Towards SVC-based adaptive streaming in information centric networks

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for video streaming services. In HAS, each video is segmented and stored in different qualities. The client can dynamically select the most appropriate quality level to download, allowing it to adapt to varying network conditions. As the Internet was not designed to deliver such applications, optimal support for multimedia delivery is still missing. Information Centric Networking (ICN) is a recently proposed disruptive architecture that could solve this issue, where the focus is given to the content rather than to end-to-end connectivity. Due to the bandwidth unpredictability typical of ICN, standard AVC-based HAS performs quality selection sub-optimally, thus leading to a poor Quality of Experience (QoE). In this article, we propose to overcome this inefficiency by using Scalable Video Coding (SVC) instead. We individuate the main advantages of SVC-based HAS over ICN and outline, both theoretically and via simulation, the research challenges to be addressed to optimize the delivered QoE

Forecasting international bandwidth capacity using linear and ANN methods

An artificial neural network (ANN) can improve forecasts through pattern recognition of historical data. This article evaluates the reliability of ANN methods, as opposed to simple extrapolation techniques, to forecast Internet bandwidth index data that is bursty in nature. A simple feedforward ANN model is selected as a nonlinear alternative, as it is flexible enough to model complex linear or nonlinear relationships without any prior assumptions about the data generating process. These data are virtually white noise and provides a challenge to forecasters. Using standard forecast error statistics, the ANN and the simple exponential smoothing model provide modestly better forecasts than other extrapolation methodsForecasting; international bandwidth capacity

Technology requirements for post-1985 communications satellites

The technical and functional requirements for commercial communication satellites are discussed. The need for providing quality service at an acceptable cost is emphasized. Specialized services are postulated in a needs model which forecasts future demands. This needs model is based upon 322 separately identified needs for long distance communication. It is shown that the 1985 demand for satellite communication service for a domestic region such as the United States, and surrounding sea and air lanes, may require on the order of 100,000 MHz of bandwith. This level of demand can be met by means of the presently allocated bandwidths and developing some key technologies. Suggested improvements include: (1) improving antennas so that high speed switching will be possible; (2) development of solid state transponders for 12 GHz and possibly higher frequencies; (3) development of switched or steered beam antennas with 10 db or higher gain for aircraft; and (4) continued development of improved video channel compression techniques and hardware

Technology requirements for communication satellites in the 1980's

The key technology requirements are defined for meeting the forecasted demands for communication satellite services in the 1985 to 1995 time frame. Evaluation is made of needs for services and technical and functional requirements for providing services. The future growth capabilities of the terrestrial telephone network, cable television, and satellite networks are forecasted. The impact of spacecraft technology and booster performance and costs upon communication satellite costs are analyzed. Systems analysis techniques are used to determine functional requirements and the sensitivities of technology improvements for reducing the costs of meeting requirements. Recommended development plans and funding levels are presented, as well as the possible cost saving for communications satellites in the post 1985 era

An Algorithm for Network and Data-aware Placement of Multi-Tier Applications in Cloud Data Centers

Today's Cloud applications are dominated by composite applications comprising multiple computing and data components with strong communication correlations among them. Although Cloud providers are deploying large number of computing and storage devices to address the ever increasing demand for computing and storage resources, network resource demands are emerging as one of the key areas of performance bottleneck. This paper addresses network-aware placement of virtual components (computing and data) of multi-tier applications in data centers and formally defines the placement as an optimization problem. The simultaneous placement of Virtual Machines and data blocks aims at reducing the network overhead of the data center network infrastructure. A greedy heuristic is proposed for the on-demand application components placement that localizes network traffic in the data center interconnect. Such optimization helps reducing communication overhead in upper layer network switches that will eventually reduce the overall traffic volume across the data center. This, in turn, will help reducing packet transmission delay, increasing network performance, and minimizing the energy consumption of network components. Experimental results demonstrate performance superiority of the proposed algorithm over other approaches where it outperforms the state-of-the-art network-aware application placement algorithm across all performance metrics by reducing the average network cost up to 67% and network usage at core switches up to 84%, as well as increasing the average number of application deployments up to 18%.Comment: Submitted for publication consideration for the Journal of Network and Computer Applications (JNCA). Total page: 28. Number of figures: 15 figure