A Survey on Energy Consumption and Environmental Impact of Video Streaming

Climate change challenges require a notable decrease in worldwide greenhouse gas (GHG) emissions across technology sectors. Digital technologies, especially video streaming, accounting for most Internet traffic, make no exception. Video streaming demand increases with remote working, multimedia communication services (e.g., WhatsApp, Skype), video streaming content (e.g., YouTube, Netflix), video resolution (4K/8K, 50 fps/60 fps), and multi-view video, making energy consumption and environmental footprint critical. This survey contributes to a better understanding of sustainable and efficient video streaming technologies by providing insights into the state-of-the-art and potential future directions for researchers, developers, and engineers, service providers, hosting platforms, and consumers. We widen this survey's focus on content provisioning and content consumption based on the observation that continuously active network equipment underneath video streaming consumes substantial energy independent of the transmitted data type. We propose a taxonomy of factors that affect the energy consumption in video streaming, such as encoding schemes, resource requirements, storage, content retrieval, decoding, and display. We identify notable weaknesses in video streaming that require further research for improved energy efficiency: (1) fixed bitrate ladders in HTTP live streaming; (2) inefficient hardware utilization of existing video players; (3) lack of comprehensive open energy measurement dataset covering various device types and coding parameters for reproducible research

Review and analysis of networking challenges in cloud computing

Cloud Computing offers virtualized computing, storage, and networking resources, over the Internet, to organizations and individual users in a completely dynamic way. These cloud resources are cheaper, easier to manage, and more elastic than sets of local, physical, ones. This encourages customers to outsource their applications and services to the cloud. The migration of both data and applications outside the administrative domain of customers into a shared environment imposes transversal, functional problems across distinct platforms and technologies. This article provides a contemporary discussion of the most relevant functional problems associated with the current evolution of Cloud Computing, mainly from the network perspective. The paper also gives a concise description of Cloud Computing concepts and technologies. It starts with a brief history about cloud computing, tracing its roots. Then, architectural models of cloud services are described, and the most relevant products for Cloud Computing are briefly discussed along with a comprehensive literature review. The paper highlights and analyzes the most pertinent and practical network issues of relevance to the provision of high-assurance cloud services through the Internet, including security. Finally, trends and future research directions are also presented

Scheduling Live-Migrations for Fast, Adaptable and Energy-Efficient Relocation Operations

International audienceEvery day, numerous VMs are migrated inside a datacenter to balance the load, save energy or prepare production servers for maintenance. Despite VM placement problems are carefully studied, the underlying migration scheduler rely on vague adhoc models. This leads to unnecessarily long and energy-intensive migrations. We present mVM, a new and extensible migration scheduler. mVM takes into account the VM memory workload and the network topology to estimate precisely the migration duration and take wiser scheduling decisions. mVM is implemented as a plugin of BtrPlace and can be customized with additional scheduling constraints to finely control the migrations. Experiments on a real testbed show mVM outperforms schedulers that cap the migration parallelism by a constant to reduce the completion time. Besides an optimal capping, mVM reduces the migration duration by 20.4% on average and the completion time by 28.1%. In a maintenance operation involving 96 VMs to migrate between 72 servers, mVM saves 21.5% Joules against BtrPlace. Finally, its current library of 6 constraints allows administrators to address temporal and energy concerns, for example to adapt the schedule and fit a power budget

General QoS-Aware Scheduling Procedure for Passive Optical Networks

Increasing volume, dynamism, and diversity of access traffic have complicated the challenging problem of dynamic resource allocation in passive optical networks. We introduce a general scheduling procedure for passive optical networks, which optimizes a desired performance metric for an arbitrary set of operational constraints. The proposed scheduling has a fast and causal iterative implementation, where each iteration involves a local optimization problem followed by a recursive update of some status information. The generality of the platform enables a proper description of the diverse quality of service requirements, while its low computational complexity makes agile tracking of the network dynamism possible. To demonstrate its versatility and generality, the applications of the scheme for service-differentiated dynamic bandwidth allocation in time- and wavelength-division-multiplexed passive optical networks are discussed. To further reduce the computational complexity, a closed-form solution of the involved optimization in each iteration of the scheduling is derived. We directly incorporate transmission delay in the scheduling and show how the consumed power is traded for the tolerable amount of transmission delay. Furthermore, a 50% power efficiency improvement is reported by exploiting the inherent service diversity among subscribers. The impact of service prioritization, finite buffer length, and packet drops on the power efficiency of the scheme are also investigated

On the multiresolution structure of Internet traffic traces

Internet traffic on a network link can be modeled as a stochastic process. After detecting and quantifying the properties of this process, using statistical tools, a series of mathematical models is developed, culminating in one that is able to generate ``traffic'' that exhibits --as a key feature-- the same difference in behavior for different time scales, as observed in real traffic, and is moreover indistinguishable from real traffic by other statistical tests as well. Tools inspired from the models are then used to determine and calibrate the type of activity taking place in each of the time scales. Surprisingly, the above procedure does not require any detailed information originating from either the network dynamics, or the decomposition of the total traffic into its constituent user connections, but rather only the compliance of these connections to very weak conditions.Comment: 57 pages, color figures. Figures are of low quality due to space consideration