (better experience) and content providers (higher user engagement). Moreover, based on the collected dataset, we build statistical models to (i) predict the user experience as a function of QoS, relevance of recommendations (QoR) and user interest, and (ii) provide useful insights for the design of QoS-aware RSs. We believe that our study is an important first step towards QoS-aware recommendations, by providing experimental evidence for their feasibility and benefits, and can help open a future research direction.

Towards QoS-Aware Recommendations

Joint caching and recommendation has been recently proposed as a new paradigm for increasing the efficiency of mobile edge caching. Early findings demonstrate significant gains for the network performance. However, previous works evaluated the proposed schemes exclusively on simulation environments. Hence, it still remains uncertain whether the claimed benefits would change in real settings. In this paper, we propose a methodology that enables to evaluate joint network and recommendation schemes in real content services by only using publicly available information. We apply our methodology to the YouTube service, and conduct extensive measurements to investigate the potential performance gains. Our results show that significant gains can be achieved in practice; e.g., 8 to 10 times increase in the cache hit ratio from cache-aware recommendations. Finally, we build an experimental testbed and conduct experiments with real users; we make available our code and datasets to facilitate further research. To our best knowledge, this is the first realistic evaluation (over a real service, with real measurements and user experiments) of the joint caching and recommendations paradigm. Our findings provide experimental evidence for the feasibility and benefits of this paradigm, validate assumptions of previous works, and provide insights that can drive future research.

Network-aware Recommendations in the Wild: Methodology, Realistic Evaluations, Experiments

Sistemas de recomendação estão cada vez mais presentes na rotina dos usuários da Internet. Portanto, plataformas como Youtube e Netflix procuram melhorar suas recomendações para fornecer uma melhor experiência aos seus usuários. No entanto, a experiência dos usuários depende de diversos fatores. Em particular, sistemas de cache têm uma grande influência na qualidade de experiência (QoE), visto que eles impactam métricas de qualidade de serviço (QoS), como atraso e vazão, experenciadas pelos usuários. Nosso objetivo é estudar a viabilidade de um sistema de recomendação sensível a QoS que maximize a QoE. Para tal, nós conduzimos experimentos com usuários reais com perfis distintos. Cada usuário é solicitado a avaliar diferentes vídeos, que variam em termos de conteúdo e de QoS. Dadas nossas descobertas quanto à relação entre QoS e QoR (quality of recommendation), investigamos seus impactos no modelo de sistema de recomendação. Um classificador baseado em árvore de decisão alcançou acurácia de 77% usando validação cruzada, e nos permite entender melhor o processo de tomada de decisão do usuário.

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Savvas Kastanakis

Papers

CABaRet: Leveraging Recommendation Systems for Mobile Edge Caching

CABaRet: Leveraging Recommendation Systems for Mobile Edge Caching

Towards QoS-Aware Recommendations

Network-aware Recommendations in the Wild: Methodology, Realistic Evaluations, Experiments

Recomendação de Conteúdo e QoE: Um Experimento Quantificando o Papel da QoS nas Preferências por Vídeos