P. Fraternali, G. Baroffio, C. Pasini, Luca Galli, I. Micheel, J. Novak, A. Rizzoli
The demo showcases the SmartH2O platform1, a system for water demand management based on an original mix of data analytics and behavioral science. SmartH2O collects consumption data from the automatic meter infrastructure of a (water) utility and allows customers access them in a Web portal, where they can see information about their actual and forecasted consumption, compare with the neighborhood, and obtain personalized water saving tips and leak alerts. Engagement is reinforced through a unique mix of in-app gamification techniques, digital educational games and real board games, which provide a rich set of behavior change stimuli to all household members. Lab tests show good acceptance and engagement by pilot users, deployment to a large set of consumers is scheduled shortly.
{"title":"Integrating Real and Digital Games with Data Analytics for Water Consumption Behavioral Change: A Demo","authors":"P. Fraternali, G. Baroffio, C. Pasini, Luca Galli, I. Micheel, J. Novak, A. Rizzoli","doi":"10.1109/UCC.2015.68","DOIUrl":"https://doi.org/10.1109/UCC.2015.68","url":null,"abstract":"The demo showcases the SmartH2O platform1, a system for water demand management based on an original mix of data analytics and behavioral science. SmartH2O collects consumption data from the automatic meter infrastructure of a (water) utility and allows customers access them in a Web portal, where they can see information about their actual and forecasted consumption, compare with the neighborhood, and obtain personalized water saving tips and leak alerts. Engagement is reinforced through a unique mix of in-app gamification techniques, digital educational games and real board games, which provide a rich set of behavior change stimuli to all household members. Lab tests show good acceptance and engagement by pilot users, deployment to a large set of consumers is scheduled shortly.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125144949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study the performance and cost efficiency as perceived by the end user of a specific class of Infrastructure-as-a-Service (IaaS) cloud instances, namely credit-based bursting instances. This class of instance types has been introduced by Amazon EC2 in summer 2014, and behaves on a fundamental level differently than any other existing instance type, either from EC2 or other vendors. We introduce a basic formal model for fostering the understanding and analysis of these types, and empirically study their performance in practice. Further, we compare the performance of credit-based bursting cloud instance types to existing general-purpose types, and derive potential use cases for practitioners. Our results indicate that bursting instance types are cost-efficient for CPU-bound applications with an average utilization of less than 40%, as well as for non-critical IO-bound applications. Finally, we also discuss a simple boosting scheme that enables practitioners to improve the cost efficiency of their bursting instance usage under given constraints.
{"title":"Bursting with Possibilities -- An Empirical Study of Credit-Based Bursting Cloud Instance Types","authors":"P. Leitner, Joel Scheuner","doi":"10.1109/UCC.2015.39","DOIUrl":"https://doi.org/10.1109/UCC.2015.39","url":null,"abstract":"We study the performance and cost efficiency as perceived by the end user of a specific class of Infrastructure-as-a-Service (IaaS) cloud instances, namely credit-based bursting instances. This class of instance types has been introduced by Amazon EC2 in summer 2014, and behaves on a fundamental level differently than any other existing instance type, either from EC2 or other vendors. We introduce a basic formal model for fostering the understanding and analysis of these types, and empirically study their performance in practice. Further, we compare the performance of credit-based bursting cloud instance types to existing general-purpose types, and derive potential use cases for practitioners. Our results indicate that bursting instance types are cost-efficient for CPU-bound applications with an average utilization of less than 40%, as well as for non-critical IO-bound applications. Finally, we also discuss a simple boosting scheme that enables practitioners to improve the cost efficiency of their bursting instance usage under given constraints.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"42 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120846963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Saadeldin Moustafa, Khalid Elgazzar, Patrick Martin, Marwa A. Elsayed
This paper presents SLAM, a customizable platform-independent SLA monitoring framework for federated cloud services. It supports monitoring of distributed nodes and hosts using an agent-based model. The framework generates monitoring templates according to the SLA terms that describe monitoring requirements and creates monitoring reports based on these generated templates. In addition, we propose a service benchmarking approach that can compare similar services offered by different providers without deploying monitoring agents on the providers' sites.
{"title":"SLAM: SLA Monitoring Framework for Federated Cloud Services","authors":"Saadeldin Moustafa, Khalid Elgazzar, Patrick Martin, Marwa A. Elsayed","doi":"10.1109/UCC.2015.90","DOIUrl":"https://doi.org/10.1109/UCC.2015.90","url":null,"abstract":"This paper presents SLAM, a customizable platform-independent SLA monitoring framework for federated cloud services. It supports monitoring of distributed nodes and hosts using an agent-based model. The framework generates monitoring templates according to the SLA terms that describe monitoring requirements and creates monitoring reports based on these generated templates. In addition, we propose a service benchmarking approach that can compare similar services offered by different providers without deploying monitoring agents on the providers' sites.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130705949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The cloud computing paradigm is continually evolving, and with it, the size and the complexity of its infrastructure. Assessing the performance of a cloud environment is an essential but an arduous task. Further, the energy consumed by data centers is steadily increasing and major components such as the storage systems need to be more energy efficient. Cloud simulation tools have proved quite useful to study these issues. However, these simulation tools lack mechanisms to study energy efficient storage in cloud systems. This paper contributes in the area of cloud computing by extending the widely used cloud simulator CloudSim. In this paper, we propose CloudSimDisk, a scalable module for modeling and simulation of energy-aware storage in cloud systems. We show how CloudSimDisk can be used to simulate energy-aware storage, and can be extended to study new algorithms for energy-awareness in cloud systems. Our simulation results proved to be in accordance with the analytical models that were developed to model energy consumption of hard disk drives in cloud systems. The source code of CloudSimDisk is also made available for the research community for further testing and development.
{"title":"CloudSimDisk: Energy-Aware Storage Simulation in CloudSim","authors":"B. Louis, Karan Mitra, S. Saguna, C. Åhlund","doi":"10.1109/UCC.2015.15","DOIUrl":"https://doi.org/10.1109/UCC.2015.15","url":null,"abstract":"The cloud computing paradigm is continually evolving, and with it, the size and the complexity of its infrastructure. Assessing the performance of a cloud environment is an essential but an arduous task. Further, the energy consumed by data centers is steadily increasing and major components such as the storage systems need to be more energy efficient. Cloud simulation tools have proved quite useful to study these issues. However, these simulation tools lack mechanisms to study energy efficient storage in cloud systems. This paper contributes in the area of cloud computing by extending the widely used cloud simulator CloudSim. In this paper, we propose CloudSimDisk, a scalable module for modeling and simulation of energy-aware storage in cloud systems. We show how CloudSimDisk can be used to simulate energy-aware storage, and can be extended to study new algorithms for energy-awareness in cloud systems. Our simulation results proved to be in accordance with the analytical models that were developed to model energy consumption of hard disk drives in cloud systems. The source code of CloudSimDisk is also made available for the research community for further testing and development.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125118457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Josef Spillner, Martin Beck, A. Schill, T. Bohnert
Sensitive data is increasingly being hosted online in ubiquitous cloud storage services. Recent advances in multi-cloud service integration through provider multiplexing and data dispersion have alleviated most of the associated risks for hosting files which are retrieved by users for further processing. However, for structured data managed in databases, many issues remain, including the need to perform operations directly on the remote data to avoid costly transfers. In this paper, we motivate the need for distributed stealth databases which combine properties from structure-preserving dispersed file storage for capacity-saving increased availability with emerging work on structure-preserving encryption for on-demand increased confidentiality with controllable performance degradation. We contribute an analysis of operators executing in map-reduce or map-carry-reduce phases and derive performance statistics. Our prototype, StealthDB, demonstrates that for typical amounts of personal structured data, stealth databases are a convincing concept for taming untrusted and unsafe cloud environments.
{"title":"Stealth Databases: Ensuring User-Controlled Queries in Untrusted Cloud Environments","authors":"Josef Spillner, Martin Beck, A. Schill, T. Bohnert","doi":"10.1109/UCC.2015.44","DOIUrl":"https://doi.org/10.1109/UCC.2015.44","url":null,"abstract":"Sensitive data is increasingly being hosted online in ubiquitous cloud storage services. Recent advances in multi-cloud service integration through provider multiplexing and data dispersion have alleviated most of the associated risks for hosting files which are retrieved by users for further processing. However, for structured data managed in databases, many issues remain, including the need to perform operations directly on the remote data to avoid costly transfers. In this paper, we motivate the need for distributed stealth databases which combine properties from structure-preserving dispersed file storage for capacity-saving increased availability with emerging work on structure-preserving encryption for on-demand increased confidentiality with controllable performance degradation. We contribute an analysis of operators executing in map-reduce or map-carry-reduce phases and derive performance statistics. Our prototype, StealthDB, demonstrates that for typical amounts of personal structured data, stealth databases are a convincing concept for taming untrusted and unsafe cloud environments.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130969907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhonghong Ou, Zhen-Huan Hwang, Feng Chen, Ren Wang, Antti Ylä-Jääski
Traditionally, network storage systems have mainly been dominated by two IP-based storage technologies, i.e., Network Attached Storage (NAS) and Storage Area Network (SAN). In recent years, cloud based storage (e.g., Amazon S3) has gained growing popularity for its high flexibility and cross-platform compatibility. Many enterprises are considering to replace traditional storage systems with cloud-based systems. Evaluating such a transition demands a systematic study on understanding the performance behaviours of the emerging cloud storage. To fill in this gap, in this paper, we conduct a comprehensive study on the three storage systems with realistic network conditions. Specifically, we select one representative from each category for comparison, i.e., Network File System (NFS) from NAS, Internet Small Computer System Interface (iSCSI) from SAN, and OpenStack Swift from cloud storage. We build a testbed and develop a suite of micro-benchmarks to study the impact of network complexities. Through a set of experiments and detailed analysis, we make several key observations: (1) iSCSI excels under good network conditions, e.g., in local area networks (LANs) where network delay and packet loss are trivial, (2) NFS and Swift are more suitable for complex networks such as wireless networks and Internet environment, (3) Swift is a viable replacement for NFS in all scenarios we investigate, and (4) system configuration on the client side impacts storage performance significantly and deserves adequate attention. We hope our findings can not only shed light on storage service design and optimizations, but also encourage more research on emerging storage technologies.
{"title":"Is Cloud Storage Ready? A Comprehensive Study of IP-Based Storage Systems","authors":"Zhonghong Ou, Zhen-Huan Hwang, Feng Chen, Ren Wang, Antti Ylä-Jääski","doi":"10.1109/UCC.2015.14","DOIUrl":"https://doi.org/10.1109/UCC.2015.14","url":null,"abstract":"Traditionally, network storage systems have mainly been dominated by two IP-based storage technologies, i.e., Network Attached Storage (NAS) and Storage Area Network (SAN). In recent years, cloud based storage (e.g., Amazon S3) has gained growing popularity for its high flexibility and cross-platform compatibility. Many enterprises are considering to replace traditional storage systems with cloud-based systems. Evaluating such a transition demands a systematic study on understanding the performance behaviours of the emerging cloud storage. To fill in this gap, in this paper, we conduct a comprehensive study on the three storage systems with realistic network conditions. Specifically, we select one representative from each category for comparison, i.e., Network File System (NFS) from NAS, Internet Small Computer System Interface (iSCSI) from SAN, and OpenStack Swift from cloud storage. We build a testbed and develop a suite of micro-benchmarks to study the impact of network complexities. Through a set of experiments and detailed analysis, we make several key observations: (1) iSCSI excels under good network conditions, e.g., in local area networks (LANs) where network delay and packet loss are trivial, (2) NFS and Swift are more suitable for complex networks such as wireless networks and Internet environment, (3) Swift is a viable replacement for NFS in all scenarios we investigate, and (4) system configuration on the client side impacts storage performance significantly and deserves adequate attention. We hope our findings can not only shed light on storage service design and optimizations, but also encourage more research on emerging storage technologies.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131029832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces XMPP and suggests how this technology might be used to help implement Intercloud communication. It gives an introduction to XMPP and how the architecture fits together as well as a discussion of the services it provides 'out of the box'. It then discusses secondary benefits of the protocol and highlights how XMPP could be an appropriate base protocol for implementing the Intercloud Control and Management Plane. This is followed by discussion of early results from a research project that looks at the ease of extending XMPP and the tractability of the standardization process.
{"title":"Intercloud Control and Management Plane with XMPP","authors":"Peter Membrey, Y. Demchenko","doi":"10.1109/UCC.2015.84","DOIUrl":"https://doi.org/10.1109/UCC.2015.84","url":null,"abstract":"This paper introduces XMPP and suggests how this technology might be used to help implement Intercloud communication. It gives an introduction to XMPP and how the architecture fits together as well as a discussion of the services it provides 'out of the box'. It then discusses secondary benefits of the protocol and highlights how XMPP could be an appropriate base protocol for implementing the Intercloud Control and Management Plane. This is followed by discussion of early results from a research project that looks at the ease of extending XMPP and the tractability of the standardization process.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121167066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mobile devices and their sensors facilitate the development of a large range of environment-sensing applications and systems. Crowd sensing is used to feed smart city applications with anonymous but still relevant data. The quality and success of smart city applications depend on several aspects of user involvement, such as data trust and information about data origin. However, with the anonymity and openness of crowd sensing, smart city applications are exposed to untrustworthy and malicious data that can lead to poor decisions. In this paper, we propose a cloud architecture for smart city applications that includes, as a core service, a reputation system for evaluating the trustworthiness of crowd sensing data. This service will run locally, as close to the crowd as possible, for example, on wireless local area network (WLAN) access points (AP). Additionally, data stored in the cloud is traceable by its origin information.
{"title":"The Origin and Trustworthiness of Data in Smart City Applications","authors":"Aseel Alkhelaiwi, D. Grigoras","doi":"10.1109/UCC.2015.60","DOIUrl":"https://doi.org/10.1109/UCC.2015.60","url":null,"abstract":"Mobile devices and their sensors facilitate the development of a large range of environment-sensing applications and systems. Crowd sensing is used to feed smart city applications with anonymous but still relevant data. The quality and success of smart city applications depend on several aspects of user involvement, such as data trust and information about data origin. However, with the anonymity and openness of crowd sensing, smart city applications are exposed to untrustworthy and malicious data that can lead to poor decisions. In this paper, we propose a cloud architecture for smart city applications that includes, as a core service, a reputation system for evaluating the trustworthiness of crowd sensing data. This service will run locally, as close to the crowd as possible, for example, on wireless local area network (WLAN) access points (AP). Additionally, data stored in the cloud is traceable by its origin information.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123840690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jafar Albadarneh, Bashar Talafha, M. Al-Ayyoub, B. Zaqaibeh, Mohammad Al-Smadi, Y. Jararweh, E. Benkhelifa
Authorship authentication of a certain text is concerned with correctly attributing it to its author based on its contents. It is a very important problem with deep root in history as many classical texts have doubtful attributions. The information age and ubiquitous use of the Internet is further complicating this problem and adding more dimensions to it. We are interested in the modern version of this problem where the text whose authorship needs authentication is an online text found in online social networks. Specifically, we are interested in the authorship authentication of tweets. This is not the only challenging aspect we consider here. Another challenging aspect is the language of the tweets. Most current works and existing tools support English. We chose to focus on the very important, yet largely understudied, Arabic language. Finally, we add another challenging aspect to the problem at hand by addressing it at a very large scale. We present our effort to employ big data analytics to address the authorship authentication problem of Arabic tweets. We start by crawling a dataset of more than 53K tweets distributed across 20 authors. We then use preprocessing steps to clean the data and prepare it for analysis. The next step is to compute the feature vectors of each tweet. We use the Bag-Of-Words (BOW) approach and compute the weights using the Term Frequency-Inverse Document Frequency (TF-IDF). Then, we feed the dataset to a Naive Bayes classifier implemented on a parallel and distributed computing framework known as Hadoop. To the best of our knowledge, none of the previous works on authorship authentication of Arabic text addressed the unique challenges associated with (1) tweets and (2) large-scale datasets. This makes our work unique on many levels. The results show that the testing accuracy is not very high (61.6%), which is expected in the very challenging setting that we consider.
{"title":"Using Big Data Analytics for Authorship Authentication of Arabic Tweets","authors":"Jafar Albadarneh, Bashar Talafha, M. Al-Ayyoub, B. Zaqaibeh, Mohammad Al-Smadi, Y. Jararweh, E. Benkhelifa","doi":"10.1109/UCC.2015.80","DOIUrl":"https://doi.org/10.1109/UCC.2015.80","url":null,"abstract":"Authorship authentication of a certain text is concerned with correctly attributing it to its author based on its contents. It is a very important problem with deep root in history as many classical texts have doubtful attributions. The information age and ubiquitous use of the Internet is further complicating this problem and adding more dimensions to it. We are interested in the modern version of this problem where the text whose authorship needs authentication is an online text found in online social networks. Specifically, we are interested in the authorship authentication of tweets. This is not the only challenging aspect we consider here. Another challenging aspect is the language of the tweets. Most current works and existing tools support English. We chose to focus on the very important, yet largely understudied, Arabic language. Finally, we add another challenging aspect to the problem at hand by addressing it at a very large scale. We present our effort to employ big data analytics to address the authorship authentication problem of Arabic tweets. We start by crawling a dataset of more than 53K tweets distributed across 20 authors. We then use preprocessing steps to clean the data and prepare it for analysis. The next step is to compute the feature vectors of each tweet. We use the Bag-Of-Words (BOW) approach and compute the weights using the Term Frequency-Inverse Document Frequency (TF-IDF). Then, we feed the dataset to a Naive Bayes classifier implemented on a parallel and distributed computing framework known as Hadoop. To the best of our knowledge, none of the previous works on authorship authentication of Arabic text addressed the unique challenges associated with (1) tweets and (2) large-scale datasets. This makes our work unique on many levels. The results show that the testing accuracy is not very high (61.6%), which is expected in the very challenging setting that we consider.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124616532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. Portilla, Alexandre Reiffers, E. Altman, R. E. Azouzi
The Youtube recommendation is one the most important view source of a video. In this paper, we focus on the recommendation system in boosting the popularity of videos. We first construct a graph that captures the recommendation system in Youtube and study empirically the relationship between the number of views of a video and the average number of views of the videos in its recommendation list. We then consider a random walker on the recommendation graph, i.e. a random user that browses through videos such that the video it chooses to watch is selected randomly among the videos in the recommendation list of the previous video it watched. We study the stability properties of this random process and we show that the trajectory obtained does not contain cycles if the number of videos in the recommendation list is small (which is the case if the computer's screen is small).
{"title":"A Study of YouTube Recommendation Graph Based on Measurements and Stochastic Tools","authors":"Y. Portilla, Alexandre Reiffers, E. Altman, R. E. Azouzi","doi":"10.1109/UCC.2015.77","DOIUrl":"https://doi.org/10.1109/UCC.2015.77","url":null,"abstract":"The Youtube recommendation is one the most important view source of a video. In this paper, we focus on the recommendation system in boosting the popularity of videos. We first construct a graph that captures the recommendation system in Youtube and study empirically the relationship between the number of views of a video and the average number of views of the videos in its recommendation list. We then consider a random walker on the recommendation graph, i.e. a random user that browses through videos such that the video it chooses to watch is selected randomly among the videos in the recommendation list of the previous video it watched. We study the stability properties of this random process and we show that the trajectory obtained does not contain cycles if the number of videos in the recommendation list is small (which is the case if the computer's screen is small).","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125001545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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