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References 1, 5 introduce a transport protocol that o ers partially ordered service for multimedia applications. This paper investigates how m uch the selection of a linear extension a ects system performance in a partially ordered service. We rst show h o w t o identify better linear extensions of a partial order, and then determine the performance gains by using such linear extensions at the time of transmission. To quantify linear extensions of a partial order, we propose a new metric pBuf -metric that is derived from bu ering probabilities. Since pBuf -metric is complex to calculate, a simpli ed version called -metric is also investigated. An OPNET simulation shows that for certain partial orders, a linear extension optimized according to these metrics provides some delay, and signi cant bu er utilization improvements over a non-optimal linear extension. Thus, prudent transmission order selection in a partially ordered service does improve system performance. Results also show that, in general, -metric is as e ective a s pBuf -metric in identifying better linear extensions of a partial order.

The IoT platforms must allow the communication between the Applications and Devices according to their non-functional requirements. One of the main non-functional requirements is the Quality of Service (QoS). In a previous work has been defined an Autonomic Internet of Things (IoT) platform for the QoS Management, based on the concept of autonomic cycle of data analysis tasks. In this platform have been defined two autonomic cycles, one based on a classification task that determines the current operational state to define the set of tasks to execute in the communication system to guarantee a given QoS. The other one is based on a clustering task that discovers the current operational state, and based on it, determines the set of tasks to be executed in the communication system. This paper analyzes the diagnostic capabilities of the system based on both approaches, using different metrics. For that, a real scenario has been considered, with simulations that have generated data to test both tasks. Each technique has different aspects to be considered for a correct QoS management in the context of IoT platforms. The classification technique can determine very well the learned operational states, but the clustering approach can carry out a more detailed description of the operational states. Additionally, due to the classification and clustering technique used, called LAMDA (Learning Algorithm for Multivariate Data Analysis), the paper analyzes the operational state profile determined by them, which is very useful in a diagnostic process. IntroductionThe classical components of an IoT ecosystem, according to the standards are [1][2]: Devices, Network, IoT platform, and Application. Devices are the responsible of the collection of the data, and in some case, the preprocessing of these data and the execution of specific tasks; the Applications exploit the advantage of a set of interconnected Devices in order to carry out actions in the environment; the Network allows the communication between the different components in the IoT platform, particularly, between the devices and applications; and finally, the IoT platform manages smart capabilities like the autonomy, security, among other things, and can support heterogeneous non-functional requirements of Applications and Devices. On the one hand, the IoT platforms are based on European Telecommunications Standards Institute (ETSI) SmartM2M and OneM2M [1][2] specifications, which define that an IoT platform consists mainly of two types of entities, IoT server(s) and gateway(s). These entities are implemented as Chain of Network Functions (NFs) that achieve a connection between applications and devices. Namely, NFs are processing functions, with defined functional behaviors and interfaces (e.g. a load balancing function applied on Internet protocol (IP) packets). On the other hand, these platforms are not able to sustain Quality of Service (QoS) to IoT missions critical Applications like remote surgery [34]. As per [34], QoS in IoT is one of the critical factors, which nee...

This paper presents an architecture that enforces time requirements and gives minimal end-to-end delays for multimedia applications. The layers and mechanisms allowing the system to fulfill the selected synchronization, i.e., the logical relationships and timed interval semantics, are presented. The proposed approach relies on the use of a formal model based on extended time Petri nets, i.e., the time stream Petri net model (TStreamPN), that allows the user to completely specify the time requirements of a given application. The architecture implements, in the application layer and on top of asynchronous environments, the requested quality of service (perceived by the user) with respect to time. At the transport layer, the use of a partial order transport service improves the reactive response of the communication transfers. Its principles are presented together with a presynchronization sublayer that makes the partial order transport service match the applicative synchronization requirements. Moreover, measurements on the implementation of a videoconference system show that the requirements of the quality of service are fulfilled.

The Internet of things (IoT) has evolved exceptionally in recent years; enabling a large number of heterogeneous devices to be interconnected to users via the Internet. This new concept promises in a few years to interconnect billions of devices, which will generate many challenges on the infrastructure supporting these communications. One of these challenges is the satisfaction of the different QoS requirements of the applications. To address this challenge, we identified two bottlenecks with respect to the QoS, which are the networks and the intermediate entities (i.e. middleware) allowing the applications to interact with the devices. In this paper, we propose a modular framework to ensure the QoS of applications at the middleware-level through QoS-oriented mechanisms deployed dynamically and autonomously on the middleware entities. The benefits of this framework are presented through test scenarios in the vehicular transportation domain.

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In the recent years, telecom and computer networks have witnessed new concepts and technologies through Network Function Virtualization (NFV) and Software-Defined Networking (SDN). SDN, which allows applications to have a control over the network, and NFV, which allows deploying network functions in virtualized environments, are two paradigms that are increasingly used for the Internet of Things (IoT). This Internet (IoT) brings the promise to interconnect billions of devices in the next few years rises several scientific challenges in particular those of the satisfaction of the quality of service (QoS) required by the IoT applications. In order to address this problem, we have identified two bottlenecks with respect to the QoS: the traversed networks and the intermediate entities that allows the application to interact with the IoT devices. In this paper, we first present an innovative vision of a "network function" with respect to their deployment and runtime environment. Then, we describe our general approach of a solution that consists in the dynamic, autonomous, and seamless deployment of QoS management mechanisms. We also describe the requirements for the implementation of such approach. Finally, we present a redirection mechanism, implemented as a network function, allowing the seamless control of the data path of a given middleware traffic. This mechanism is assessed through a use case related to vehicular transportation.

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