Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Call for papers for Special Issue of ACM Transactions on Multimedia Computing, Communications and Applications on Interactive Digital Television

ACM Transactions on Multimedia Computing, Communications and Applications (ACM TOMCCAP)

Federation of geo-distributed cloud services is a trend in cloud computing which, by spanning multiple data centers at different geographical locations, can provide a cloud platform with much larger capacities. Such a geo-distributed cloud is ideal for supporting large-scale social media streaming applications (e.g., YouTube-like sites) with dynamic contents and demands, owing to its abundant on-demand storage/bandwidth capacities and geographical proximity to different groups of users. Although promising, its realization presents challenges on how to efficiently store and migrate contents among different cloud sites (i.e. data centers), and to distribute user requests to the appropriate sites for timely responses at modest costs. These challenges escalate when we consider the persistently increasing contents and volatile user behaviors in a social media application. By exploiting social influences among users, this paper proposes efficient proactive algorithms for dynamic, optimal scaling of a social media application in a geo-distributed cloud. Our key contribution is an online content migration and request distribution algorithm with the following features: (1) future demand prediction by novelly characterizing social influences among the users in a simple but effective epidemic model; (2) oneshot optimal content migration and request distribution based on efficient optimization algorithms to address the predicted demand, and (3) a Δ(t)-step look-ahead mechanism to adjust the one-shot optimization results towards the offline optimum. We verify the effectiveness of our algorithm using solid theoretical analysis, as well as large-scale experiments under dynamic realistic settings on a home-built cloud platform.

/pdf/scaling-social-media-applications-into-geo-distributed-492bglzdm0.pdf

Scaling social media applications into geo-distributed clouds

This paper presents a comprehensive literature review on applications of economic and pricing models for resource management in cloud networking. To achieve sustainable profit advantage, cost reduction, and flexibility in provisioning of cloud resources, resource management in cloud networking requires adaptive and robust designs to address many issues, e.g., resource allocation, bandwidth reservation, request allocation, and workload allocation. Economic and pricing models have received a lot of attention as they can lead to desirable performance in terms of social welfare, fairness, truthfulness, profit, user satisfaction, and resource utilization. This paper reviews applications of the economic and pricing models to develop adaptive algorithms and protocols for resource management in cloud networking. Besides, we survey a variety of incentive mechanisms using the pricing strategies in sharing resources in edge computing. In addition, we consider using pricing models in cloud-based software defined wireless networking. Finally, we highlight important challenges, open issues and future research directions of applying economic and pricing models to cloud networking.

Resource Management in Cloud Networking Using Economic Analysis and Pricing Models: A Survey

Multimedia big data computing and Internet of Things applications: A taxonomy and process model

Popularity prediction, with both technological and economic importance, has been extensively studied for conventional video sharing sites (VSSes), where the videos are mainly found via searching, browsing, or related links. Recent statistics however suggest that online social network (OSN) users regularly share video contents from VSSes, which has contributed to a significant portion of the accesses; yet the popularity prediction in this new context remains largely unexplored. In this paper, we present an initial study on the popularity prediction of videos propagated in OSNs along friendship links.We conduct a large-scale measurement and analysis of viewing patterns of videos shared in one of largest OSNs in China, and examine the performance of typical views-based prediction models. We find that they are generally ineffective, if not totally fail, especially when predicting the early peaks and later bursts of accesses, which are common during video propagations in OSNs. To overcome these limits, we track the propagation process of videos shared in a Facebook-like OSN in China, and analyze the user viewing and sharing behaviors. We accordingly develop a novel propagation-based video popularity prediction solution, namely SoVP. Instead of relying solely on the early views for prediction, SoVP considers both the intrinsic attractiveness of a video and the influence from the underlying propagation structure. The effectiveness of SoVP, particularly for predicting the peaks and bursts, have been validated through our trace-driven experiments.

/pdf/on-popularity-prediction-of-videos-shared-in-online-social-4ovgwy136k.pdf

On popularity prediction of videos shared in online social networks

Since user demand for a Video-on-demand (VoD) service varies with time in one-day period, provisioning self-owned servers for the peak load it must sustain a few hours per day leads to bandwidth underutilization at other times. Content clouds, e.g. Amazon Cloud Front and Azure CDN, let VoD providers pay by bytes for bandwidth resources, potentially leading to cost savings even if the unit rate to rent a machine from a cloud provider is higher than the rate to own one. In this paper, based on long-term traces from two large-scale VoD systems and temporal development model of content clouds, we tackle challenges, design and potential benefits in migrating VoD services into the hybrid cloud-assisted deployment, where the user requests are partly served by the self-owned servers and partly served by the cloud. Our measurements show that the popularity of the most popular videos decays so quickly, for example, by 11% after one hour that it poses large challenges on updating videos in the cloud. However, the trace-driven evaluations show that our proposed migration strategies (active, reactive and smart strategies), although simply based on the current information, can make the hybrid cloud-assisted VoD deployment save up to 30% bandwidth expense compared with the Clients/Server mode. They can also handle unpredicted the flash crowd traffic with little cost. It also shows that the cloud price and server bandwidth chosen play the most important roles in saving cost, while the cloud storage size and cloud content update strategy play the key roles in the user experience improvement.

/pdf/cost-effective-partial-migration-of-vod-services-to-content-21e79c579z.pdf

Cost-Effective Partial Migration of VoD Services to Content Clouds

This paper presents a systematic online prediction method (Social-Forecast) that is capable to accurately forecast the popularity of videos promoted by social media. Social-Forecast explicitly considers the dynamically changing and evolving propagation patterns of videos in social media when making popularity forecasts, thereby being situation and context aware. Social-Forecast aims to maximize the forecast reward, which is defined as a tradeoff between the popularity prediction accuracy and the timeliness with which a prediction is issued. The forecasting is performed online and requires no training phase or a priori knowledge. We analytically bound the prediction performance loss of Social-Forecast as compared to that obtained by an omniscient oracle and prove that the bound is sublinear in the number of video arrivals, thereby guaranteeing its short-term performance as well as its asymptotic convergence to the optimal performance. In addition, we conduct extensive experiments using real-world data traces collected from the videos shared in RenRen, one of the largest online social networks in China. These experiments show that our proposed method outperforms existing view-based approaches for popularity prediction (which are not context-aware) by more than 30% in terms of prediction rewards.

/pdf/forecasting-popularity-of-videos-using-social-media-yrw7unrss3.pdf

Forecasting Popularity of Videos Using Social Media

YouTube-like video sharing sites (VSSes) have gained increasing popularity in recent years. Meanwhile, Face-book-like online social networks (OSNs) have seen their tremendous success in connecting people of common interests. These two new generation of networked services are now bridged in that many users of OSNs share video contents originating from VSSes with their friends, and it has been shown that a significant portion of views of VSS videos are attributed to this sharing scheme of social networks. To understand how the video sharing behavior, which is largely based on social relationship, impacts users' viewing pattern, we have conducted a long-term measurement with RenRen and YouKu, the largest online social network and the largest video sharing site in China, respectively. We show that social friends have higher common interest and their sharing behaviors provide guidance to enhance recommended video lists. In this paper, we take a first step toward learning OSN video sharing patterns for video recommendation. An autoencoder model is developed to learn the social similarity of different videos in terms of their sharing in OSNs. We, therefore, propose a similarity-based strategy to enhance video recommendation for YouTube-like social media. Evaluation results demonstrate that this strategy can remarkably improve the precision and recall of recommendations, as compared to other widely adopted strategies without social information.

https://www.cs.sfu.ca/~jcliu/Papers/ExploringSharingPatterns.pdf

Exploring sharing patterns for video recommendation on YouTube-like social media

Online social networks (OSNs) have become popular destinations for connecting friends and sharing information. Recent statistics suggest that OSN users regularly share contents from video sites, and a significant amount of requests of the video sites are indeed from them nowadays. These behaviors have substantially changed the workload of online video services. To better understand this paradigm shift, we conduct a long-term and extensive measurement of video sharing in RenRen, the largest Facebook-like OSN in China. In this paper, we focus on the video popularity distribution and evolution. In particular, we find that the video popularity distribution exhibits perfect power-law feature (while videos in YouTube exhibit a power-law waist with a long truncated tail). Moreover, we observe that the requests for the new published videos generally experience two or three days latency to reach the peak value, and then change dynamically with a series of unpredictable bursts (while in YouTube, videos reach the global peak immediately after introduction to the system, and then the accesses generally decrease overtime, except possibly on some special days). These differences can raise new challenges to content providers. For example, the video popularity is now hard to predict based on their historical requests. We further develop a simple yet effective model to simulate user requests process across videos in OSNs. Trace-based simulation shows that it can well capture the observed features.

https://www.cs.sfu.ca/~jcliu/Papers/VideoSharinginOnline.pdf

Haitao Li

Papers

On popularity prediction of videos shared in online social networks

Cost-Effective Partial Migration of VoD Services to Content Clouds

Forecasting Popularity of Videos Using Social Media

Exploring sharing patterns for video recommendation on YouTube-like social media

Video sharing in online social networks: measurement and analysis