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  • 1.
    Liu, Ying
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Xhagjika, Vamis
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS. Universitat Politecnica de Catalunya, Spain.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Shishtawy, A. A.
    BwMan: Bandwidth manager for elastic services in the cloud2014In: Proceedings - 2014 IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2014, IEEE , 2014, p. 217-224Conference paper (Refereed)
    Abstract [en]

    The flexibility of Cloud computing allows elastic services to adapt to changes in workload patterns in order to achieve desired Service Level Objectives (SLOs) at a reduced cost. Typically, the service adapts to changes in workload by adding or removing service instances (VMs), which for stateful services will require moving data among instances. The SLOs of a distributed Cloud-based service are sensitive to the available network bandwidth, which is usually shared by multiple activities in a single service without being explicitly allocated and managed as a resource. We present the design and evaluation of BwMan, a network bandwidth manager for elastic services in the Cloud. BwMan predicts and performs the bandwidth allocation and tradeoffs between multiple service activities in order to meet service specific SLOs and policies. To make management decisions, BwMan uses statistical machine learning (SML) to build predictive models. This allows BwMan to arbitrate and allocate bandwidth dynamically among different activities to satisfy specified SLOs. We have implemented and evaluated BwMan for the OpenStack Swift store. Our evaluation shows the feasibility and effectiveness of our approach to bandwidth management in an elastic service. The experiments show that network bandwidth management by BwMan can reduce SLO violations in Swift by a factor of two or more.

  • 2.
    Xhagjika, Vamis
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS. Polytechnic University of Catalonia.
    Resource, Data and Application Management for Cloud Federations and Multi-Clouds2017Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Distributed Real-Time Media Processing refers to classes of highly distributed, delay no-tolerant applications that account for the majority of the data traffic generated in the world today. Real-Time audio/video conferencing and live content streaming are of particular research interests as technology forecasts predict video traffic surpassing every other type of data traffic in the world in the near future. Live streaming refers to applications in which audio/video streams from a source need to be delivered to a set of geo-distributed destinations while maintaining low latency of stream delivery. Real-time conferencing platforms are application platforms that implement many-to-many audio/video real-time communications. Both of these categories exhibit high sensitivity to both network state (latency, jitter, packet loss, bit rate) as well as stream processing backend load profiles (latency and jitter introduced as Cloud processing of media packets). This thesis addresses enhancing real-time media processing both at the network level parameters as well as Cloud optimisations.

    We provide a novel, bandwidth management algorithm, for cloud services sharing the same network infrastructure, which provides a 2x improvement in system stability. Further examining network impact on cloud services, we provide a novel hybrid Cloud-Network distributed Cloud architecture to enable locality aware, application enhancements. This architecture led to a multi-cloud management overlay algorithm that maintains low management overhead on large scale cloud deployments. On the application level we provide a study of Media Quality parameters for a WebRTC enabled Media Cloud back-end, and provide patterns of quality metrics with respect to back-end stream load and network parameters. Additionally we empirically show that a "minimal load" algorithm for stream allocation, outperforms other Rotational, or Static Threshold based algorithms.

  • 3.
    Xhagjika, Vamis
    et al.
    KTH.
    Divorra Escoda, Oscar
    Navarro, Leandro
    Vlassov, Vladimir
    KTH, School of Electrical Engineering (EES).
    Media Streams Allocation and Load Patterns for a WebRTC Cloud Architecture2017In: 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, p. 14-21Conference paper (Refereed)
    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.

  • 4.
    Xhagjika, Vamis
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Molin, Magnus
    Toma, Simona
    Structured Cloud federation for Carrier and ISP infrastructure2014In: 2014 IEEE 3rd International Conference on Cloud Networking, CloudNet 2014, 2014, p. 20-26Conference paper (Refereed)
    Abstract [en]

    Cloud Computing in recent years has seen enhanced growth and extensive support by the research community and industry. The advent of cloud computing realized the concept of commodity computing, in which infrastructure (resources) can be allocated on demand giving the illusion of infinite resource availability. The state-of-art Carrier and ISP infrastructure technology is composed of tightly coupled software services with the underlying customized hardware architecture. The fast growth of cloud computing as a vastly consolidated and stabilized technology is appealing to Carrier Providers in order to reduce Carrier deployment costs and enable a future of Carrier Clouds with easily accessible virtual carriers. For such migration to happen software services need to be generalized, to decouple hardware and software, and prepared to move into the Cloud. The network backbone is centrally managed and only provides network connectivity. We believe this presents an opportunity. The edges of such networks and the core are interconnected with high performance links. If services could be deployed in these edges they would benefit from enhanced locality to the user. In this position paper we propose a distributed cloud architecture (precisely a structured multi-cloud federated infrastructure), with minimal impact on existing infrastructure, as a first step to incorporate the Cloud into the network infrastructure of such providers, enabling and enhancing novel and existing applications.

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