Recommendation Y.3600 provides requirements, capabilities and use cases of cloud computing based big data as well as its system context. Cloud computing based big data provides the capabilities to collect, store, analyze, visualize and manage varieties of large volume datasets, which cannot be rapidly transferred and analysed using traditional technologies.
Recommendation ITU-T Y.3519 describes the functional architecture for big data as a service (BDaaS). The functional architecture is defined on the basis of the analysis of requirements and activities of cloud computing-based big data described in Recommendation ITU-T Y.3600.
In a mobile network, a great deal of traffic data which reflects the real status of the mobile network and customers' actual experience is generated all the time. Based on the big data generated from the mobile network more efficient management and reasonable planning of mobile networks can be achieved.
Recommendation ITU-T Y.3601 provides a framework for data exchange in a big data ecosystem. Big data exchange covers multiple processes for data import and data export within a big data ecosystem. Big data exchange is used for exchanging data of multiple types and multiple formats from a data source to a data target.
This Recommendation specifies framework of big data driven networking. The scope of this recommendation includes the model architecture of big data driven networking (bDDN), the high-level capabilities of bDDN, the interface capabilities among different planes and layers.
The present document provides a contribution to the evolution of network performance testing towards a professional degree of transparency. This begins with a consistent framework of definitions and technical terms. The elements of the testing process are then described within this context.
Apart from the obvious direct parameters of throughput testing, such as time windows or transferred data volumes, there are numerous other elements which can have an impact on data values obtained. In this sense, methodology and definition of metrics cannot be decoupled from each other. The process starts with selecting the boundaries to the system under test, i.e. insertion or demarcation points. Next comes the way the system under test is accessed. For instance, if the test is run over a radio access network using a mobile device such as a smartphone, the type and degree of influence needs to be assessed. The type of stimulus is likewise important, such as the protocol type, the structure of data traffic (e.g. TCP or UDP based), and the number of parallel connections. Depending on these selections, other choices also become parameters for testing. An example would be to use some kind of real application to create a particular type of traffic, versus using synthetically generated traffic.
Standardization of coded representation of audio, picture, multimedia and hypermedia information - and sets of compression and control functions for use with such information - such as:
Excluded: Character Coding
The present document's scope is to provide guidance on OTT video streaming testing approach with a set of minimum desired and most meaningful QoE centric QoS parameters along with recommendations to create a figure of merit quantifying the OTT video streaming session quality, where possible. In addition, the set of introduced QoE centric QoS parameters aim to help with the identification of the possible roots of video quality degradation. The present document also offers means to understand aspects related with network and services optimization and troubleshooting, such as the trade-off between bandwidth usage or controlled throttling and end-to-end video quality.
