QoE-Aware Resource Allocation For Crowdsourced Live Streaming: A Machine Learning Approach

In the last decade, empowered by the technological advancements of mobile devices and the revolution of wireless mobile network access, the world has witnessed an explosion in crowdsourced live streaming. Ensuring a stable high-quality playback experience is compulsory to maximize the viewers’ Quality of Experience and the content providers’ profits. This can be achieved by advocating a geo-distributed cloud infrastructure to allocate the multimedia resources as close as possible to viewers, in order to minimize the access delay and video stalls. Additionally, because of the instability of network condition and the heterogeneity of the end-users capabilities, transcoding the original video into multiple bitrates is required. Video transcoding is a computationally expensive process, where generally a single cloud instance needs to be reserved to produce one single video bitrate representation. On demand renting of resources or inadequate resources reservation may cause delay of the video playback or serving the viewers with a lower quality. On the other hand, if resources provisioning is much higher than the required, the extra resources will be wasted. In this thesis, we introduce a prediction-driven resource allocation framework, to maximize the QoE of viewers and minimize the resources allocation cost. First, by exploiting the viewers’ locations available in our unique dataset, we implement a machine learning model to predict the viewers’ number near each geo-distributed cloud site. Second, based on the predicted results that showed to be close to the actual values, we formulate an optimization problem to proactively allocate resources at the viewers’ proximity. Additionally, we will present a trade-off between the video access delay and the cost of resource allocation. Considering the complexity and infeasibility of our offline optimization to respond to the volume of viewing requests in real-time, we further extend our work, by introducing a resources forecasting and reservation framework for geo-distributed cloud sites. First, we formulate an offline optimization problem to allocate transcoding resources at the viewers’ proximity, while creating a tradeoff between the network cost and viewers QoE. Second, based on the optimizer resource allocation decisions on historical live videos, we create our time series datasets containing historical records of the optimal resources needed at each geo-distributed cloud site. Finally, we adopt machine learning to build our distributed time series forecasting models to proactively forecast the exact needed transcoding resources ahead of time at each geo-distributed cloud site. The results showed that the predicted number of transcoding resources needed in each cloud site is close to the optimal number of transcoding resources

Cloud media video encoding:review and challenges

In recent years, Internet traffic patterns have been changing. Most of the traffic demand by end users is multimedia, in particular, video streaming accounts for over 53%. This demand has led to improved network infrastructures and computing architectures to meet the challenges of delivering these multimedia services while maintaining an adequate quality of experience. Focusing on the preparation and adequacy of multimedia content for broadcasting, Cloud and Edge Computing infrastructures have been and will be crucial to offer high and ultra-high definition multimedia content in live, real-time, or video-on-demand scenarios. For these reasons, this review paper presents a detailed study of research papers related to encoding and transcoding techniques in cloud computing environments. It begins by discussing the evolution of streaming and the importance of the encoding process, with a focus on the latest streaming methods and codecs. Then, it examines the role of cloud systems in multimedia environments and provides details on the cloud infrastructure for media scenarios. After doing a systematic literature review, we have been able to find 49 valid papers that meet the requirements specified in the research questions. Each paper has been analyzed and classified according to several criteria, besides to inspect their relevance. To conclude this review, we have identified and elaborated on several challenges and open research issues associated with the development of video codecs optimized for diverse factors within both cloud and edge architectures. Additionally, we have discussed emerging challenges in designing new cloud/edge architectures aimed at more efficient delivery of media traffic. This involves investigating ways to improve the overall performance, reliability, and resource utilization of architectures that support the transmission of multimedia content over both cloud and edge computing environments ensuring a good quality of experience for the final user

Machine Learning Algorithms for Provisioning Cloud/Edge Applications

Mención Internacional en el título de doctorReinforcement Learning (RL), in which an agent is trained to make the most favourable decisions in the long run, is an established technique in artificial intelligence. Its popularity has increased in the recent past, largely due to the development of deep neural networks spawning deep reinforcement learning algorithms such as Deep Q-Learning. The latter have been used to solve previously insurmountable problems, such as playing the famed game of “Go” that previous algorithms could not. Many such problems suffer the curse of dimensionality, in which the sheer number of possible states is so overwhelming that it is impractical to explore every possible option. While these recent techniques have been successful, they may not be strictly necessary or practical for some applications such as cloud provisioning. In these situations, the action space is not as vast and workload data required to train such systems is not as widely shared, as it is considered commercialy sensitive by the Application Service Provider (ASP). Given that provisioning decisions evolve over time in sympathy to incident workloads, they fit into the sequential decision process problem that legacy RL was designed to solve. However because of the high correlation of time series data, states are not independent of each other and the legacy Markov Decision Processes (MDPs) have to be cleverly adapted to create robust provisioning algorithms. As the first contribution of this thesis, we exploit the knowledge of both the application and configuration to create an adaptive provisioning system leveraging stationary Markov distributions. We then develop algorithms that, with neither application nor configuration knowledge, solve the underlying Markov Decision Process (MDP) to create provisioning systems. Our Q-Learning algorithms factor in the correlation between states and the consequent transitions between them to create provisioning systems that do not only adapt to workloads, but can also exploit similarities between them, thereby reducing the retraining overhead. Our algorithms also exhibit convergence in fewer learning steps given that we restructure the state and action spaces to avoid the curse of dimensionality without the need for the function approximation approach taken by deep Q-Learning systems. A crucial use-case of future networks will be the support of low-latency applications involving highly mobile users. With these in mind, the European Telecommunications Standards Institute (ETSI) has proposed the Multi-access Edge Computing (MEC) architecture, in which computing capabilities can be located close to the network edge, where the data is generated. Provisioning for such applications therefore entails migrating them to the most suitable location on the network edge as the users move. In this thesis, we also tackle this type of provisioning by considering vehicle platooning or Cooperative Adaptive Cruise Control (CACC) on the edge. We show that our Q-Learning algorithm can be adapted to minimize the number of migrations required to effectively run such an application on MEC hosts, which may also be subject to traffic from other competing applications.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Antonio Fernández Anta.- Secretario: Diego Perino.- Vocal: Ilenia Tinnirell

Network-aware video streaming for future media Internet

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Exploring manycore architectures for next-generation HPC systems through the MANGO approach

[EN] The Horizon 2020 MANGO project aims at exploring deeply heterogeneous accelerators for use in High-Performance Computing systems running multiple applications with different Quality of Service (QoS) levels. The main goal of the project is to exploit customization to adapt computing resources to reach the desired QoS. For this purpose, it explores different but interrelated mechanisms across the architecture and system software. In particular, in this paper we focus on the runtime resource management, the thermal management, and support provided for parallel programming, as well as introducing three applications on which the project foreground will be validated.This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 671668.Flich Cardo, J.; Agosta, G.; Ampletzer, P.; Atienza-Alonso, D.; Brandolese, C.; Cappe, E.; Cilardo, A.... (2018). Exploring manycore architectures for next-generation HPC systems through the MANGO approach. Microprocessors and Microsystems. 61:154-170. https://doi.org/10.1016/j.micpro.2018.05.011S1541706