Mobile edge caching enables content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case where the model noise is zero-mean, a ridge regression based online algorithm with positive perturbation is proposed. Regret analysis indicates that the proposed algorithm asymptotically approaches the optimal caching strategy in the long run. When the noise structure is unknown, an @math filter based online algorithm is further proposed by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases, and hence are robust to the time-varying user demands. The underlying causes of estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments on real world dataset are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving content hit rate that is comparable to that via the hindsight optimal strategy.
In this paper, we exploit locational features for context differentiation. Locational information can be easily obtained, for example, users attached to different ENs are naturally divided into geographical groups. Based on which we investigate the location-aware caching problem with unknown and time-varying content popularity profile. By modeling user demand as linear combination of location features and content attributes, our previous work has addressed the content popularity prediction problem with the assumption that the model noise is zero-mean @cite_25 . As an extension, this paper additionally considers the practical scenario, where noise structure is unknown . Specifically, a robust prediction algorithm is proposed with detailed theoretical caching performance analysis. The proposed algorithm is robust and practical as it guarantees prediction accuracy regardless of the noise statistics. Additionally, numerical analysis and comparison on the root causes of estimation errors of both algorithms are presented. Much extensive experiments are conducted to validate the performance of the proposed algorithms.
@cite_25
2756113194
Mobile edge caching enables content delivery directly within the radio access network, which effectively alleviates the backhaul burden and reduces round-trip latency. To fully exploit the edge resources, the most popular contents should be identified and cached. Observing that content popularity varies greatly at different locations, to maximize local hit rate, this paper proposes an online learning algorithm that dynamically predicts content hit rate, and makes location-differentiated caching decisions. Specifically, a linear model is used to estimate the future hit rate. Considering the variations in user demand, a perturbation is added to the estimation to account for uncertainty. The proposed learning algorithm requires no training phase, and hence is adaptive to the time-varying content popularity profile. Theoretical analysis indicates that the proposed algorithm asymptotically approaches the optimal policy in the long term. Extensive simulations based on real world traces show that, the proposed algorithm achieves higher hit rate and better adaptiveness to content popularity fluctuation, compared with other schemes. Optimal cache content placement in a wireless small cell base station (sBS) with limited backhaul capacity is studied. The sBS has a large cache memory and provides content-level selective offloading by delivering high data rate contents to users in its coverage area. The goal of the sBS content controller (CC) is to store the most popular contents in the sBS cache memory such that the maximum amount of data can be fetched directly form the sBS, not relying on the limited backhaul resources during peak traffic periods. If the popularity profile is known in advance, the problem reduces to a knapsack problem. However, it is assumed in this work that, the popularity profile of the files is not known by the CC, and it can only observe the instantaneous demand for the cached content. Hence, the cache content placement is optimised based on the demand history. By refreshing the cache content at regular time intervals, the CC tries to learn the popularity profile, while exploiting the limited cache capacity in the best way possible. Three algorithms are studied for this cache content placement problem, leading to different exploitation-exploration trade-offs. We provide extensive numerical simulations in order to study the time-evolution of these algorithms, and the impact of the system parameters, such as the number of files, the number of users, the cache size, and the skewness of the popularity profile, on the performance. It is shown that the proposed algorithms quickly learn the popularity profile for a wide range of system parameters. Content caching in small base stations or wireless infostations is considered to be a suitable approach to improve the efficiency in wireless content delivery. Placing the optimal content into local caches is crucial due to storage limitations, but it requires knowledge about the content popularity distribution, which is often not available in advance. Moreover, local content popularity is subject to fluctuations, since mobile users with different interests connect to the caching entity over time. Which content a user prefers may depend on the user’s context. In this paper, we propose a novel algorithm for context-aware proactive caching. The algorithm learns context-specific content popularity online by regularly observing context information of connected users, updating the cache content and observing cache hits subsequently. We derive a sublinear regret bound, which characterizes the learning speed and proves that our algorithm converges to the optimal cache content placement strategy in terms of maximizing the number of cache hits. Furthermore, our algorithm supports service differentiation by allowing operators of caching entities to prioritize customer groups. Our numerical results confirm that our algorithm outperforms state-of-the-art algorithms in a real world data set, with an increase in the number of cache hits of at least 14 . We present a new algorithm for inferring the home location of Twitter users at different granularities, including city, state, time zone, or geographic region, using the content of users’ tweets and their tweeting behavior. Unlike existing approaches, our algorithm uses an ensemble of statistical and heuristic classifiers to predict locations and makes use of a geographic gazetteer dictionary to identify place-name entities. We find that a hierarchical classification approach, where time zone, state, or geographic region is predicted first and city is predicted next, can improve prediction accuracy. We have also analyzed movement variations of Twitter users, built a classifier to predict whether a user was travelling in a certain period of time, and use that to further improve the location detection accuracy. Experimental evidence suggests that our algorithm works well in practice and outperforms the best existing algorithms for predicting the home location of Twitter users. Online social networks are now a popular way for users to connect, express themselves, and share content. Users in today's online social networks often post a profile, consisting of attributes like geographic location, interests, and schools attended. Such profile information is used on the sites as a basis for grouping users, for sharing content, and for suggesting users who may benefit from interaction. However, in practice, not all users provide these attributes. In this paper, we ask the question: given attributes for some fraction of the users in an online social network, can we infer the attributes of the remaining users? In other words, can the attributes of users, in combination with the social network graph, be used to predict the attributes of another user in the network? To answer this question, we gather fine-grained data from two social networks and try to infer user profile attributes. We find that users with common attributes are more likely to be friends and often form dense communities, and we propose a method of inferring user attributes that is inspired by previous approaches to detecting communities in social networks. Our results show that certain user attributes can be inferred with high accuracy when given information on as little as 20 of the users. Content Centric Networking (CCN) is a recently proposed information-centric Internet architecture in which the main network abstraction is represented by location-agnostic content identifiers instead of node identifiers. In CCN each content object is divided into packet-size chunks. When a content object is transferred, routers on the path can cache single chunks which they can use to serve subsequent requests from other users. Since content chunks in CCN may be retrieved from a number of different nodes caches, implicit-feedback transport protocols will not be able to work efficiently, because it is not possible to set an appropriate timeout value based on RTT estimations given that the data source may change frequently during a flow. In order to address this problem, we propose in this paper a scalable, implicit-feedback congestion control protocol, capable of coping with RTT unpredictability using a novel anticipated interests mechanism to predict the location of chunks before they are actually served. Our evaluation shows that our protocol outperforms similar receiver-driven protocols, in particular when content chunks are scattered across network paths due to reduced cache sizes, long-tail content popularity distribution or the adoption of specific caching policies. With the availability of cheap location sensors, geotagging of messages in online social networks is proliferating. For instance, Twitter, Facebook, Foursquare, and Google+ provide these services both explicitly by letting users choose their location or implicitly via a sensor. This paper presents an integrated generative model of location and message content. That is, we provide a model for combining distributions over locations, topics, and over user characteristics, both in terms of location and in terms of their content preferences. Unlike previous work which modeled data in a flat pre-defined representation, our model automatically infers both the hierarchical structure over content and over the size and position of geographical locations. This affords significantly higher accuracy --- location uncertainty is reduced by 40 relative to the best previous results [21] achieved on location estimation from Tweets. We achieve this goal by proposing a new statistical model, the nested Chinese Restaurant Franchise (nCRF), a hierarchical model of tree distributions. Much statistical structure is shared between users. That said, each user has his own distribution over interests and places. The use of the nCRF allows us to capture the following effects: (1) We provide a topic model for Tweets; (2) We obtain location specific topics; (3) We infer a latent distribution of locations; (4) We provide a joint hierarchical model of topics and locations; (5) We infer personalized preferences over topics and locations within the above model. In doing so, we are both able to obtain accurate estimates of the location of a user based on his tweets and to obtain a detailed estimate of a geographical language model. In this work we consider the problem of an optimal geographic placement of content in wireless cellular networks modelled by Poisson point processes. Specifically, for the typical user requesting some particular content and whose popularity follows a given law (e.g. Zipf), we calculate the probability of finding the content cached in one of the base stations. Wireless coverage follows the usual signal-to-interference-and noise ratio (SINR) model, or some variants of it. We formulate and solve the problem of an optimal randomized content placement policy, to maximize the user's hit probability. The result dictates that it is not always optimal to follow the standard policy “cache the most popular content, everywhere”. In fact, our numerical results regarding three different coverage scenarios, show that the optimal policy significantly increases the chances of hit under high-coverage regime, i.e., when the probabilities of coverage by more than just one station are high enough. The delivery of video content is expected to gain huge momentum, fueled by the popularity of user-generated clips, growth of VoD libraries, and wide-spread deployment of IPTV services with features such as CatchUp PauseLive TV and NPVR capabilities. The time-shifted' nature of these personalized applications defies the broadcast paradigm underlying conventional TV networks, and increases the overall bandwidth demands by orders of magnitude. Caching strategies provide an effective mechanism for mitigating these massive bandwidth requirements by replicating the most popular content closer to the network edge, rather than storing it in a central site. The reduction in the traffic load lessens the required transport capacity and capital expense, and alleviates performance bottlenecks. In the present paper, we develop light-weight cooperative cache management algorithms aimed at maximizing the traffic volume served from cache and minimizing the bandwidth cost. As a canonical scenario, we focus on a cluster of distributed caches, either connected directly or via a parent node, and formulate the content placement problem as a linear program in order to benchmark the globally optimal performance. Under certain symmetry assumptions, the optimal solution of the linear program is shown to have a rather simple structure. Besides interesting in its own right, the optimal structure offers valuable guidance for the design of low-complexity cache management and replacement algorithms. We establish that the performance of the proposed algorithms is guaranteed to be within a constant factor from the globally optimal performance, with far more benign worst-case ratios than in prior work, even in asymmetric scenarios. Numerical experiments for typical popularity distributions reveal that the actual performance is far better than the worst-case conditions indicate. Geographical location is vital to geospatial applications like local search and event detection. In this paper, we investigate and improve on the task of text-based geolocation prediction of Twitter users. Previous studies on this topic have typically assumed that geographical references (e.g., gazetteer terms, dialectal words) in a text are indicative of its author's location. However, these references are often buried in informal, ungrammatical, and multilingual data, and are therefore non-trivial to identify and exploit. We present an integrated geolocation prediction framework and investigate what factors impact on prediction accuracy. First, we evaluate a range of feature selection methods to obtain "location indicative words". We then evaluate the impact of nongeotagged tweets, language, and user-declared metadata on geolocation prediction. In addition, we evaluate the impact of temporal variance on model generalisation, and discuss how users differ in terms of their geolocatability. We achieve state-of-the-art results for the text-based Twitter user geolocation task, and also provide the most extensive exploration of the task to date. Our findings provide valuable insights into the design of robust, practical text-based geolocation prediction systems. Small cells constitute a promising solution for managing the mobile data growth that has overwhelmed network operators. Local caching of popular content items at the small cell base stations (SBSs) has been proposed to decrease the costly transmissions from the macrocell base stations without requiring high capacity backhaul links for connecting the SBSs with the core network. However, the caching policy design is a challenging problem especially if one considers realistic parameters such as the bandwidth capacity constraints of the SBSs that can be reached in congested urban areas. We consider such a scenario and formulate the joint routing and caching problem aiming to maximize the fraction of content requests served locally by the deployed SBSs. This is an NP-hard problem and, hence, we cannot obtain an optimal solution. Thus, we present a novel reduction to a variant of the facility location problem, which allows us to exploit the rich literature of it, to establish algorithms with approximation guarantees for our problem. Although the reduction does not ensure tight enough bounds in general, extensive numerical results reveal a near-optimal performance that is even up to 38 better compared to conventional caching schemes using realistic system settings. Geographical characteristics derived from the historical check-in data have been reported effective in improving location recommendation accuracy. However, previous studies mainly exploit geographical characteristics from a user's perspective, via modeling the geographical distribution of each individual user's check-ins. In this paper, we are interested in exploiting geographical characteristics from a location perspective, by modeling the geographical neighborhood of a location. The neighborhood is modeled at two levels: the instance-level neighborhood defined by a few nearest neighbors of the location, and the region-level neighborhood for the geographical region where the location exists. We propose a novel recommendation approach, namely Instance-Region Neighborhood Matrix Factorization (IRenMF), which exploits two levels of geographical neighborhood characteristics: a) instance-level characteristics, i.e., nearest neighboring locations tend to share more similar user preferences; and b) region-level characteristics, i.e., locations in the same geographical region may share similar user preferences. In IRenMF, the two levels of geographical characteristics are naturally incorporated into the learning of latent features of users and locations, so that IRenMF predicts users' preferences on locations more accurately. Extensive experiments on the real data collected from Gowalla, a popular LBSN, demonstrate the effectiveness and advantages of our approach. User profiling is crucial to many online services. Several recent studies suggest that demographic attributes are predictable from different online behavioral data, such as users' "Likes" on Facebook, friendship relations, and the linguistic characteristics of tweets. But location check-ins, as a bridge of users' offline and online lives, have by and large been overlooked in inferring user profiles. In this paper, we investigate the predictive power of location check-ins for inferring users' demographics and propose a simple yet general location to profile (L2P) framework. More specifically, we extract rich semantics of users' check-ins in terms of spatiality, temporality, and location knowledge, where the location knowledge is enriched with semantics mined from heterogeneous domains including both online customer review sites and social networks. Additionally, tensor factorization is employed to draw out low dimensional representations of users' intrinsic check-in preferences considering the above factors. Meanwhile, the extracted features are used to train predictive models for inferring various demographic attributes. We collect a large dataset consisting of profiles of 159,530 verified users from an online social network. Extensive experimental results based upon this dataset validate that: 1) Location check-ins are diagnostic representations of a variety of demographic attributes, such as gender, age, education background, and marital status; 2) The proposed framework substantially outperforms compared models for profile inference in terms of various evaluation metrics, such as precision, recall, F-measure, and AUC. We present a new algorithm for inferring the home locations of Twitter users at different granularities, such as city, state, or time zone, using the content of their tweets and their tweeting behavior. Unlike existing approaches, our algorithm uses an ensemble of statistical and heuristic classifiers to predict locations. We find that a hierarchical classification approach can improve prediction accuracy. Experimental evidence suggests that our algorithm works well in practice and outperforms the best existing algorithms for predicting the location of Twitter users. Micro-blogging services have become indispensable communication tools for online users for disseminating breaking news, eyewitness accounts, individual expression, and protest groups. Recently, Twitter, along with other online social networking services such as Foursquare, Gowalla, Facebook and Yelp, have started supporting location services in their messages, either explicitly, by letting users choose their places, or implicitly, by enabling geo-tagging, which is to associate messages with latitudes and longitudes. This functionality allows researchers to address an exciting set of questions: 1) How is information created and shared across geographical locations, 2) How do spatial and linguistic characteristics of people vary across regions, and 3) How to model human mobility. Although many attempts have been made for tackling these problems, previous methods are either complicated to be implemented or oversimplified that cannot yield reasonable performance. It is a challenge task to discover topics and identify users' interests from these geo-tagged messages due to the sheer amount of data and diversity of language variations used on these location sharing services. In this paper we focus on Twitter and present an algorithm by modeling diversity in tweets based on topical diversity, geographical diversity, and an interest distribution of the user. Furthermore, we take the Markovian nature of a user's location into account. Our model exploits sparse factorial coding of the attributes, thus allowing us to deal with a large and diverse set of covariates efficiently. Our approach is vital for applications such as user profiling, content recommendation and topic tracking. We show high accuracy in location estimation based on our model. Moreover, the algorithm identifies interesting topics based on location and language. This article explores one of the key enablers of beyond 4G wireless networks leveraging small cell network deployments, proactive caching. Endowed with predictive capabilities and harnessing recent developments in storage, context awareness, and social networks, peak traffic demands can be substantially reduced by proactively serving predictable user demands via caching at base stations and users' devices. In order to show the effectiveness of proactive caching, we examine two case studies that exploit the spatial and social structure of the network, where proactive caching plays a crucial role. First, in order to alleviate backhaul congestion, we propose a mechanism whereby files are proactively cached during off-peak periods based on file popularity and correlations among user and file patterns. Second, leveraging social networks and D2D communications, we propose a procedure that exploits the social structure of the network by predicting the set of influential users to (proactively) cache strategic contents and disseminate them to their social ties via D2D communications. Exploiting this proactive caching paradigm, numerical results show that important gains can be obtained for each case study, with backhaul savings and a higher ratio of satisfied users of up to 22 and 26 percent, respectively. Higher gains can be further obtained by increasing the storage capability at the network edge. While 5G wireless networks are expected to handle ever growing data avalanche, classical deployment optimization approaches such as hyper- dense deployment of base stations or having more bandwidth are cost-inefficient, therefore seen as stopgaps. In this regard, context-aware approaches which exploits human predictability, recent advances in storage, edge cloud computing and big data analytics are needed. In this article, we approach to this problem from a proactive caching perspective where gains of cache-enabled base stations in 5G wireless are studied. In particular, huge amount of real data from a telecom operator in Turkey is collected processed on a big data platform, and an analysis is carried out for content popularity estimation for caching, aiming to improve users' experience in terms of request satisfactions and offload the backhaul. Subsequently, with this mobile traffic data collected from many base stations within several hours of time interval and estimation of content popularity via machine learning tools, we investigate the gains of the proactive caching via numerical simulations. The results show that proactive caching fulfils 100 of user request satisfaction and offloads 98 of the backhaul, in a setting of 16 base station with 15.4 Gbyte of storage size (87 of the total catalog size) and 10 of content ratings. Small cells constitute a promising solution for managing the mobile data growth that has overwhelmed network operators. Local caching of popular content items at the small cell base stations has been proposed in order to decrease the capacity-and hence the cost- of the backhaul links that connect these base stations with the core network. However, deriving the optimal caching policy remains a challenging open problem especially if one considers realistic parameters such as the bandwidth limitation of the base stations. The latter constraint is particularly important for cases when users requests are massive. We consider such a scenario and formulate the joint caching and routing problem aiming to maximize the fraction of content requests served by the deployed small cell base stations. This is an NP-hard problem and hence we cannot obtain an exact optimal solution. Thus, we present a novel approximation framework based on a reduction to a well known variant of the facility location problem. This allows us to exploit the rich literature in facility location problems, in order to establish bounded approximation algorithms for our problem. A heterogenous network with base stations (BSs), small base stations (SBSs), and users distributed according to independent Poisson point processes is considered. SBS nodes are assumed to possess high storage capacity and to form a distributed caching network. Popular files are stored in local caches of SBSs, so that a user can download the desired files from one of the SBSs in its vicinity. The offloading-loss is captured via a cost function that depends on the random caching strategy proposed here. The popularity profile of cached content is unknown and estimated using instantaneous demands from users within a specified time interval. An estimate of the cost function is obtained from which an optimal random caching strategy is devised. The training time to achieve an @math difference between the achieved and optimal costs is finite provided the user density is greater than a predefined threshold, and scales as @math , where @math is the support of the popularity profile. A transfer learning-based approach to improve this estimate is proposed. The training time is reduced when the popularity profile is modeled using a parametric family of distributions; the delay is independent of @math and scales linearly with the dimension of the distribution parameter. In this article, we propose content-centric, in-network content caching and placement approaches that leverage cooperation among edge cloud devices, content popularity, and GPS trajectory information to improve content delivery speeds, network traffic congestion, cache resource utilization efficiency, and users' quality of experience in highly populated cities. More specifically, our proposed approaches exploit collaborative filtering theory to provide accurate and efficient content popularity predictions to enable proactive in-network caching of Internet content. We propose a practical content delivery architecture that consists of standalone edge cloud devices to be deployed in the city to cache and process popular Internet content as it disseminates throughout the network. We also show that our proposed approaches ensure responsive cloud content delivery with minimized service disruption.