ARIA, architecture for interactive arts, is a middleware to process, filter, and fuse sensory inputs and actuate responses in real-time. An ARIA media processing workflow describes how the data sensed through media will be processed and what audio-visual responses will be actuated. Each object streamed between ARIA processing components is subject to transformations, as described by a media workflow graph. The media capture and processing components, such as media filters and fusion operators, are programmable and adaptable; i.e, the delay, size, frequency, and quality/precision characteristics of individual operators can be controlled via a number of parameters. In this paper, we present the underlying model which captures the dynamic nature of the ARIA media processing workflows. This model enables design time verification, optimization, and runtime adaptation

Fundamental challenges in designing environments with media-rich ambient services involves not only the development of appropriate sensing technologies, but as importantly, the implementation of a distributed media processing system which can process, integrate, and leverage the sensed data in real time to provide the various services. In recent years, a great deal of progress has been made in media service workflow processing systems. In most existing solutions, however, the workflow nodes, which operate on the data, are selected out of a centrally assigned candidate pool. These candidate organizations cause either extensive resource provisioning or poor-quality operator mapping between logical workflow nodes and the available physical resources nodes. Consequently, instantiating a media processing workflow to the underlying hardware before the workflow execution begins does not lends itself to adaptive and autonomous operation of the workflow, scalable to resources and demand.In this paper, we propose a novel decentralized multimedia workflow processing system, DANS, in which operators defined in workflows are mapped into (distributed) physical nodes through Distributed Hash Table (DHT)-based overlay substrate in a purely decentralized and adaptive manner. The redundancy in the system, in terms of availability of multiple nodes able to perform the same task, enables the system to scale with demand. Furthermore, physical workflow nodes (operator instances) are able to locate and select the next filter or fusion operator instance autonomously, while ensuring the correct execution of the workflow.

Recently, we introduced a novel ARchitecture for Interactive Arts (ARIA) middleware that processes, filters, and fuses sensory inputs and actuates responses in real-time while providing various Quality of Service (QoS) guarantees. The objective of ARIA is to incorporate realtime, sensed, and archived media and audience responses into live performances, on demand. An ARIA media workflow graph describes how the data sensed through media capture devices will be processed and what audio-visual responses will be actuated. Thus, each data object streamed between ARIA processing components is subject to transformations, as described by a media workflow graph. The media capture and processing components, such as media filters and fusion operators, are programmable and adaptable; i.e, the delay, size, frequency, and quality/precision characteristics of individual operators can be controlled via a number of parameters. In [1, 4, 5], we developed static and dynamic optimization algorithms which maximize the quality of the actuated responses, minimize the corresponding delay and the resource usage. In this demonstration, we present the ARIA GUI and the underlying kernel. More specifically, we describe how to design a media processing workflow, with adaptive operators, using the ARIA GUI and how to use the various optimization and adaptation alternatives provided by the ARIA kernel to execute media processing workflows.

This paper proposes the design issues and implementations of management functions, which belong to the management plane in the protocol reference model (PRM), for the UNI/NNI resources in the HANbit ACE64 ATM switching system. We define the target resources of management functions, and then determine the list of management functions of the HANbit ACE64 following the recommendation 1.610 of the ITU-T. We evaluate the fitness of the management functions of the HANbit ACE64 by comparing them with functions in the 1.610. Also, this paper gives an account of the relation between the control plane and the management plane in the HANbit ACE64 ATM switching system.