Crowdsourced Live Streaming over the Cloud

Empowered by today's rich tools for media generation and distribution, and the convenient Internet access, crowdsourced streaming generalizes the single-source streaming paradigm by including massive contributors for a video channel. It calls a joint optimization along the path from crowdsourcers, through streaming servers, to the end-users to minimize the overall latency. The dynamics of the video sources, together with the globalized request demands and the high computation demand from each sourcer, make crowdsourced live streaming challenging even with powerful support from modern cloud computing. In this paper, we present a generic framework that facilitates a cost-effective cloud service for crowdsourced live streaming. Through adaptively leasing, the cloud servers can be provisioned in a fine granularity to accommodate geo-distributed video crowdsourcers. We present an optimal solution to deal with service migration among cloud instances of diverse lease prices. It also addresses the location impact to the streaming quality. To understand the performance of the proposed strategies in the realworld, we have built a prototype system running over the planetlab and the Amazon/Microsoft Cloud. Our extensive experiments demonstrate that the effectiveness of our solution in terms of deployment cost and streaming quality

Cost Minimization of Virtual Machine Allocation in Public Clouds Considering Multiple Applications

International Conference, GECON 2017 (14. 2017. Biarritz)This paper presents a virtual machine (VM) allocation strategy to optimize the cost of VM deployments in public clouds. It can simultaneously deal with multiple applications and it is formulated as an optimization problem that takes the level of performance to be reached by a set of applications as inputs. It considers real characteristics of infrastructure providers such as VM types, limits on the number VMs that can be deployed, and pricing schemes. As output, it generates a VM allocation to support the performance requirements of all the applications. The strategy combines short-term and long-term allocation phases in order to take advantage of VMs belonging to two different pricing categories: on-demand and reserved. A quantization technique is introduced to reduce the size of the allocation problem and, thus, significantly decrease the computational complexity. The experiments show that the strategy can optimize costs for problems that could not be solved with previous approache

SDN/NFV-enabled satellite communications networks: opportunities, scenarios and challenges

In the context of next generation 5G networks, the satellite industry is clearly committed to revisit and revamp the role of satellite communications. As major drivers in the evolution of (terrestrial) fixed and mobile networks, Software Defined Networking (SDN) and Network Function Virtualisation (NFV) technologies are also being positioned as central technology enablers towards improved and more flexible integration of satellite and terrestrial segments, providing satellite network further service innovation and business agility by advanced network resources management techniques. Through the analysis of scenarios and use cases, this paper provides a description of the benefits that SDN/NFV technologies can bring into satellite communications towards 5G. Three scenarios are presented and analysed to delineate different potential improvement areas pursued through the introduction of SDN/NFV technologies in the satellite ground segment domain. Within each scenario, a number of use cases are developed to gain further insight into specific capabilities and to identify the technical challenges stemming from them.Peer ReviewedPostprint (author's final draft

Algorithms for advance bandwidth reservation in media production networks

Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results

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