Service QoS Research Articles

Proposed Fuzzy Real-Time HaPticS Protocol Carrying Haptic Data and Multisensory Streams

Sensory and haptic data transfers to critical real-time applications over the Internet require better than best effort transport, strict timely and reliable ordered deliveries. Multi-sensory applications usually include video and audio streams with real-time control and sensory data, which aggravate and compress within real-time flows. Such real-time are vulnerable to synchronization to synchronization problems, if combined with poor Internet links. Apart from the use of differentiated QoS and MPLS services, several haptic transport protocols have been proposed to confront such issues, focusing on minimizing flows rate disruption while maintaining a steady transmission rate at the sender. Nevertheless, these protocols fail to cope with network variations and queuing delays posed by the Internet routers.
 This paper proposes a new haptic protocol that tries to alleviate such inadequacies using three different metrics: mean frame delay, jitter and frame loss calculated at the receiver end and propagated to the sender. In order to dynamically adjust flow rate in a fuzzy controlled manners, the proposed protocol includes a fuzzy controller to its protocol structure. The proposed FRTPS protocol (Fuzzy Real-Time haPticS protocol), utilizes crisp inputs into a fuzzification process followed by fuzzy control rules in order to calculate a crisp level output service class, denoted as Service Rate Level (SRL). The experimental results of FRTPS over RTP show that FRTPS outperforms RTP in cases of congestion incidents, out of order deliveries and goodput.

QoS Guaranteed Edge Cloud Resource Provisioning for Vehicle Fleets

Nowadays vehicle fleets are launched to perform business or scientific tasks, with new features supported by the emerging multi-access edge computing (MEC) platform. In the presence of high vehicle mobility, however, it is challenging to precisely provision resources among distributed edge clouds so that i) the QoS of vehicular service is guaranteed and meanwhile ii) the provisioning cost is minimized. We systematically investigate the QoS guaranteed optimal resource provisioning problem for the connected vehicle fleet in the MEC environment. Based on stochastic traffic analysis, we propose an optimization framework to minimize the cost of resource provisioning, while the service blocking probability is guaranteed to be smaller than a predefined threshold. We then present a lightweight two-phase algorithm based on bracketing and binary searching to solve the problem efficiently. To evaluate our method, we use two large real-world datasets collected by an online taxi service platform and validate the QoS with our resource provisioning strategy. The results demonstrate that our method can save the total provision cost up to 40%, compared with the naive resource provisioning strategy, and meanwhile can provide reliable QoS guarantee, compared with the mobility estimation-based approach.

Data Traffic Based on Slicing in a 5G Smart Uplink System

The paper considers the data traffic based on slicing in a 5g mobile network uplink system. Slicing is a promising technology for the fifth generation of networks that provides optimal quality of QOS services for each specific user or group of users. Data traffic that is processed by cellular networks increases every year. Therefore, we should consider all set of traffic from VoIP to M2M devices. For example, smart devices in the healthcare system transmit big data that is sensitive to latency, but also a video stream that requires minimal latency in certain cases. The paper focuses on the successful processing of traffic through a relay node, donor microstates, and a base station. All traffic is divided into three levels of QoS segmentation: sensitive, less sensitive, and low-sensitivity, using the AnyLogic simulation program. For fifth-generation 5G networks, achieving minimum latency and maximum data transfer speed within QoS is an important implementation condition. Therefore, in this paper, using simulation modeling, the main and possible results of each segment in the new generation of mobile networks are obtained. The use of a relay node in conjunction with micro-stations can ensure optimal station load and successful data processing. Also, the solutions outlined in this paper will allow you to identify a number of areas for future research to assess possible ways to design new mobile networks, or improve existing ones.

Study of IPv6 Protocol in the Data Model of the Smart Grid Distribution Domain

Introduction: This article is the product of the research “Study of the IPv6 Protocol in the data model of the Smart Grid distribution domain” developed at the Universidad del Valle and carried out during 2019.
 Problem: There is an immediate need to establish standards and protocols for the Smart Grid for both the electrical components and the component technologies of information and communication.
 Objective: The objective of the research is to characterize the use of IPv6 in the context of the communications domain distribution of the Smart Grid.
 Methodology: The work defines a virtualization environment in which the performance of IPv6 in the domain distribution of the Smart Grid will be evaluated; this evaluation includes measurement and analysis of delays as well as traffic volumes, bandwidth, cyber-security conditions, and time allocation of network addresses.
 Results: The IPv6 protocol is considered as a viable alternative in the Smart Grid communication model in order to comply with the communication requirements.
 Conclusion: The implementation of Quality of Service QoS in IPv6 defined in RFC2474 is essential in the Smart Grid communication network in order to meet the communication requirements of the defined applications.
 Originality: There is great expectation that networks based on the Internet Protocol will serve as a key element for communications within the Smart Grid.
 Limitations: The wide scope and dimensions involving Smart Grids, it is almost impossible to implement the communication network of a Smart Grid completely in a single simulation tool or emulation.

Novel Algorithm for Service Composition and QOS Enhancements in Composite Computing Services

The swift progress in the computing set up by providing services has caused the sudden increase in the amount of the services that are operationally similar. The dissimilarity in such services are observed based on the characteristics standards of their service quality. Consequently a thought-provoking issue in the perspective of the service-configuration is based on the choosing the accurate client services according to the user demands for which the acquired compound services guarantees the non-computational demands on the ground of comprehensive service quality constraints. The procedure carried out in the paper presents the novel algorithm for the service composition/configuration for enhancing the quality of service of the compound /composite computing services. The algorithm focusses in improve the service configuration feasibility and optimality and minimize the configuration time. The algorithm is totally based on the limits in the configuration strategies that relies on the Pareto-Dominance and the quality of service decomposition (PD-QOSD). The quantity of the service arrangements is expected to increase according to the service count and quality of service characteristics in case of PD and whereas the QOSD takes care of the feasibility of the arrangements during the high restrictions imposed by users on the constraints of the comprehensive quality of service. The validation outcomes prove that the novel algorithm developed function efficiently on the basis of the time required for composition, the optimality, as well as the search space.

Routing optimization for cloud services in SDN-based Internet of Things with TCAM capacity constraint

Distributed in-network cloud architecture is a promising solution to efficiently host next generation internet-of-things (IoT) services. With the rapid increase of IoT devices and applications, the backhaul or backbone networks, which transmit IoT traffic to various in-network clouds, will experience a predicted explosion in the volume of carried traffic. To guarantee the QoS of IoT cloud services and improve the network performance, it is crucial for network operator to implement efficient routing optimization strategies for IoT traffic. As a promising networking paradigm, software-defined networking (SDN) has flexible and programmable control capability for fine-grained flows. The emergence of SDN paves a way for implementing high-performance routing optimization in networks. In SDN networks, the routing strategies are realized through flow rules, which are usually stored in TCAM with very limited capacity. However, the number of IoT flows are enormous. Thus, in this paper, we address the routing optimization problem in SDN-based IoT with TCAM capacity constraint. We first formulate the problem as a mixed integer linear programming problem and prove the problem is NP-hard. Then to solve the problem efficiently, we propose several approximate algorithms, which solve the problem in two stages. In the first stage, the algorithms calculate the routing strategies for flows without considering the TCAM capacity constraint. To meet the TCAM capacity constraint, the algorithms using different strategies to adjust the paths of some flows in the second stage. Extensive simulations are conducted on both real ISP and synthetic topologies to evaluate the performance of the algorithms. The simulation results verify that the algorithms can achieve promising load balancing performance in SDN-based IoT, where the capacity of TCAM in SDN switches is very limited.

Dynamic Scheduling Algorithm for Delay-Sensitive Vehicular Safety Applications in Cellular Network

The vehicular safety applications disseminate the burst messages during an emergency scenario, but effort to reduce delay of communication are hampered by wireless access technology. As conventional VANET (Vehicular ad-hoc network) connected intermittently, the LTE-based framework has been established for the vehicular communication environment. However, resource allocation which affected by many factors, such as power, PRB (physical resource block), channel quality, are challenging to guarantee the safety services QoS in LTE downlink for OFDM. In order to solve the problem of safety message dissemination in LTE vehicular network, we proposed a delay-aware control policy by leveraging cross-layer approach to maximize the system throughput. First, we model the resource allocation problem using the queuing theory based on the MISO. Second, the method casts the problem of throughput and latency for dynamic communication system into a stochastic network optimization problem, and then makes tradeoffs between them by Lyapunov optimization technique. Finally, we use the improved the branch and bound algorithm to search optimal solution in system capacity region for these decomposed subproblems. The simulation results show that our algorithm can guarantee the delay while maximum system throughput

Cloud Qos Ranking Prediction using Tanimoto Coefficient Similarity Based Deep Learning Method

Cloud computing is a service which provides virtualized resources conforming to the end-user needs. Infrastructure, platform and software included in it. For the last two decades, it has achieved very gigantic growth. Currently, there are several cloud service providers in the market. The primary aim of this research is to minimize cloud service violation. It helps the service providers in exempting the penalty enhancing their reliability. So, cloud service QOS prediction is a research problem that must be solved. It is a very necessary thing for cloud service providers and cloud users. We have discussed several QoS prediction related to researches in the literature survey. But none of them has given a satisfactory QoS prediction. In this paper, we proposed a Tanimoto Coefficient Similarity-Based Deep Learning Method for QoS ranking prediction. The analysis helps service providers choose a suitable prediction method with optimal control parameters so that they can obtain accurate prediction results and avoid violation penalties. In comparison with the prior method in practice, the proposed method is more significant in terms of prediction accuracy, prediction time and error rate.

Decentralized learning for self-adaptive QoS-aware service assembly

The highly dynamic nature of future computing systems, where applications dynamically emerge as opportunistic aggregation of autonomous and independent resources available at any given time, requires a radical shift in the adopted computing paradigms. Indeed, they should fully reflect the decentralized perspective of the execution environment and consider QoS, scalability and resilience as key objectives. In this context, the everything-as-a-service (XaaS) paradigm, which envisions the creation of new services as an assembly of independent services available within the environment, can greatly help in tackling the challenges of developing future applications. However, in order to be effective, XaaS paradigm requires self-adaptive service assembly solutions able to cope with the unpredictable variability and scalability of the execution environment, the lack of global knowledge, and the QoS requirements of services to be built. We contribute in this direction by designing a fully decentralized and collective self-adaptive service assembly framework whose main features are: (i) self-assembly, i.e., the ability to operate autonomously, (ii) online-learning, i.e., the ability to dynamically learn from experience, (iii) QoS-awareness, i.e., the inclusion of QoS requirements as driving forces for self-assembly, (iv) scalability, i.e., the ability to cope with a large number of services, and (v) resilience, i.e., the ability to maintain the persistence of service delivery when facing unexpected changes (e.g., in the number and/or QoS of services). Simulation experiments show that our solution makes the system able to quickly converge to viable assemblies that improve and maintain over time the social welfare of the system, despite the local perspective of each participating service.

An Efficient Approach for Privacy-Preserving of the Client’s Location and Query in M-Business Supplying LBS Services

Privacy-preserving in mobile business supplying location-based services (LBS) has the potential to become a primary concern for clients and service providers. In m-business providing LBS services, a client sends its exact locations to service providers. This data may involve sensitive and private personal information. Therefore, the misuse of location information by service providers creates privacy issues for clients. Moreover, the query must not be linked to the mobile client, even if the location information is exposed willingly by her/him to obtain specific services. Thus, there are location cloaking algorithms that allow the protection of the location privacy of mobile users. Hence, many temporal and spatial approaches to cloaking a specific user’s location have been proposed. Different from the existing methods, the current works define location and query privacy separately. Therefore, in this paper, we investigate the issues related to the mobile client privacy. Mainly, we aim to preserve the client location privacy as well as the continuous queries privacy, where mobile clients continuously emit different queries during their travels. It’s on this premise that we propose a new clique-based cloaking algorithm named Mobile Clique Cloak (MCC) to preserve the mobile client’s privacy in the M-business providing LBS services. Also, to build the cloaking region in our approach, we take into account the similarity of client velocity and direction to obtain a right balance between quality of service QoS and privacy. Furthermore, we generate different realistic dummies instead of dropping the query; thus, all queries will be processed even in the case of k−1 other mobile clients’ queries cannot be found. Moreover, our work deals with a series of attacks in the same cloaking process (location attack, tracking attack, query sampling attacks and homogenous attack). We evaluate our approach from three aspects: privacy guaranty, quality of service and performance. Experimental evaluation of our algorithms on a real world map shows that our approach ensures total privacy for clients and protects the privacy of clients during the entire query period whiles allowing clients’ choice of privacy requirements. Besides, we compare our algorithm with existing privacy protection algorithms such as V-DCA, D-TC and GCA. According to the evaluation results and a comparison of the algorithms, our algorithm MCC can make a good balance between quality of service, performance and privacy.

Assessment of TCP Multi-Pathization Method on IoT Devices by SDN based on WebQoE

This paper assesses QoE for a Web service (WebQoE) for the TCP multi-pathization method by SDN by experiments with subjects. The multi-pathization method is one of the QoS controls for IoT networks; it can multi-pathize a TCP connection without any changes of terminals or protocols. The previous work of the authors has evaluated QoS of Web services but has not assessed QoE. This paper assesses QoE of an online map service for the multi-pathization method by experiments with subjects. In the experiment, the authors treat various environments which differs in the amount of load traffic. The obtained results show that WebQoE for the multi-pathization method is always higher than that without the method. Consequently, the paper confirms that the multi-pathization is effective from point of users’ view.

Rationing the main parameters of the Quality of Network services

The European Telecommunications Standards Institute (ETSI) recommends that IP-telephony networks be divided into four classes according to the quality of service (quality of network services) QoS, the main indicator of which is packet delay (Y.1541). ITU-T Recommendation G.114 for public switched telephone networks provides delays close to ETSI gradations that correspond to different types of communication. The quality of the service from the user's point of view can be expressed by a set of indicators. These metrics are described in terms that are understandable to both the user and the service, and are independent of the network structure. Quality of service indicators are focused primarily on the effect perceived by the user, must be guaranteed to the user by the service and be objectively measured at the point of access to the service (ITU-T Recommendation I.350). ITU-T Recommendation E.862 provides possible approaches to accounting for the economic losses of the operator (in planning, design, operation and telecommunication network) and the user associated with technical failures. Operators of networks, working in market conditions, are interested in assessing possible losses due to failures and in comparing them with the costs of increasing the reliability of their funds.

Mission QoS and Satellite Service Lifetime Tradeoff in Remote Sensing Satellite Networks

The large-scale charging and discharging processes can accelerate the attenuation of battery components to further decrease the satellite service lifetime (SSL). Consequently, how to effectively manage the energy consumption during mission acquisition and transmission is an urgent problem under the condition of the stochastically fluctuating solar infeed and limited on-board battery storage resource. On the other hand, the QoS requirements of on-board missions should be guaranteed. Therefore, this letter proposes an intelligent energy management strategy by designing an online mission acquisition, processing, and transmission integration scheduling (MAPTIS) scheme, so as to control the risk of battery depletion thereby further guaranteeing SSL while improving mission QoS requirements. Particularly, we formulate the online battery management based MAPTIS problem as a finite-horizon expected total service reward Markov decision process problem. Furthermore, a backward induction battery management (BIBM) algorithm is proposed to achieve the optimal solution. Simulation results show that the BIBM scheme can effectively reduce the dynamic range of energy consumption, while achieving a compromise in the mission QoS.

A modified water cycle evolutionary game theory algorithm to utilize QoS for IoT services in cloud-assisted fog computing environments

The Internet of Things (IoT) is rapidly gaining popularity as a result of the advancements in portable embedded devices and wireless protocols, enabling a new class of services. On the other hand, edge clouds provide IoT services as a new paradigm called fog computing. As the number of available IoT devices increases, more efficient methods are required to select the optimal combination of services out of several existing candidates in edge clouds while composing more complex IoT workflow tasks. So, cloud-assisted fog computing requires a platform for management, composition and provisioning of IoT services for IoT–cloud integration. Resent works have some weaknesses and did not consider some aspects of fog computing such as low latency, low energy and efficient resource allocation. We propose a cloud-based platform for management of IoT service selection and composition in fog computing to enhance QoS parameters such as bandwidth usage, latency and distributed resource utilization. In particular, we propose a multi-objective evolutionary game theory, enhanced by evaporation-based water cycle algorithm (EG-ERWCA) to optimize CPU usage, power consumption and latency of the IoT workflows in cloud-assisted fog computing environments. Many different real IoT workflows are used for evaluation of the proposed method in comparison with the state-of-art algorithms. Simulation results show that the overall quality of service is improved by 2.66 times compared to rival algorithms.

Learning-Based Adaptive Management of QoS and Energy for Mobile Robotic Missions

Mobile robotic systems are normally confronted with the shortage of on-board resources such as computing capabilities and energy, as well as significantly influenced by the dynamics of surrounding environmental conditions. This context requires adaptive decisions at run-time that react to the dynamic and uncertain operational circumstances for guaranteeing the performance requirements while respecting the other constraints. In this paper, we propose a reinforcement learning (RL)-based approach for Quality of Service QoS and energy-aware autonomous robotic mission manager. The mobile robotic mission manager leverages the idea of (RL) by monitoring actively the state of performance and energy consumption of the mission and then selecting the best mapping parameter configuration by evaluating an accumulative reward feedback balancing between QoS and energy. As a case study, we apply this methodology to an autonomous navigation mission. Our simulation results demonstrate the efficiency of the proposed management framework and provide a promising solution for the real mobile robotic systems.

IoT/cloud‐enabled smart services: A review on QoS requirements in fog environment and a proposed approach based on priority classification technique

SummarySmart applications are increasingly emerged in our daily professional and personal lives, ranging from augmented reality and gaming applications to E‐health and telesurgery services. In fact, the growing development of Internet of Things (IoT) and its integration with cloud and fog paradigms have encouraged infrastructures and services providers to perform intelligence in advanced technologies. However, the deployment of IoT/cloud‐enabled smart services still suffers from several quality of services (QoSs) management issues such as latency sensitivity for interactive applications and real‐time big data analytics. In this paper, we propose to present a novel approach for the management of smart services QoS. Accordingly, we introduce an overview of QoS requirements in fog computing environment for several IoT/cloud‐enabled use cases, to identify QoS metrics. The synthesis of the requirements review allows us to present a solution for QoS enhancements on the basis of a priority classification (PC) technique. Simulated with iFogSim, PC shows a significant enhancement of the system performance, notably for latency, energy consumption, and network usage.

Intelligent Network Slicing for V2X Services Toward 5G

Benefiting from the widely deployed LTE infrastructures, 5G wireless networks are becoming a critical enabler for the emerging V2X communications. However, existing LTE networks cannot efficiently support stringent but dynamic requirements of V2X services. One effective solution to overcome this challenge is network slicing, whereby different services could be supported by logically separated networks. To mitigate the increasing complexity of network slicing in 5G, we propose to leverage the recent advancement of Machine Learning (ML) technologies for automated network operation. Specifically, we propose intelligent network slicing architecture for V2X services, where network functions and multi-dimensional network resources are virtualized and assigned to different network slices. In achieving optimized slicing intelligently, several critical techniques, including mobile data collection and the design of an ML algorithm, are discussed to tackle the related challenges. Then, we develop a simulation platform to illustrate the effectiveness of our proposed intelligent network slicing. With the integration of 5G network slicing and ML technologies, the QoS of V2X services is expected to be dramatically enhanced.

Adaptive and Optimized Meta-Heuristic Framework for Establishing QoS and Transaction Properties-based Dynamic Web Service Composition

The dynamic web service composition based on QoS and transactional properties is considered to be the most important challenging research issues. Most of the works explored in the area of dynamic web service composition have mainly focused on some specific QoS parameters, which avoid potential factors from utilization. Further, the transaction properties also play a vital role in handling uncertainty which introduces most vital functionalities into the reactive environment. In this paper, an Adaptive and Optimized Meta-Heuristic Framework (AOMHF) is proposed for establishing dynamic web service composition based on QoS and transaction properties. This proposed AOMHF framework considered the user preferences and unpredictable service QoS for achieving reliable dynamic web service composition. This proposed AOMHF framework is developed as a dual staged web service composition scheme which incorporates the merits of monitoring mechanism which improves the possibility of improving and guaranteeing reliability and availability under reliable web service composition.

QoS Prediction for Web Services Using User Based Classification

With the development of technology, the web services increase dynamically with using of the internet. In order to avoid the time consuming and the expensive selection for selecting the best web service, it is becoming more important to design effective approaches for the selection and recommendation of the internet web services. This paper proposed a new collaborative for web service QoS prediction by using user-based classification technique, specifically the voting method. The experimental result shows the effectiveness and efficient QoS prediction of our approach. For comparison purpose, we employ real-world web service QoS datasets (namely DS-DREAM datasets). In sum, the experimental results compete with the recent related works.

Secure and Proficient Cross Layer (SPCL) QoS Framework for Mobile Ad-hoc

A cross layer QoS framework is a complete system that provides required QoS services to each node present in the network. All components within it cooperate together for providing the required services. In existing QoS frameworks there is no security mechanism provided while Security is a critical aspect for QoS in the MANET environment. Cross layer QoS framework tend to be vulnerable to a number of threats and attacks like, over/under-reporting of available bandwidth, over-reservation, state table starvation, QoS degradation, information disclosure, theft of services timing attack, flooding attack, replay attack, and denial of service (DoS) attack, attacks on information in transit and attacks against routing. So it is necessary when designing protocols for QoS framework, the harmony between security and QoS must be present as one impacts the others. In this work we proposed secure and proficient cross layer (SPCL) QoS frameworks which prevents from various types of threats and attacks. The proposed SPCL QoS framework achieves better performance compared to existing QoS frameworks in metrics of throughput, packet drop ratio, end-to-end delay, and average jitter in both condition when malicious node present in the network and when malicious node not present in the network