Quality Of Service For End Users Research Articles

Efficient Slice Creation in Network Slicing using K-Prototype Clustering and Context-Aware Slice Selection for Service Provisioning

The advent of 5G technology has ushered in a new era of communication where the customization of network services is crucial to meet diverse user demands. Network slicing has emerged as a pivotal technology to achieve this customization. In this research, we present an innovative approach to optimize network slicing in 5G by employing K-Prototype Clustering for slice creation and Context-Aware Slice Selection for efficient resource allocation. In slice creation, we delve into the innovative application of the K-Prototype clustering algorithm. Recognizing that 5G networks encompass numerical and categorical attributes, the K-Prototype algorithm enables the creation of network slices that cater to diverse service requirements. By harnessing this clustering technique, our proposed method optimizes the creation of network slices, resulting in improved resource utilization and reduced network congestion. Furthermore, we introduce the concept of Context-Aware Slice Selection, which considers the dynamic and evolving nature of network demands. Context-awareness ensures that network slices are selected based on real-time contextual information, enabling a more adaptive and responsive network. This approach leads to the efficient allocation of resources and a higher quality of service for end-users. To evaluate the performance of our proposed methodology, we employ key performance metrics, including slice selection accuracy, slice selection delay, and radio link failure. Through comprehensive testing and analysis, our research demonstrates that our approach consistently outperforms existing methods in terms of these metrics.

Integration of cloud, fog, and edge technologies for the optimization of high-load systems

The study is dedicated to analyzing methods and tools for optimizing the performance of high-load systems using cloud, fog, and edge technologies. The focus is on understanding the concept of high-load systems, identifying the main reasons for increased load on such systems, and studying the dependency of the load on the system's scalability, number of users, and volume of processed data. The introduction of these technologies implies the creation of a multi-level topological structure that facilitates the efficient operation of distributed corporate systems and computing networks. Modern approaches to load management are considered, the main factors affecting performance are investigated, and an optimization model is proposed that ensures a high level of system efficiency and resilience to peak loads while ensuring continuity and quality of service for end-users. The methodology is based on a comprehensive approach, including the analysis of existing problems and the proposal of innovative solutions for optimization, the application of architectural solutions based on IoT, cloud, fog, and edge computing to improve performance and reduce delays in high-load systems. The scientific novelty of this work lies in the development of a unique multi-level topological structure capable of integrating cloud, fog, and edge computing to optimize high-load systems. This structure allows for improved performance, reduced delays, and effective system scaling while addressing the challenges of managing large data volumes and servicing multiple requests simultaneously. The conclusions of the study highlight the significant potential of IoT technology in improving production processes, demonstrating how the integration of modern technological solutions can contribute to increased productivity, product quality, and risk management.

Deep Reinforcement Learning for Computation Offloading and Resource Allocation in Unmanned-Aerial-Vehicle Assisted Edge Computing.

Computation offloading technology extends cloud computing to the edge of the access network close to users, bringing many benefits to terminal devices with limited battery and computational resources. Nevertheless, the existing computation offloading approaches are challenging to apply to specific scenarios, such as the dense distribution of end-users and the sparse distribution of network infrastructure. The technological revolution in the unmanned aerial vehicle (UAV) and chip industry has granted UAVs more computing resources and promoted the emergence of UAV-assisted mobile edge computing (MEC) technology, which could be applied to those scenarios. However, in the MEC system with multiple users and multiple servers, making reasonable offloading decisions and allocating system resources is still a severe challenge. This paper studies the offloading decision and resource allocation problem in the UAV-assisted MEC environment with multiple users and servers. To ensure the quality of service for end-users, we set the weighted total cost of delay, energy consumption, and the size of discarded tasks as our optimization objective. We further formulate the joint optimization problem as a Markov decision process and apply the soft actor–critic (SAC) deep reinforcement learning algorithm to optimize the offloading policy. Numerical simulation results show that the offloading policy optimized by our proposed SAC-based dynamic computing offloading (SACDCO) algorithm effectively reduces the delay, energy consumption, and size of discarded tasks for the UAV-assisted MEC system. Compared with the fixed local-UAV scheme in the specific simulation setting, our proposed approach reduces system delay and energy consumption by approximately 50% and 200%, respectively.

Design Guidelines for Blockchain-Assisted 5G-UAV Networks

Fifth generation (5G) wireless networks are designed to meet various end-user quality of service (QoS) requirements through high data rates (typically of gigabits per second) and low latencies. Coupled with fog and mobile edge computing, 5G can achieve high data rates, enabling complex autonomous smart city services such as the large deployment of self-driving vehicles and large-scale artificial-intelligence-enabled industrial manufacturing. However, to meet the exponentially growing number of connected IoT devices and irregular data and service requests in both low- and high-density locations, the process of enacting traditional cells supported through fixed and costly base stations requires rethought to enable on-demand mobile access points in the form of unmanned aerial vehicles (UAV) for diversified smart city scenarios. This article envisions a 5G network environment that is supported by blockchain-enabled UAVs to meet dynamic user demands with network access supply. The solution enables decentralized service delivery (drones as a service) and routing to and from end users in a reliable and secure manner. Both public and private blockchains are deployed within the UAVs, supported by fog and cloud computing devices and data centers to provide a wide range of complex authenticated service and data availability. Particular attention is paid to comparing data delivery success rates and message exchange in the proposed solution against traditional UAV-supported cellular networks. Challenges and future research are also discussed with highlights on emerging technologies such as federated learning.

Integrated resource management pipeline for dynamic resource-effective cloud data center

Cloud computing is a popular emerging computing technology that has revolutionized information technology through flexible provisioning of computing resources. Therefore, efforts to develop an effective resource management approach have found that implementing efficient resource sharing among multiple customers that considers power saving, service-level agreements, and network traffic simultaneously is difficult. This paper proposes a practical integrated pipeline that can use various algorithms. The performance of each algorithm is evaluated independently to obtain the combination of algorithms that guarantees a resource-effective cloud data center framework. This integrated resource management pipeline approach would optimize performance based on several key performance indicators, such as power saving, network traffic, and service-level agreements, for either the whole system or the end-user. The performance of the proposed resource management framework was evaluated using a real testbed. The results demonstrated that the proactive double exponential smoothing algorithm prevents unnecessary migrations, the MMTMC2 VM selection algorithm improved the quality of service for end-users and reduced overall energy consumption and network traffic, and the medium-fit placement algorithm provided load balancing between active servers and decreased service level agreement violations. The performance comparison illustrated that the combination of these algorithms was considered to be the best solution toward a dynamic resource-effective cloud data center. Our results showed that energy consumption and the total number of migrations decreased by 16.64% and 49.44%, respectively.

RTSM: Response time optimisation during switch migration in software‐defined wide area network

The distributed control plane is the alternate solution to reliability and scalability like potential issues in the software-defined wide area network (SDWAN), but the static mapping between controller and switches might cause an uneven load distribution among controllers. Migration of switches from one controller to another under-loaded controller is a solution to handle the peak traffic. However, the switch migration process is a complex process that may affect the end-user quality of service (QoS), as a result, the response time of the control messages are also affected. To address this issue, the authors present RTSM, a novel strategy, that optimises the response time of the control messages during switch migration. Further, an SDWAN architecture was proposed, in which the introduction of the layer-2 controller reduces the dependency on WAN communication of the forwarding devices. The Karush–Kuhn–Tucker conditions have been applied for target controller selection, which ensures improved response time. A switch selection method was also introduced, which minimally affects the end-user QoS during device migration in SDWAN scenario. To evaluate the performance of the RTSM, Mininet simulator with the Floodlight controller has been used, and the result shows that the proposed algorithm outperforms the other existing works during load balancing.

Energy-Efficient and Delay-Guaranteed Workload Allocation in IoT-Edge-Cloud Computing Systems

Edge computing has recently emerged as an extension to cloud computing for quality of service (QoS) provisioning particularly delay guarantee for delay-sensitive applications. By offloading the computationally intensive workloads to edge servers, the quality of computation experience, e.g., network transmission delay and transmission energy consumption, could be improved greatly. However, the computation resource of an edge server is so scarce that it cannot respond quickly to the bursting computation requirements. Accordingly, queuing delay is un-negligible in a computationally intensive environment, e.g., a computing environment consists of the Internet of Things (IoT) applications. In addition, the computation energy consumption in edge servers may be higher than that in clouds when the workload is heavy. To provide QoS for end users while achieving green computing for computing systems, the cooperation between edge servers and the cloud is significantly important. In this paper, the energy-efficient and delay-guaranteed workload allocation problem in an IoT-edge-cloud computing system are investigated. We formulate a delay-based workload allocation problem which suggests the optimal workload allocations among local edge server, neighbor edge servers, and cloud toward the minimal energy consumption as well as the delay guarantee. The problem is then tackled using a delay-base workload allocation (DBWA) algorithm based on Lyapunov drift-plus-penalty theory. The theoretical analysis and simulation results have been conducted to demonstrate the efficiency of the proposal for energy efficiency and delay guarantee in an IoT-edge-cloud system.

QoS, Energy and Cost Efficient Resource Allocation for Cloud-Based Interactive TV Applications

Internet-based social and interactive video applications have become major constituents of the envisaged applications for next-generation multimedia networks. However, inherently dynamic network conditions, together with varying user expectations, pose many challenges for resource allocation mechanisms for such applications. Yet, in addition to addressing these challenges, service providers must also consider how to mitigate their operational costs (e.g., energy costs, equipment costs) while satisfying the end-user quality of service (QoS) expectations. This paper proposes a heuristic solution to the problem, where the energy incurred by the applications, and the monetary costs associated with the service infrastructure, are minimized while simultaneously maximizing the average end-user QoS. We evaluate the performance of the proposed solution in terms of serving probability, i.e., the likelihood of being able to allocate resources to groups of users, the computation time of the resource allocation process, and the adaptability and sensitivity to dynamic network conditions. The proposed method demonstrates improvements in serving probability of up to 27%, in comparison with greedy resource allocation schemes, and a several-orders-of-magnitude reduction in computation time, compared to the linear programming approach, which significantly reduces the service-interrupted user percentage when operating under variable network conditions.

Mechanism and architecture for the migration of service implementation during traffic peaks

Service-Oriented Architecture has been widely applied in enterprise computing systems for software-enabled services. However, cost efficiency and scalability requirements have moved the execution environment towards the cloud domain. Hybrid approaches have emerged, which utilise both enterprise and cloud domains in order to balance between the cost of service execution and the provided Quality of Service (QoS) for end users. This paper presents a migration, monitoring and load-balancing mechanism and architecture for scaling services between the enterprise and cloud domains during traffic peaks. The argued benefit of the proposal is the automation of the service-migration process and improvement of the QoS. A prototype system is presented as a proof of the conceptual architecture. The performance results in a hybrid cloud environment indicate that service implementation can be migrated and load can be balanced within 200 ms. Furthermore, the mechanism can improve the QoS for end users during traffic peaks. Our approach differs from existing proposals by focusing on the migration of service implementation, instead of the migration of service as part of a virtual machine.

Optimizing end user QoS in heterogeneous network environments using reputation and prediction

Telecommunication consumers are fueling a demand for mobile devices that are rapidly increasing in their capability to provide a wider range of services. These services in turn are consuming more bandwidth and require richer quality of service (QoS) in order to ensure a good end user experience when performing activities such as streaming video content or facilitating voice over IP. As a result, network providers are expanding and improving their coverage area while technology to establish Wi-Fi hotspots is becoming more accessible to every day users. This combination of increase in demand and accessibility, coupled with users' ever-increasing expectations for high quality service presents a growing need to seamlessly optimize the use of the overlaid heterogeneous networks in urban areas to maximize the end user experience via the use of a vertical handover mechanism (VHO). Grey systems theory has been used in a wide range of systems including economic, financial, transportation, and military to accurately forecast time series based on limited information. In this paper, we build on a novel reputation-based VHO decision rating system by proposing the use of the grey model first-order one variable, GM(1,1), in the handover decision making progress. The low complexity of the GM(1,1) model allows for a quick and efficient prediction of the future reputation score for a given network, providing deeper insight into the current state of the target network. Furthermore, simulations show that the proposed model, in comparison with the original reputation model, improves the decision capability of a mobile node and helps balance the load across the heterogeneous networks employing its strategy.

Inter-carrier SLA negotiation using Q-learning

Inter-domain high performance services (e.g. telepresence) are not sustainable over the current Internet architecture. The Quality of Service (QoS) guarantees they demand require to settle on end-to-end Service Level Agreements (SLAs) among providers (aka. carriers) and across different networks. This process is critical since it must provide the most benefits while dealing with heterogeneous operators' business interests and confidentiality constraints. In this paper, we propose, in the frame of a cooperative organizational model called federation, a composition technique for inter-carrier SLAs that respects end-user's QoS requirements while maximizing network operators' long-term benefits. We formulate the dynamic optimization problem as a Markov Decision process (MDP). This latter allows to provide an iterative near-optimal solution through reinforcement learning (more precisely, Q-learning). The SLA composition is thus performed taking into account customers and network providers' utilities. We also propose a version including several negotiation rounds and observe how it affects the results.

Per-flow optimal service selection for Web services based processes

With the development of the Service-Oriented Computing (SOC) paradigm, flexible business processes can be defined from independently developed services. Multiple services corresponding to the same functionality but characterized by different Quality of Service (QoS) attributes can be offered by different service providers and the best set of Web services can be selected at run-time in order to maximize the QoS for end users. In the literature many approaches have been proposed for the optimal service selection which is usually performed on a per-request basis, i.e., considering a single process invocation. In this paper we propose a broker-based framework which solves the optimal service selection on a per-flow basis. Multiple applications, defined as different BPEL processes are considered at the same time and multiple requests to the same process are optimized concurrently. Service selection is formulated as a constrained non-linear multi-criteria optimization problem and an heuristic algorithm is proposed to determine a scalable and efficient solution. A comparison with top performing state-of-the-art approaches for a number of different scenarios of interest is also provided. Results show that the overall optimization time reduction which can be achieved by our solution is proportional to the system incoming workload. Order of magnitudes optimization time improvements can be obtained if compared to alternative methods. Moreover, our solution is robust to workload prediction uncertainty.

End-to-End Quality Management Method for VoIP Speech Using RTCP XR

Quality of service (QoS) control is one of the key technologies for the next-generation network (NGN). In the conventional method, the bearer quality on the carrier network is managed, but the end-to-end QoS for end users needs to be guaranteed. The quality of a public switched telephone network (PSTN) is guaranteed, and the quality of the telephone terminal is also stable. Therefore, end-to-end quality management of PSTN services has been achieved. However, the quality of neither IP networks nor VoIP services is guaranteed in general. In addition, there are numerous VoIP terminals and the differences in their implementations strongly affect the speech quality experienced by end users. Thus, quality management technologies need to be embedded in the VoIP terminal to achieve the equivalent of end-to-end QoS management for the PSTN. These technologies are recommended in IETF RFC3611 and ITU-T recommendation P.564 as the framework for end-to-end quality management, but their usage is not shown. Therefore, we propose an end-to-end quality management method for VoIP speech using RTCP XR. We realize an end-to-end QoS monitoring method between the customer and the operator of the carrier network. We define the parameters that should be implemented in RTCP XR packets to estimate the quality of VoIP services based on experimental results.

A QoS model for collaboration through Distributed Virtual Environments

Today’s virtual environments are expected to be distributed to allow collaboration for common purposes. However, they must ensure a high level of Quality of Service (QoS) to the user, especially in an open context with unknown, a priori, man-in-the-loop event occurrence. This paper presents our Distributed Virtual Environment Collaboration Model (DVECOM) and its implementation which aims to provide an end-user QoS support for distributed virtual reality applications. This model aims to reconcile openness and real-time requirements for collaborative relationships using virtual environments. Real-time requirements ensure the logical synchronization between the displayed scenes of the same virtual world on a set of distributed machines. The DVECOM model is based partially upon COREMO concepts and further work done in the context of the Amusement European Esprit project.DVECOM integrates QoS provision and management. The major idea is to guarantee the consistency of scene rendering and the synchronization of the display from the user point of view. The other idea is to auto-adapt rendering in accordance with the retained strategy, ensuring best effort and least suffering virtual world rendering. Representation degradation is driven by the users’ choices and is assisted by the system. The receiver-side protocol is based upon end-user preferences, physical level capability information, as well as pertinence of notification to each client (contextual end-user information). The model should be able to guarantee logical consistency and synchronization of virtual world distributed displays, with the least rendering degradation possible. When available, such guarantees would make it possible to use DVE out of a closed, oversized, very restricted context for industrial collaborative applications with an expected QoS.

Source models of network game traffic

We study the traffic generated by sessions of a popular multi-player network game. Our analysis indicates that empirical game traffic can be characterized well by certain analytical models. While clients and servers as well as hosts with different configurations, produce different models, all models from the game are well modeled by the same families of distributions. We find that some data sets are best modeled with split distributions; that is, one portion of the data is well modeled with one particular distribution, and the rest of the data with another. We describe how our models can be simulated and discuss how host processing speed influences packet interarrival processes, which in turn influence playability. The latter is a clear indication that end user quality of service is more than just a network issue—host characteristics must be considered as well. These are empirical results that have been rarely incorporated into theoretical analyses of network traffic. As Internet gaming becomes more popular, we expect that our models will be useful for testing hardware and protocols that support gaming.