Endre søk
Begrens søket
1 - 16 of 16
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Treff pr side
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
Merk
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1.
    Abbas, Zainab
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Sigurdsson, Thorsteinn Thorri
    KTH.
    Al-Shishtawy, Ahmad
    RISE Res Inst Sweden, Stockholm, Sweden..
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Evaluation of the Use of Streaming Graph Processing Algorithms for Road Congestion Detection2018Inngår i: 2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS / [ed] Chen, JJ Yang, LT, IEEE COMPUTER SOC , 2018, s. 1017-1025Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Real-time road congestion detection allows improving traffic safety and route planning. In this work, we propose to use streaming graph processing algorithms for road congestion detection and evaluate their accuracy and performance. We represent road infrastructure sensors in the form of a directed weighted graph and adapt the Connected Components algorithm and some existing graph processing algorithms, originally used for community detection in social network graphs, for the task of road congestion detection. In our approach, we detect Connected Components or communities of sensors with similarly weighted edges that reflect different states in the traffic, e.g., free flow or congested state, in regions covered by detected sensor groups. We have adapted and implemented the Connected Components and community detection algorithms for detecting groups in the weighted sensor graphs in batch and streaming manner. We evaluate our approach by building and processing the road infrastructure sensor graph for Stockholm's highways using real-world data from the Motorway Control System operated by the Swedish traffic authority. Our results indicate that the Connected Components and DenGraph community detection algorithms can detect congestion with accuracy up to approximate to 94% for Connected Components and up to approximate to 88% for DenGraph. The Louvain Modularity algorithm for community detection fails to detect congestion regions for sparsely connected graphs, representing roads that we have considered in this study. The Hierarchical Clustering algorithm using speed and density readings is able to detect congestion without details, such as shockwaves.

  • 2.
    Garcia Lozano, Marianela
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Programvaruteknik och datorsystem, SCS. FOI Swedish Defence Research Agency, Stockholm, SE-164 90, Sweden.
    Brynielsson, Joel
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Teoretisk datalogi, TCS. FOI Swedish Defence Research Agency, Stockholm, SE-164 90, Sweden.
    Franke, Ulrik
    RISE Res Inst Sweden, POB 1263, SE-16429 Kista, Sweden..
    Rosell, Magnus
    FOI Swedish Def Res Agcy, SE-16490 Stockholm, Sweden..
    Tjörnhammar, Edward
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Programvaruteknik och datorsystem, SCS. FOI Swedish Defence Research Agency, Stockholm, SE-164 90, Sweden.
    Varga, Stefan
    KTH. Swedish Armed Forces Headquarters, Stockholm, SE-107 85, Sweden.
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Programvaruteknik och datorsystem, SCS.
    Veracity assessment of online data2020Inngår i: Decision Support Systems, ISSN 0167-9236, E-ISSN 1873-5797, Vol. 129, artikkel-id 113132Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Fake news, malicious rumors, fabricated reviews, generated images and videos, are today spread at an unprecedented rate, making the task of manually assessing data veracity for decision-making purposes a daunting task. Hence, it is urgent to explore possibilities to perform automatic veracity assessment. In this work we review the literature in search for methods and techniques representing state of the art with regard to computerized veracity assessment. We study what others have done within the area of veracity assessment, especially targeted towards social media and open source data, to understand research trends and determine needs for future research. The most common veracity assessment method among the studied set of papers is to perform text analysis using supervised learning. Regarding methods for machine learning much has happened in the last couple of years related to the advancements made in deep learning. However, very few papers make use of these advancements. Also, the papers in general tend to have a narrow scope, as they focus on solving a small task with only one type of data from one main source. The overall veracity assessment problem is complex, requiring a combination of data sources, data types, indicators, and methods. Only a few papers take on such a broad scope, thus, demonstrating the relative immaturity of the veracity assessment domain.

  • 3.
    Imtiaz, Sana
    et al.
    Université Catholique de Louvain.
    Sadre, Ramin
    Université Catholique de Louvain.
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Programvaruteknik och datorsystem, SCS.
    On the case of privacy in the iot ecosystem: a survey2019Inngår i: Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019, 2019, s. 1015-1024Konferansepaper (Fagfellevurdert)
    Abstract [en]

    IoT has enabled the creation of a multitude of personal applications and services for a better understanding of urban environments and our personal lives. These services are driven by the continuous collection and analysis of user data in order to provide personalized experiences. However, there is a strong need to address user privacy concerns as most of the collected data is of sensitive nature. This paper provides an overview of privacy preservation techniques and solutions proposed so far in literature along with the IoT levels at which privacy is addressed by each solution as well as their robustness to privacy breaching attacks. An analysis of functional and non-functional limitations of each solution is done, followed by a short survey of machine learning applications designed with these solutions. We identify open issues in the privacy preserving solutions when used in IoT environments. Moreover, we note that most of the privacy preservation solutions need to be adapted in the light of GDPR to accommodate the right to privacy of the users.

  • 4. Kalavri, Vasiliki
    et al.
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Haridi, Seif
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    High-Level Programming Abstractions for Distributed Graph Processing2018Inngår i: IEEE Transactions on Knowledge and Data Engineering, ISSN 1041-4347, E-ISSN 1558-2191, Vol. 30, nr 2, s. 305-324Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Efficient processing of large-scale graphs in distributed environments has been an increasingly popular topic of research in recent years. Inter-connected data that can be modeled as graphs appear in application domains such as machine learning, recommendation, web search, and social network analysis. Writing distributed graph applications is inherently hard and requires programming models that can cover a diverse set of problems, including iterative refinement algorithms, graph transformations, graph aggregations, pattern matching, ego-network analysis, and graph traversals. Several high-level programming abstractions have been proposed and adopted by distributed graph processing systems and big data platforms. Even though significant work has been done to experimentally compare distributed graph processing frameworks, no qualitative study and comparison of graph programming abstractions has been conducted yet. In this survey, we review and analyze the most prevalent high-level programming models for distributed graph processing, in terms of their semantics and applicability. We review 34 distributed graph processing systems with respect to the graph processing models they implement and we survey applications that appear in recent distributed graph systems papers. Finally, we discuss trends and open research questions in the area of distributed graph processing.

  • 5.
    Khan, Amin M.
    et al.
    UiT Arctic Univ Norway, Dept Comp Sci, Tromso, Norway.;Hitachi Vantara Corp, Lisbon, Portugal..
    Freitag, Felix
    Univ Politecn Cataluna, Dept Comp Architecture, Barcelona, Spain..
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Ha, Phuong Hoai
    Demo Abstract: Towards IoT Service Deployments on Edge Community Network Microclouds2018Inngår i: IEEE INFOCOM 2018 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS), IEEE , 2018Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Internet of Things (IoT) services for personal devices and smart homes provided by commercial solutions are typically proprietary and closed. These services provide little control to the end users, for instance to take ownership of their data and enabling services, which hinders these solutions' wider acceptance. In this demo paper, we argue for an approach to deploy professional IoT services on user-controlled infrastructure at the network edge. The users would benefit from the ability to choose the most suitable service from different IoT service offerings, like the one which satisfies their privacy requirements, and third-party service providers could offer more tailored IoT services at customer premises. We conduct the demonstration on microclouds, which have been built with the Cloudy platform in the Guifi. net community network. The demonstration is conducted from the perspective of end users, who wish to deploy professional IoT data management and analytics services in volunteer microclouds.

  • 6. Koubarakis, M.
    et al.
    Bereta, K.
    Bilidas, D.
    Giannousis, K.
    Ioannidis, T.
    Pantazi, D. -A
    Stamoulis, G.
    Haridi, Seif
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Bruzzone, L.
    Paris, C.
    Eltoft, T.
    Krämer, T.
    Charalabidis, A.
    Karkaletsis, V.
    Konstantopoulos, S.
    Dowling, J.
    Kakantousis, T.
    Datcu, M.
    Dumitru, C. O.
    Appel, F.
    Bach, H.
    Migdall, S.
    Hughes, N.
    Arthurs, D.
    Fleming, A.
    From copernicus big data to extreme earth analytics2019Inngår i: Advances in Database Technology - EDBT, OpenProceedings, 2019, s. 690-693Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Copernicus is the European programme for monitoring the Earth. It consists of a set of systems that collect data from satellites and in-situ sensors, process this data and provide users with reliable and up-to-date information on a range of environmental and security issues. The data and information processed and disseminated puts Copernicus at the forefront of the big data paradigm, giving rise to all relevant challenges, the so-called 5 Vs: volume, velocity, variety, veracity and value. In this short paper, we discuss the challenges of extracting information and knowledge from huge archives of Copernicus data. We propose to achieve this by scale-out distributed deep learning techniques that run on very big clusters offering virtual machines and GPUs. We also discuss the challenges of achieving scalability in the management of the extreme volumes of information and knowledge extracted from Copernicus data. The envisioned scientific and technical work will be carried out in the context of the H2020 project ExtremeEarth which starts in January 2019.

  • 7. Lin, X.
    et al.
    Buyya, R.
    Yang, L.
    Tari, Z.
    Choo, K. -KR.
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Yao, L.
    Yin, H.
    Wang, W.
    Message from the BDCloud 2018 Chairs2019Inngår i: 16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018, s. XXIX-XXX, artikkel-id 8672358Artikkel i tidsskrift (Fagfellevurdert)
  • 8.
    Liu, Ying
    et al.
    KTH.
    Gureya, Daharewa
    KTH.
    Al-Shishtawy, Ahmad
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    OnlineElastMan: self-trained proactive elasticity manager for cloud-based storage services2017Inngår i: Cluster Computing, ISSN 1386-7857, E-ISSN 1573-7543, Vol. 20, nr 3, s. 1977-1994Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The pay-as-you-go pricing model and the illusion of unlimited resources in the Cloud initiate the idea to provision services elastically. Elastic provisioning of services allocates/de-allocates resources dynamically in response to the changes of the workload. It minimizes the service provisioning cost while maintaining the desired service level objectives (SLOs). Model-predictive control is often used in building such elasticity controllers that dynamically provision resources. However, they need to be trained, either online or offline, before making accurate scaling decisions. The training process involves tedious and significant amount of work as well as some expertise, especially when the model has many dimensions and the training granularity is fine, which is proved to be essential in order to build an accurate elasticity controller. In this paper, we present OnlineElastMan, which is a self-trained proactive elasticity manager for cloud-based storage services. It automatically evolves itself while serving the workload. Experiments using OnlineElastMan with Cassandra indicate that OnlineElastMan continuously improves its provision accuracy, i.e., minimizing provisioning cost and SLO violations, under various workload patterns.

  • 9. Liu, Ying
    et al.
    Gureya, Daharewa
    KTH, Skolan för informations- och kommunikationsteknik (ICT).
    Al-Shishtawy, Ahmad
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    OnlineElastMan: Self-Trained Proactive Elasticity Manager for Cloud-Based Storage Services2016Inngår i: 2016 INTERNATIONAL CONFERENCE ON CLOUD AND AUTONOMIC COMPUTING (ICCAC), Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 50-59Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The pay-as-you-go pricing model and the illusion of unlimited resources in the Cloud initiate the idea to provision services elastically. Elastic provisioning of services allocates/deallocates resources dynamically in response to the changes of the workload. It minimizes the service provisioning cost while maintaining the desired service level objectives (SLOs). Model-predictive control is often used in building such elasticity controllers that dynamically provision resources. However, they need to be trained, either online or offline, before making accurate scaling decisions. The training process involves tedious and significant amount of work as well as some expertise, especially when the model has many dimensions and the training granularity is fine, which is proved to be essential in order to build an accurate elasticity controller. In this paper, we present OnlineElastMan, which is a self-trained proactive elasticity manager for cloud-based storage services. It automatically trains and evolves itself while serving the workload. Experiments using OnlineElastMan with Cassandra indicate that OnlineElastMan continuously improves its provision accuracy, i.e., minimizing provisioning cost and SLO violations, under various workload patterns.

  • 10.
    Rameshan, Navaneeth
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Liu, Ying
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Navarro, Leandro
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Augmenting Elasticity Controllers for Improved Accuracy2016Inngår i: 2016 IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING (ICAC), IEEE Computer Society, 2016, s. 117-126Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Elastic resource provisioning is used to guarantee service level objectives (SLO) at reduced cost in a Cloud platform. However, performance interference in the hosting platform introduces uncertainty in the performance guarantees of provisioned services. Existing elasticity controllers are either unaware of this interference or over-provision resources to meet the SLO. In this paper, we show that assuming predictable performance of VMs in a multi-tenant environment to scale, will result in long periods of SLO violations. We augment the elasticity controller to be aware of interference and improve the convergence time of scaling without over provisioning. We perform experiments with Memcached and compare our solution against a baseline elasticity controller that is unaware of performance interference. Our results show that augmentation can reduce SLO violations by 65% or more and also save provisioning costs compared to an interference oblivious controller.

  • 11. Rameshan, Navaneeth
    et al.
    Liu, Ying
    KTH.
    Navarro, Leandro
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Elastic Scaling in the Cloud: A Multi-Tenant Perspective2016Inngår i: 2016 IEEE 36TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS WORKSHOPS (ICDCSW 2016), IEEE conference proceedings, 2016, s. 25-30Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Performance interference in the hosting platform introduces uncertainty in the performance guarantees of provisioned services. Existing elasticity controllers are either unaware of this interference or over-provision resources to meet the SLO. In this paper, we take a holistic view on elastic scaling from a multi-tenant perspective. We show that performance interference can significantly impact the accuracy of scaling and result in long periods of SLO violation. Using Memcached as a case-study, we show that making an elasticity controller interference aware can improve the accuracy of scaling decisions and significantly reduce the periods of SLO violation.

  • 12.
    Sozinov, Konstantin
    et al.
    KTH.
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Girdzijauskas, Sarunas
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Human Activity Recognition Using Federated Learning2018Inngår i: 2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS / [ed] Chen, JJ Yang, LT, IEEE COMPUTER SOC , 2018, s. 1103-1111Konferansepaper (Fagfellevurdert)
    Abstract [en]

    State-of-the-art deep learning models for human activity recognition use large amount of sensor data to achieve high accuracy. However, training of such models in a data center using data collected from smart devices leads to high communication costs and possible privacy infringement. In order to mitigate aforementioned issues, federated learning can be employed to train a generic classifier by combining multiple local models trained on data originating from multiple clients. In this work we evaluate federated learning to train a human activity recognition classifier and compare its performance to centralized learning by building two models, namely a deep neural network and a softmax regression trained on both synthetic and real-world datasets. We study communication costs as well as the influence of erroneous clients with corrupted data in federated learning setting. We have found that federated learning for the task of human activity recognition is capable of producing models with slightly worse, but acceptable, accuracy compared to centralized models. In our experiments federated learning achieved an accuracy of up to 89 % compared to 93 % in centralized training for the deep neural network. The global model trained with federated learning on skewed datasets achieves accuracy comparable to centralized learning. Furthermore, we identified an important issue of clients with corrupted data and proposed a federated learning algorithm that identifies and rejects erroneous clients. Lastly, we have identified a trade-off between communication cost and the complexity of a model. We show that more complex models such as deep neural network require more communication in federated learning settings for human activity recognition compared to less complex models, such as multinomial logistic regression.

  • 13.
    Trunfio, P.
    et al.
    DIMES, University of Calabria, Rende, Italy.
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Programvaruteknik och datorsystem, SCS.
    Clouds for scalable Big Data processing2019Inngår i: International Journal of Parallel, Emergent and Distributed Systems, ISSN 1744-5760, E-ISSN 1744-5779, Vol. 34, nr 6, s. 629-631Artikkel i tidsskrift (Fagfellevurdert)
  • 14.
    Vlassov, Vladimir
    et al.
    KTH.
    Bohn, R.
    Message from the ICCAC 2017 Program Chairs2017Inngår i: 4th IEEE International Conference on Cloud and Autonomic Computing, ICCAC 2017, artikkel-id 8064047Artikkel i tidsskrift (Fagfellevurdert)
  • 15. Xhagjika, V.
    et al.
    Escoda, O. D.
    Navarro, L.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Load and video performance patterns of a cloud based WebRTC Architecture2017Inngår i: Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017, Institute of Electrical and Electronics Engineers Inc. , 2017, s. 739-744Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Web Real-Time Communication or Realtime communication in the Web (WebRTC/RTCWeb) is a prolific new standard and technology stack, providing full audio/video agnostic communications for the Web. Service providers implementing such technology deal with various levels of complexity ranging anywhere from: high service distribution, multi-client integration, P2P and Cloud assisted communication backends, content delivery, real-Time constraints and across clouds resource allocation. This work presents a study of the joint factors including multi-cloud distribution, network performance, media parameters and back-end resource loads, in Cloud based Media Selective Forwarding Units for WebRTC infrastructures. The monitored workload is sampled from a large population of real users of our testing infrastructure, additionally the performance data is sampled both by passive user measurements as well as server side measurements. Patterns correlating such factors enable designing adaptive resource allocation algorithms and defining media Service Level Objectives (SLO) spanning over multiple data-centers or servers. Based on our analysis, we discover strong periodical load patterns even though the nature of user interaction with the system is mostly not predetermined with variable user churn.

  • 16.
    Xhagjika, Vamis
    et al.
    KTH.
    Divorra Escoda, Oscar
    Navarro, Leandro
    Vlassov, Vladimir
    KTH, Skolan för elektro- och systemteknik (EES).
    Media Streams Allocation and Load Patterns for a WebRTC Cloud Architecture2017Inngår i: PROCEEDINGS OF THE 2017 8TH INTERNATIONAL CONFERENCE ON THE NETWORK OF THE FUTURE (NOF) / [ed] Mahmoodi, T Secci, S Cianfrani, A Idzikowski, F, IEEE , 2017, s. 14-21Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Web Real-Time Communication Web Real-Time Communication (WebRTC) is seeing a rapid rise in adoption footprint. This standard provides an audio/video platform-agnostic communications framework for the Web build-in right in the browser. The complex technology stack of a full implementation of the standard is vast and includes elements of various computational disciplines like: content delivery, audio/video processing, media transport and quality of experience control, for both P2P and Cloud relayed communications. To the best of our knowledge, no previous study examines the impact of Cloud back-end load and media quality at production scale for a media stream processing application, as well as load mitigation for Cloud media Selective Forwarding Units. The contribution of this work is the analysis and exploitation of server workload (predictable session size, strong periodical load patterns) and media bit rate patterns that are derived from real user traffic (toward our test environment), over an extended period of time. Additionally, a simple and effective load balancing scheme is discussed to fairly distribute big sessions over multiple servers by exploiting the discovered patterns of stable session sizes and server load predictability. A Cloud simulation environment was built to compare the performance of the algorithm with other load allocation policies. This work is a basis for more advanced resource allocation algorithms and media Service Level Objectives (SLO) spanning multiple Cloud entities.

1 - 16 of 16
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf