Quality Of Service Settings Research Articles

Service selection model based on user intention and context

The internet interconnects billions of objects and services. Real-world services are offered by entities with various functions communicating with one another. The selection of services based on their functionality is a complex process because as the number of services rises, so does the number of services that offer the same functionality. Quality of service (QoS) can be a criterion for selecting a suitable service. However, QoS' relative significance fluctuates due to changing user preferences, and users may exhibit various behaviors depending on their contexts and intentions. Defining a user's preference based on user intentions and context is similarly challenging; nonetheless, scholars have paid little attention to this topic. This study provides a new model for service selection based on user intentions and context. The model dynamically selects the appropriate set of QoS with their importance to specify a user preference for various behaviors. The issue of assessing user preference to select the desired service is resolved by calculating the QoS importance based on the user's behavior history and context. The study proposed a dynamic K-Skyline method to optimize a search space and a multi-criteria decision-making technique to select and rank services efficiently. A case study and an experiment demonstrating the proposed model are presented, in which real-world datasets are utilized. The experimental results of the proposed model validate the model's efficiency and robustness.

Machine Monitoring Protocols Based on Quality of Service (QoS) to Improve Performance of Real-Time Industrial Applications

A major factor in the optimization of real-time industrial applications is Quality of Service (QoS). There is also a quality-of-service parameter (QoS). Normally, dynamic networks require QoS settings for real-time applications. The industrial production environment is changing due to rapid advances in ICT. Industries have increased output due to improved manufacturing chain efficiency, flexibility, and security. Establishing a secure, reliable, and fast network in the sector is therefore critical. In industrial applications, data collection is an important factor to consider. Various routing methods exist for collecting data from industrial environments and sending it to processing. In this situation, the energy consumption of device nodes and data transmission delay are important factors to consider. This research proposes to construct an energy-efficient delay-aware WSN routing (EDR) protocol for defect detection. The clustering group optimal approach is used to identify which clusters should be selected. A modified task selection method is proposed to calculate the most efficient path between industrial areas and data centres (monitoring rooms). The node selection approach is used to find the network’s aggregate node. The Network Simulator (NS2) programmed is used to model the projected IEM-EDR system. The proposed Industrial Energy Monitoring-Energy-Efficient Delay-Aware Routing (IEM-EDR) system’s performance is compared to that of existing techniques in hop count, latency, energy consumption, throughput, and delivery rate.

Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models

Numerous middleware application programming interfaces (APIs) and protocols were introduced in the literature in order to facilitate the application development of the Internet of Things (IoT). Such applications are built on reliable or even unreliable protocols that may implement different quality-of-service (QoS) delivery modes. The exploitation of these protocols, APIs and QoS modes, can satisfy QoS requirements in critical IoT applications (e.g., emergency response operations). To study QoS in IoT applications, it is essential to leverage a performance analysis methodology. Queueing-network models offer a modeling and analysis framework that can be adopted for the IoT interactions of QoS representation through either analytical or simulation models. In this paper, various types of queueing models are presented that can be used for the representation of various QoS settings of IoT interactions. In particular, we propose queueing models to represent message-drop probabilities, intermittent mobile connectivity, message availability or validity, the prioritization of important information, and the processing or transmission of messages. Our simulation models demonstrate the significant effect on delivery success rates and response times when QoS settings are varied.

Revisiting LTE LAA: Channel Access, QoS, and Coexistence with WiFi

Network operators are looking toward LTE License Assisted Access (LAA) as a means of extending capacity by offloading traffic to unlicensed bands. However, operation in these bands requires abiding by certain coexistence rules in terms of channel access. The description of these rules in the existing literature is not always in line with the latest standards. Therefore, in this article, we clarify the operation of LAA, focusing on channel access and methods of providing quality of service (QoS) support. In terms of coexistence, we evaluate the impact of LAA under its various QoS settings on WiFi performance in an experimental testbed. Finally, we describe the upcoming research challenges for LTE-based technologies in unlicensed bands considering the latest developments.

Framework for web service composition based on QoS in the multi cloud environment

Recently, the term cloud computing is widely used in the searching community, which shows the importance given by the scientists to this research area; cloud computing is a new computing model provides shared resources and data based on service delivery model, where everything from infrastructures, platforms, and software are given to the user like a set of services. The users of cloud platforms deal with these services to satisfy their requests, however these requests become more complex, and they need more than one service to accomplish one request; the process of gathering a set of services to satisfy a user request is called the service composition. In addition to the fact that one request needs a set of services to be executed, and die to the quick development of cloud technology, there are many of similar services which offer the same functionality, for each service in this set, which make the composition process needs a mechanism to choose between these infinity choices to give the user an optimal satisfaction. In this paper, We propose a framework for service composition in the multi cloud environment where we compose these services based on two factors: the first is a set of QoS (quality of service) criteria for each service, and the second is the number of cloud bases involved in the composition process, to build a composed service that satisfy the user request.

TMP: Tele-Medicine Protocol for Slotted 802.15.4 With Duty-Cycle Optimization in Wireless Body Area Sensor Networks

Recently, adaptive duty cycle-based medium access control (MAC) protocols based on the IEEE 802.15.4 standard have been proposed to address the quality of service (QoS) requirements, including a specified and combined set of time bounded data transmission services, data rate, reliability, and energy consumption for wireless body area sensor networks. However, the current protocols suffer limitations in the context of providing a combined set of QoS. Moreover, for efficient energy consumption, these protocols adjust duty cycle values on estimations, such as active periods, buffer occupancy, and collision rates. These estimations consume resources in terms of delay and energy, which makes them hardly acceptable for medical applications. In this context, we propose a tele-medicine protocol (TMP) under the IEEE 802.15.4 slotted CSMA/CA with beacon enabled mode on the basis of a novel idea which combines two optimizations methods, i.e., MAC layer parameter tuning optimization and duty cycle optimization. The TMP duty cycle is adjusted by three factors: offered network traffic load, delay-reliability factor, and superframe duration. The proposed protocol provides the required set of QoS (delay, reliability, and efficient energy consumption) at the same time for patient monitoring applications. Performance of the proposed protocol is computed on the basis of average end-to-end delay, reliability, packet delivery ratio, collision rate, and energy consumption by varying the number of nodes and offered network traffic. The TMP performed well in terms of delay, reliability, energy consumption, and collision rate in comparison of the other existing protocols under the constraints of patient monitoring applications.

GT-QoSec: A Game-Theoretic Joint Optimization of QoS and Security for Differentiated Services in Next Generation Heterogeneous Networks

Recently, numerous real-time, data-rich, and differentiated applications and services have appeared in the next-generation Heterogeneous Networks. As a result, the number of potentially “untrusted” connections to the mobile operator’s core network is expected to dramatically increase. Therefore, the operators must provide adequate security, without significantly affecting the quality of service (QoS). Hence, joint consideration of QoS and security is a critical research issue. However, due to their difficult-to-model conflicting objectives, existing research works have often dealt with them separately. In this paper, we address this problem, formally formulate it, and envision GT-QoSec, a game-theoretic joint optimization of QoS and security. Using GT-QoSec, the mobile user equipment (UE) and their servicing base stations (eNBs) play games with each other. Thus, the UE obtains a balanced set of QoS and security levels while the eNBs maximize their bandwidth utilization. Extensive analysis and simulation results are presented to evaluate the performance of GT-QoSec in contrast with several conventional methods.

Joint Transceiver Design for Iterative FDE

This paper considers the wideband single-carrier transmission through frequency-selective wireless channels with a quality-of-service (QoS) requirement. Power allocation at the transmitter and iterative frequency domain equalization (FDE) at the receiver are jointly designed to achieve the target QoS with minimum transmission power consumption. The joint transceiver design is based on a novel evolutional analysis framework for iterative FDE without the aid of decoding during the iterative processing. It introduces a set of QoS and quality-of-convergence (QoC) constraints, which recast the design problem into a convex and structured joint QoS/QoC constrained power allocation problem. Thanks to the underlying sparsity in the mathematical structure of the problem, a specific interior-point method (IPM) solver is developed, which significantly reduces the computational complexity, as compared to the generic solvers. A duality gap analysis is then applied to show that the performance gap between the numerical algorithms and the theoretical optimum can be controlled by adjusting its key parameters, which, in turn, determines the convergence behavior of the iterative solver. Extensive simulation results are given to support the mathematical development of the problem.

Infrastructure for component-based DDS application development

Enterprise distributed real-time and embedded (DRE) systems are increasingly being developed with the use of component-based software techniques. Unfortunately, commonly used component middleware platforms provide limited support for event-based publish/subscribe (pub/sub) mechanisms that meet both quality-of-service (QoS) and configurability requirements of DRE systems. On the other hand, although pub/sub technologies, such as OMG Data Distribution Service (DDS), support a wide range of QoS settings, the level of abstraction they provide make it hard to configure them due to the significant source-level configuration that must be hard-coded at compile time or tailored at run-time using proprietary, ad hoc configuration logic. Moreover, developers of applications using native pub/sub technologies must write large amounts of boilerplate "glue" code to support run-time configuration of QoS properties, which is tedious and error-prone. This paper describes a novel, generative approach that combines the strengths of QoS-enabled pub/sub middleware with component-based middleware technologies. In particular, this paper describes the design and implementation of DDS4CIAO which addresses a number of inherent and accidental complexities in the DDS4CCM standard. DDS4CIAO simplifies the development, deployment, and configuration of component-based DRE systems that leverage DDS's powerful QoS capabilities by provisioning DDS QoS policy settings and simplifying the development of DDS applications.

Service-level agreement-based QoS analysis for web services discovery and composition

Quality-of-Service (QoS) in Web services considers a service's non- functional characteristics during service specification, discovery, and composition. In order to encourage the development of QoS-aware Web services, we first develop a QoS-aware model, which contains a common set of QoS attributes including response time, throughput, reliability, availability and price etc. Then, based on the attributes, two alternative service selection methods, namely absolute and relative matchmaking, are presented. Finally, according to the formal semantics of different workflow patterns, we utilize the aggregative effects of QoS attributes to help service consumers perform QoS-aware service composition.

Efficient QoS Provisioning for Adaptive Multimedia in Mobile Communication Networks by Reinforcement Learning

The scarcity and large fluctuations of link bandwidth in wireless networks have motivated the development of adaptive multimedia services in mobile communication networks, where it is possible to increase or decrease the bandwidth of individual ongoing flows. This paper studies the issues of quality of service (QoS) provisioning in such systems. In particular, call admission control and bandwidth adaptation are formulated as a constrained Markov decision problem. The rapid growth in the number of states and the difficulty in estimating state transition probabilities in practical systems make it very difficult to employ classical methods to find the optimal policy. We present a novel approach that uses a form of discounted reward reinforcement learning known as Q-learning to solve QoS provisioning for wireless adaptive multimedia. Q-learning does not require the explicit state transition model to solve the Markov decision problem; therefore more general and realistic assumptions can be applied to the underlying system model for this approach than in previous schemes. Moreover, the proposed scheme can efficiently handle the large state space and action set of the wireless adaptive multimedia QoS provisioning problem. Handoff dropping probability and average allocated bandwidth are considered as QoS constraints in our model and can be guaranteed simultaneously. Simulation results demonstrate the effectiveness of the proposed scheme in adaptive multimedia mobile communication networks.