Service QoS Research Articles

Lcsa-fed: a low cost semi-asynchronous federated learning based on lag tolerance for services QoS prediction

Lcsa-fed: a low cost semi-asynchronous federated learning based on lag tolerance for services QoS prediction

Enhancing Machine Learning-Based Autoscaling for Cloud Resource Orchestration

Performance and cost-effectiveness are sustained by efficient management of resources in cloud computing. Current autoscaling approaches, when trying to balance between the consumption of resources and QoS requirements, usually fall short and end up being inefficient and leading to service disruptions. The existing literature has primarily focuses on static metrics and/or proactive scaling approaches which do not align with dynamically changing tasks, jobs or service calls. The key concept of our approach is the use of statistical analysis to select the most relevant metrics for the specific application being scaled. We demonstrated that different applications require different metrics to accurately estimate the necessary resources, highlighting that what is critical for an application may not be for the other. The proper metrics selection for control mechanism which regulates the requried recources of application are described in this study. Introduced selection mechanism enables us to improve previously designed autoscaler by allowing them to react more quickly to sudden load changes, use fewer resources, and maintain more stable service QoS due to the more accurate machine learning models. We compared our method with previous approaches through a carefully designed series of experiments, and the results showed that this approach brings significant improvements, such as reducing QoS violations by up to 80% and reducing VM usage by 3% to 50%. Testing and measurements were conducted on the Hungarian Research Network (HUN-REN) Cloud, which supports the operation of over 300 scientific projects.

Evaluation of Handoff Drop Call Rate in GSM Network in Delta State with Dynamic Cut off Channel Allocation Scheme

This study aims at evaluating Handoff drop call rate in GSM Network using dynamic Cut off Channel Allocation Scheme by evaluating different Mobile Network Operators, MNOs, and comparing with Nigerian Communication Commission (NCC)’s Key Performance Indicator (KPI). A hybrid handoff algorithm was developed to reduce drop calls, analyze the performance of the hybridized handoff algorithm; and compare the simulated results of the hybridized algorithm with that of the existing Mobile network operators, MNOs. The current handoff failure and drop call rates of the best mobile communication network in Nigeria was evaluated and was discovered that the existing Hand off failure rate HFR per cell was 5% and the drop call rate DCR was 10%. These were above the NCC maximum target of 2%. These defects account for the unacceptable drop call rates experienced and thus negatively impact on the quality of service QoS. An optimal solution was provided by developing an enhanced and highly sensitive handoff algorithm from two different algorithms - the Entire Channel Allocation, and the Fixed Channel Allocation - while putting the Modified Reserved Channel Scheme with Guard Channel, and the Fuzzy handoff initiation method into consideration. A software known as Netbeans 7.2 was interfaced with Octave 5.1.0 and used to simulate the developed algorithm. The programme trial was made to run repeatedly for 1200s. Our simulation results indicated HFR of 3% as against 5% from that of the existing network, and DCR of 6% as against the 10% from the network. In further comparing the existing DCR with that obtained from our simulation, an average of 40% dropped calls reduction was realized. This implied that the algorithm reduced drop calls thereby enhancing connectivity and mobility management through the minimization of handoff failures.

A QoS and sustainability-driven two-stage service composition method in cloud manufacturing: combining clustering and bi-objective optimization

A QoS and sustainability-driven two-stage service composition method in cloud manufacturing: combining clustering and bi-objective optimization

A Deep-Learning Model for Service QoS Prediction Based on Feature Mapping and Inference

A Deep-Learning Model for Service QoS Prediction Based on Feature Mapping and Inference

HTG: A heterogeneous topology aware model to improve cold start in cloud service QoS prediction

HTG: A heterogeneous topology aware model to improve cold start in cloud service QoS prediction

Location-Aware Deep Interaction Forest for Web Service QoS Prediction

With the rapid development of the web service market, the number of web services shows explosive growth. QoS is an important factor in the recommendation scene; how to accurately recommend a high-quality service for users among the massive number of web services has become a tough problem. Previous methods usually acquired feature interaction information by network structures like DNN to improve the QoS prediction accuracy, but this generates unnecessary computations. Aiming at addressing the above problem, inspired by the multigrained scanning mechanism in a deep forest, we propose a location-aware deep interaction forest approach for web service QoS prediction (LDIF). This approach offers the following innovations: The model fuses the location similarity of users and services as a latent feature representation of them. In addition, we designed a scanning interaction structure (SIS), which obtains multiple local feature combinations from the interaction between user and service features, uses interactive computing to extract feature interaction information, and concatenates the feature interaction information with original features, which aims to enhance the dimension of the features. Equipped with these, we compose a layer-by-layer cascade by using SIS to fuse low- and high-order feature interaction information, and the early-stop mechanism controls the cascade depth to avoid unnecessary computation. The experiments demonstrate that our model outperforms eight other state-of-the-art methods on MAE and RMSE common metrics on real public datasets.

A Novel E2E Path Selection Algorithm for Superior QoS and QoE for 6G Services

A Novel E2E Path Selection Algorithm for Superior QoS and QoE for 6G Services

5G Network Performance Optimization Strategies for Smart Grids

Abstract In this paper, a network slice resource allocation algorithm based on delay sensing is proposed for the problems of service delay and QoS reduction, and resource utilization reduction caused by unreasonable network slice resource allocation. MIMO technology is being studied to establish a communication system that supports multiple inputs and outputs for channels. A resource preallocation method for 5G edge computing is proposed by reasonably allocating resources in the 5G edge computing environment. Then, the performance of the 5G network is optimized, and the optimized network performance is analyzed based on case studies. The results show that the buffer depth of the optimized 5G network is reduced by 1.25, the network delay is reduced by 0.02s, and the network throughput is significantly improved. This study provides the theoretical basis and technical support for the further development of a smart grid in a 5G environment.

Resource Allocation with Graph Neural Networks-Multi Agent Reinforcement Learning for 6G HetNets

The sixth generation (6G) mobile communication system is expected to meet the different service needs of modern communication scenarios. In this paper, the spectrum sharing problem in the 6G HetNets is addressed by combining multi-agent reinforcement learning (MARL) with Graph Neuronal Networks (GNN) for graph-structured problems. A problem is formulated to maximize the sum of the data rate of all the links in the network. In particular, GNN extracts the characteristics of the different types of communication links that coexist in the network taking into account the network topology and the observations of the neighbors. Then, we model the spectrum access of the multiple links as a multi-agent problem and exploit recent progress of multi-agent Reinforcement Learning to develop a distributed spectrum that guarantees good quality of service QoS, with low levels of interference and good data rate. Extensive simulation demonstrates that the proposed method is near-optimal compared with other relevant previous research work.

Joint QoS Prediction for Web Services based on Deep Fusion of Features

In order to solve the problem of insufficient accuracy of Web service QoS prediction, a joint QoS prediction method for Web services based on the deep fusion of features was proposed by considering the hidden environmental preference information in QoS and the common characteristics of multi-class QoS. QoS data was modeled as user-service bipartite graph at first, then, multi-component graph convolution neural network was used for feature extraction and mapping, and weighted fusion method was used for the same dimensional mapping of multi-class of QoS features. Subsequently, the attention factor decomposition machine was used to extract the first-order features, second-order interactive features and high-order interactive features of the mapped feature vector. Finally, the results of each part were combined to achieve the joint QoS prediction. The experimental results show that the proposed method is superior to the existing QoS prediction methods in terms of root mean square error (RMSE) and average absolute error (MAE).

Matrix Factorization-based Web Service QoS Prediction: Methods and Applications

Matrix Factorization-based Web Service QoS Prediction: Methods and Applications

Optimizing User-Level Packet Scheduling Performance through Optimal CQI-Based Resource Allocation in LTE

Our research investigates the Dynamic Resource Allocation in the Downlink of OFDMA-based LTE-A networks. It addresses both user-level and system-level packet scheduling performance. At the user-level, a Traffic Differentiator stage segregates packet queues from active users into different service queues based on service types. Users are prioritized within each service queue based on their QoS requirements and wireless channel conditions, utilizing SPSSA. At the system-level, fairness among users is a key consideration. We propose the PITDSA in the TD Scheduler stage, which aims to allocate just enough radio resource to real-time and non-real time services and assign the remaining available resource to background service. In the FD Scheduler stage, we propose an optimal CQI selection algorithm for resource allocation to exploit frequency domain multi-user diversity. We present our simulation results and analyses of all novel algorithms, and the performance of the Optimal CQI selection algorithm is compared with other algorithms. Our proposed scheduling algorithm demonstrates improved QoS for real-time and non-real time services while maintaining a good traded user-level and system-level performance. Future work can focus on optimizing the throughput or the fairness or both, and more advanced and complex techniques can be designed with the same goal.

Retracted: Mobile Internet of Things Dynamic Grid QoS Service Matching Mechanism and Simulation Analysis

Retracted: Mobile Internet of Things Dynamic Grid QoS Service Matching Mechanism and Simulation Analysis

ROS2 Real-time Performance Optimization and Evaluation

Real-time interaction with uncertain and dynamic environments is essential for robotic systems to achieve functions such as visual perception, force interaction, spatial obstacle avoidance, and motion planning. To ensure the reliability and determinism of system execution, a flexible real-time control system architecture and interaction algorithm are required. The ROS framework was designed to improve the reusability of robotic software development by providing a distributed structure, hardware abstraction, message-passing mechanism, and application prototypes. Rich ecosystems for robotic development have been built around ROS1 and ROS2 architectures based on the Linux system. However, because of the fairness scheduling principle of the default Linux system design and the complexity of the kernel, the system does not have real-time computing. To achieve a balance between real-time and non-real-time computing, this paper uses the transmission mechanism of ROS2, combines it with the scheduling mechanism of the Linux operating system, and uses Preempt_RT to enhance the real-time computing of ROS1 and ROS2. The real-time performance evaluation of ROS1 and ROS2 is conducted from multiple perspectives, including throughput, transmission mode, QoS service quality, frequency, number of subscription nodes and EtherCAT master. This paper makes two significant contributions: firstly, it employs Preempt_RT to optimize the native ROS2 system, effectively enhancing the real-time performance of native ROS2 message transmission; secondly, it conducts a comprehensive evaluation of the real-time performance of both native and optimized ROS2 systems. This comparison elucidates the benefits of the optimized ROS2 architecture regarding real-time performance, with results vividly demonstrated through illustrative figures.

Reliability-Aware Task Processing and Offloading for Data-Intensive Applications in Edge computing

With the growing popularity of mobile edge computing (MEC), a number of data-intensive applications have been deployed. Quality of Service (QoS), as one of the most important requirements in MEC, has attracted significant attention of both academia and industry. There are two challenges for ensuring the Qos of the data-intensive services. Firstly, besides performance, reliability is another important concern especially for some critical applications, which remains unexplored. Secondly, data compression has to be involved for reducing the heavy workload of wireless communications at the edge of the network. To overcome these challenges, this paper jointly studies the reliability-aware data compression and task offloading for data-intensive applications in MEC. Markov models are constructed to capture the dynamics of the state transitions of MEC systems, and quantitative analyses are conducted for performance and reliability evaluation. For fully taking advantages of data compression in QoS guarantee, we formulate an optimization problem with the objective of minimizing latency while satisfying constraints on reliability and energy consumption. After describing the NP-Hardness of the problem, we apply the techniques of problem linearization and constraint relaxation, transform the original problem into a convex optimization problem. Then, the problem can be solved in a parallel way by introducing the Alternating Direction Multiplier Method (ADMM), and a distributed Reliability-aware Task Processing and Offloading (RTPO) algorithm is presented. Finally, extensive simulation experiments are conducted to validate the efficacy of our approach, the experimental results illustrate the superiority of our approach over both baseline and state-of-the-art algorithm.

Toward Effective Personalized Service QoS Prediction From the Perspective of Multi-Task Learning

End-to-end QoS measurement plays an indispensable role in the decision-making of cloud services and IoT services. Many efforts have paid on developing QoS prediction approaches in the past decade leveraging the principle of collaborative filtering. But there remain many challenging issues concerning multi-task prediction requirements, feature selection for heterogeneous prediction tasks, and model training. To this end, we propose an effective personalized service QoS prediction method from the perspective of multi-task learning, named PMT. PMT consists of specially-designed feature selection components and a multi-step model training strategy. The feature selection method leverages the principle of multi-expert decision-making and self-attention mechanism. The multi-step model training enables a weight-free configuration for parallel prediction tasks. Experimental results on a large dataset with two tasks and a small dataset with three tasks demonstrate that PMT is superior to the state-of-the-art QoS prediction methods.

Adaptive Bandwidth Allocation for Massive MIMO Systems Based on Multiple Services

Aiming at the characteristics of resource periodicity in massive MIMO systems and bandwidth allocation without comprehensive consideration of user service QoS and channel state information, resulting in poor user satisfaction and low bandwidth utilization, this paper proposes an adaptive bandwidth allocation method based on user services. This method comprehensively considers factors, such as user service QoS, channel state information, and resource periodicity, to adaptively allocate bandwidth for users using different services. Firstly, based on the service priority, the user priority is dynamically adjusted according to the current channel state information and the continuous periodicity of the allocation, and the user is scheduled.; Secondly, the dynamic priority is combined with the minimum guaranteed time slot to establish the objective function of adaptive bandwidth allocation. Finally, chaos theory, Levy flight, and reverse learning are integrated to improve the bald eagle optimization algorithm. The improved bald eagle algorithm is used to solve the problem, and the optimal solution to bandwidth allocation is obtained. The simulation shows that compared with the traditional bandwidth allocation method based on user service quality perception, the bandwidth allocation algorithm based on the minimum rate requirement, and the ant colony-based allocation algorithm, the bandwidth allocation method proposed in this paper improves the system utility value, bandwidth utilization rate, throughput, and user satisfaction by 23.70%, 4.22%, 6.55%, and 4.28%, respectively, and better meets the business needs of users.

Joint Optimization of Massive MIMO System Resources Based on Service QoS

Aiming at the problem of low throughput and energy efficiency caused by the mutual restriction of energy efficiency and spectral efficiency in massive MIMO systems and the fact that resource allocation does not consider the factors of user service QoS and the upper and lower speed limits, a resource joint optimization method based on user service QoS guarantee is proposed. The method first performs user scheduling according to service delay and channel state under the condition of equal power distribution and calculates the current system capacity, and then combines transmit antenna power and service QoS constraints to redistribute power, and corrects the system capacity, establishing the objective function for the joint optimization of the spectral efficiency and energy efficiency. An algorithm combining deep learning and Q learning is used to solve the problem, and finally, the purpose of joint optimization is achieved. The simulation shows that the joint optimization method proposed in this paper can control the timeout of user data packets more finely and, at the same time, obtain greater energy efficiency and throughput.

Exploring the potential of dynamic quality of service cloud-based network slicing in 5G and next generation virtualized networks: A simulation-based study

With global mobile traffic doubling every 18 months along with the massive increase in unique data users across digital platforms during the Covid-19 pandemic, the world's demand for high-speed connectivity continues to increase every second. The Covid-19 pandemic has transformed the global workforce with remote working arrangements, embracing new platforms, subsequently increasing the requirement for reliable, quality, ubiquitous high speed data connectedness everywhere. 5th Generation (5G) mobile communication technology provides an effective solution, modernizing network infrastructure, efficiently providing reliable high bandwidth capacity for massive data growth and ultra-low latency, making delays virtually impossible to perceive. This means elevating reality even further with fiber-like speeds for everyone, everywhere. 5G network slicing makes it possible for services to own dedicated portions of the 5G network with guaranteed performance for their particular need. These virtual networks allow for a service like IoT, at industrial scale, making society more sustainable and increasing resource efficiency. Our paper proposes a model for 5G network slice creation that meets industry verticals service QoS requirements which are critical to service adoption. A software emulation is utilized to guide the design and evaluation of our proposed model. Simulated results show QoS guarantees are met for the reviewed service requirements in varying network conditions.