User Quality Of Service Requirements Research Articles

A new QoS-aware service composition technique in cloud computing using capuchin search algorithm

Cloud platforms rapidly develop and supply many cloud-based services with various functions but varying Quality of Service (QoS) attributes. In the context of cloud computing, a major concern is how to incorporate disparate cloud services to maximize QoS value, which is underpinned by meeting the local QoS requirements of users. To deal with multiple QoS parameters in service composition, we propose a new mechanism using the Capuchin Search Algorithm (CapSA) that provides solutions to satisfy connectivity constraints. The CapSA algorithm mimics the dynamic behavior of capuchin monkeys in the forest to solve global and local optimization problems. The CapSA was selected for its simplicity, reduced computational complexity, and exploration/exploitation balance. The approach takes the form of an optimization problem to minimize energy consumption and total cost. Compared with other approaches, the proposed algorithm improves the efficiency of cloud service composition by convergent rapidly and obtaining better compositions.

On user fairness of uplink rate splitting multiple access with randomly deployed users

In this paper, we investigate fairness of an uplink rate splitting multiple access (RSMA) system with randomly deployed users, which are modeled as a homogeneous Poisson Point Process. Specifically, randomly deployed users are divided into the central and edge groups, respectively. Two RSMA schemes are considered, namely, the cognitive radio-inspired RSMA (CR-RSMA) and rate fairness-oriented RSMA (RF-RSMA). In the CR-RSMA scheme, the quality of service (QoS) requirement of the primary user (PU) is given a priority, while the secondary user (SU) is served without affecting the outage performance of the PU. In the RF-RSMA scheme, by flexibly adjusting the SU’s power allocation factor to improve the rates fairness of users. Taking into account the user's QoS requirements, we first derive the closed-form expression for the outage probability of a user with a fixed location, and then we utilize a modified Jain's index to measure user fairness. Considering that the distance between the user and the base station (BS) affects the channel fading, the relative position of the user also impacts the RSMA performance. To reveal the effect of user's location on the outage performance and user fairness, we propose the distance-based user pairing strategies and obtain closed-form expressions for outage probabilities, which by using stochastic geometry. Simulation results validate the accuracy of the analytical indicates that, in the medium and high signal-to-noise ratio regions, the proposed RSMA schemes achieve better user fairness than the benchmark schemes.

User Pairing for Delay-Limited NOMA-Based Satellite Networks with Deep Reinforcement Learning.

In this paper, we investigate a user pairing problem in power domain non-orthogonal multiple access (NOMA) scheme-aided satellite networks. In the considered scenario, different satellite applications are assumed with various delay quality-of-service (QoS) requirements, and the concept of effective capacity is employed to characterize the effect of delay QoS limitations on achieved performance. Based on this, our objective was to select users to form a NOMA user pair and utilize resource efficiently. To this end, a power allocation coefficient was firstly obtained by ensuring that the achieved capacity of users with sensitive delay QoS requirements was not less than that achieved with an orthogonal multiple access (OMA) scheme. Then, considering that user selection in a delay-limited NOMA-based satellite network is intractable and non-convex, a deep reinforcement learning (DRL) algorithm was employed for dynamic user selection. Specifically, channel conditions and delay QoS requirements of users were carefully selected as state, and a DRL algorithm was used to search for the optimal user who could achieve the maximum performance with the power allocation factor, to pair with the delay QoS-sensitive user to form a NOMA user pair for each state. Simulation results are provided to demonstrate that the proposed DRL-based user selection scheme can output the optimal action in each time slot and, thus, provide superior performance than that achieved with a random selection strategy and OMA scheme.

Automatic Spike Neural Technique for Slicing Bandwidth Estimated Virtual Buffer-Size in Network Environment

The Next-generation networks, such as 5G and 6G, need capacity and requirements for low latency, and high dependability. According to experts, one of the most important features of (5 and 6) G networks is network slicing. To enhance the Quality of Service (QoS), network operators may now operate many instances on the same infrastructure due to configuring able slicing QoS. Each virtualized network resource, such as connection bandwidth, buffer size, and computing functions, may have a varied number of virtualized network resources. Because network resources are limited, virtual resources of the slices must be carefully coordinated to meet the different QoS requirements of users and services. These networks may be modified to achieve QoS using Artificial Intelligence (AI) and machine learning (ML). Developing an intelligent decision-making system for network management and reducing network slice failures requires reconfigurable wireless network solutions with machine learning capabilities. Using Spiking Neural Network (SNN) and prediction, we have developed a 'Buffer-Size Management' model for controlling network load efficiency by managing the slice's buffer size. To analyze incoming traffic and predict the network slice buffer size; our proposed Buffer-Size Management model can intelligently choose the best amount of buffer size for each slice to reduce packet loss ratio, increase throughput to 95% and reduce network failure by about 97%.

A Multi-Objective Cloud Workflow Scheduling Strategy Based on the Modified HEFT Algorithm

Aiming at the issues of poor system resource utilization and the challenge of ensuring the quality of service (QoS) in scientific workflow scheduling in the current cloud computing environment. By combining the existing IaaS cloud resource model with the scientific workflow task model, the article proposed a cloud workflow scheduling model framework. Firstly, proposed multi-objective QoS constraints based on the execution time and costs in scientific workflow scheduling. Then, modified the HEFT algorithm which was a typical scientific workflow scheduling algorithm. Based on the modified HEFT algorithm makes full use of the idle time of resources and maximizes the multi-objective QoS requirements of users. The comparative experiments show that the scheduling strategy of the modified HEFT algorithm can effectively optimize the costs and execution time of scientific workflow scheduling and achieve higher system resource utilization.

Effective capacity optimization for UDN

For the ultra-dense distributed network (UDN), the intensive deployment leads to the collision problem of the unlicensed spectrum access due to the random contention access mechanism of the unlicensed spectrum, which is difficult to improve the user quality of service (QoS). Here, the wireless access process of the licensed assisted access (LAA) network is modelled as a four-state semi-Markov model. Then, the effective capacity (EC) of the UDN based on the random access is derived, and the network capacity limit under the QoS requirements in the unlicensed spectrum is given. Based on the obtained EC, two power control algorithms are proposed to maximize the EC and effective energy efficiency (EEE) respectively under the user QoS requirements. Finally, numerical results verify the accuracy of the proposed theory, and the capacity-delay domain of the UDN is given. The simulation results show that the proposed algorithms improve the EC and the EEE compared with existing classical schemes.

An Adaptive Energy-Aware Stochastic Task Execution Algorithm in Virtualized Networked Datacenters

Virtualized networked datacenters (VNDCs) are gaining considerable attention for stochastic task execution under real-time constraints. However, the problem of efficiently minimizing the high energy consumption while ensuring high quality of service (QoS) in VNDCs has not been fully addressed. Although many solutions have been proposed to address this challenge, they are not efficient and only consider one or two of the energy consuming resources of VNDCs. To this end, an adaptive energy-aware algorithm, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MCEC</i> , that efficiently reduces the energy consumption of VNDCs while ensuring high QoS is proposed. Different from the existing approaches, the MCEC algorithm considers energy consumed by computing resources, virtual machine (VM) reconfiguration, communication resources and storage media resources while meeting user QoS requirements defined in the service level agreement (SLA). To validate the effectiveness of our algorithm, we carried out extensive experiments and compared the performance of our algorithm with existing baseline algorithms. The results of the experiments show that our algorithm substantially outperforms the baseline algorithms with respect to reducing energy consumption while respecting the service level agreement.

A Profit-Aware Coalition Game for Cooperative Content Caching at the Network Edge

The user demands to delay-sensitive applications have put forward new requirements for the mobile networks. Edge caching as a promising way is proposed to enhance the Quality of Service (QoS) for end users at the network edge. Given the widely distributed edge nodes, the content providers (CPs) usually prefer to integrate them with cooperative caching services, by forming a cache coalition. Although such a coalition could be beneficial as a whole, it neglects the profits of individual members, which is one major concern in forming the coalition itself. Besides, due to the poor scalability, the conventional cooperation scheme, which only considers fixed edge nodes, cannot adapt to the spatial and temporal imbalance of user requests. In this article, we tackle the problems of coalition establishment and profit allocation among the coalition members. Particularly, by adopting both fixed edge nodes and mobile vehicles as caching nodes, we propose a hybrid service provisioning framework and cooperative service caching and workload scheduling methods. To maximize the profits in managing the caching resources in the established coalition, we devise an optimization model with a mixed-integer programming (MIP), in which the QoS requirements of end users and caching capacities of each coalition member are also considered as constraints. In addition, based on the Hedonic game theory, we propose a dynamic coalition algorithm to guide each member to join or leave the coalition at each time slot out of its own profits. The experimental results demonstrate that compared to the cases only considering fixed caching nodes, our hybrid caching scheme can improve: 1) the overall profit of the coalition by 53% and 2) the average profit of individual participants by 42%, respectively.

Distributed multi-user QoS-aware service selection

Previous research on service selection in cloud environments often assumes a centralised broker that keeps track of all candidate services and selects the appropriate candidate for each user request based on quality of service (QoS). As the number of users and services increases, the centralised service selection algorithm becomes a bottleneck. We propose a distributed, multi-broker selection algorithm that enables real-time processing of a large number of user requests. Each broker keeps track of a subset of users and a subset of service candidates, and rebalancing is performed to ensure that no broker is overloaded. The selection procedure considers the user QoS requirements, service QoS values and their processing capacities. Simulations demonstrate a clear advantage of the proposed approach over a single-broker approach by up to 96% in terms of QoS satisfaction when using 100 brokers, and especially in terms of response time satisfaction, showing positive impact of user and service balancing, and analysing various selection algorithms run by the brokers.

A game-based power optimization for 5G femtocell networks

Spectrum sharing deployment of femtocells brings interferences which dramatically degrade network performance. Hence, interference control is a crucial challenge for femtocell networks. In this paper, we propose a power optimization approach for 5G femtocell networks consisting of macrocell and underlying femtocells to manage the interference. Firstly, we formulate the problem based on a non-cooperative game to analyze the competition among the users to access shared spectrum. We then design a pricing mechanism in the utility function to guarantee quality of service (QoS) requirements of macro users. The mechanism lets the macro users experience lower interference and achieve the minimum required data rate. As a result, QoS requirements of both macro and femto users are fulfilled in a non-cooperative manner. We also design a minimax decision rule to optimize the worst-case performance and find an optimal transmission power for each user. By adjusting the optimal power for each user, the maximum aggregate interference is minimized, and the network throughput is maximized. Finally, we develop an iterative learning-based algorithm to implement the proposed scheme and achieve the game equilibrium. Theoretical analysis and simulation results verifies the effectiveness of the proposed mechanism in terms of throughput maximization, QoS assurance and interference mitigation.

Dual Dynamic Scheduling for Hierarchical QoS in Uplink-NOMA: A Reinforcement Learning Approach.

The demand for bandwidth-intensive and delay-sensitive services is surging daily with the development of 5G technology, resulting in fierce competition for scarce radio resources. Power domain Nonorthogonal Multiple Access (NOMA) technologies can dramatically improve system capacity and spectrum efficiency. Unlike existing NOMA scheduling that mainly focuses on fairness, this paper proposes a power control solution for uplink hybrid OMA and PD-NOMA in dual dynamic environments: dynamic and imperfect channel information together with the random user-specific hierarchical quality of service (QoS). This paper models the power control problem as a nonconvex stochastic, which aims to maximize system energy efficiency while guaranteeing hierarchical user QoS requirements. Then, the problem is formulated as a partially observable Markov decision process (POMDP). Owing to the difficulty of modeling time-varying scenes, the urgency of fast convergency, the adaptability in a dynamic environment, and the continuity of the variables, a Deep Reinforcement Learning (DRL)-based method is proposed. This paper also transforms the hierarchical QoS constraint under the NOMA serial interference cancellation (SIC) scene to fit DRL. The simulation results verify the effectiveness and robustness of the proposed algorithm under a dual uncertain environment. As compared with the baseline Particle Swarm Optimization algorithm (PSO), the proposed DRL-based method has demonstrated satisfying performance.

Multi-criteria handover mobility management in 5G cellular network

To fulfill the future demand and expansion of the coverage of the network, ultra-dense deployment of small cell (SC) is an optimal solution for future 5G networks, which will ensure the UEs (User Equipment) continuous connectivity. However, these small cells (SCs) lead to the issue of interference, additional unnecessary handover (HO), signaling overhead, and which in turn decreases the overall quality of service (QoS) of the users. In this paper, an intelligent mobility management system based on Enhanced Multi-Objective Optimization Method by Ratio Analysis (E-MOORA) and Q-learning approach is introduced for handover optimization. E-MOORA method is the combination of modified entropy weighting technique and Multi-Objective Optimization Method by Ratio Analysis (MOORA) which introduces vector normalization. The proposed E-MOORA method judicially exploits the performance parameters and thus reduces ranking abnormality when it selects a HO target cell. Q-learning approach is applied to select the optimal triggering points to minimize the effect of frequent unnecessary handovers for satisfying user QoS requirements. The performance analysis results depict significant performance improvement in terms of minimizing the unnecessary HO, radio link failure, and user throughput compared to other existing Multi-Criteria Decision Making (MCDM) methods.

A Service Recommendation Method Based on Requirements for the Cloud Environment

In the cloud computing environment, there are huge amounts of functionally similar cloud services. Additionally, user requirements can change. Therefore, it is difficult to recommend services that meet users’ requirements. To overcome the problems, a service recommendation method based on requirements is proposed. First, we form user communities by clustering to reduce the recommended range. Second, we use the reported QoS (Quality of Service) values and the evaluated QoS values to predict the QoS requirements of users. Third, based on the requirements, the matching degree of users to services is obtained. Finally, based on the similarity between the target user and the user’s neighbors and the difference in their matching degree of service and the ratings of services by the neighbors, we can obtain a list of service recommendations for the target user. Compared to the traditional collaborative filtering method and the deviation-based method, our method improves the recommendation accuracy without lowering the efficiency.

Energy-Efficient Multivariate Privacy-Aware RF Spectrum Reservation in Wireless Virtualization for Wireless Internet of Things

Next generation communication systems are expected to be heterogeneous and to have varying Quality of Service (QoS) requirements for different Internet of Things (IoT) applications. Wireless virtualization is regarded as an emerging technology capable of handling the ever growing resource demands by those heterogeneous IoT applications and systems by enabling wireless resource sharing through slicing. Scarce wireless resources and expensive physical infrastructure are leased to business units. The business units do not own the infrastructure, but rather gain access through a subleasing process. However, due to the lack of efficient management of wireless resources, energy efficiency of the physical infrastructure cannot be guaranteed. A viable solution to this problem is to anticipate resource needs and proactively implement energy efficient measures to ensure QoS requirements of users are met using a reservation scheme. Previous spectrum reservation approaches do not consider energy efficiency in the reservation process. In this paper, we present an adaptive resource reservation scheme for wireless network virtualization. The proposed scheme relies on an informed estimate of resource needs by a combination of aggregated event data from multiple contributors, and prediction based on previous allocation data. We present a Privacy aware Aggregation Model (PrivAgg) that relies on the truthfulness of contributors by providing secure enrollment and communication. We also present a prediction algorithm, Volume and Bandwidth-conditioned Spectrum Selective Moving Average (VBSSMA), that enforces a multivariate filter for the allocation history in order to increase the accuracy of prediction. We evaluate the performance of the reservation scheme and algorithms using theoretical analysis and numerical results from extensive simulations. Numerical results over multiple configurations for the weight of the aggregator and predictor components shows that VBSSMA results in up to 27% less allocation cost and 4% less error than existing Volume-conditioned Spectrum Selective Moving Average reservation approach.

Transfer Learning for Autonomous Cell Activation Based on Relational Reinforcement Learning With Adaptive Reward

With the increasing threat of global warming due to high energy consumption of wireless network infrastructure, cell activation complements the capabilities of next-generation wireless technology. In this article, we propose an energy consumption optimization strategy based on deep reinforcement learning (DRL) and transfer learning (TL) techniques. We implement an adaptive reward to autonomously adjust parameters in a reward function to balance energy consumption and quality of service (QoS) requirement of users during the learning process. We further formulate a cell activation/deactivation problem as a Markov decision process and set up our proposed relational DRL model to meet the QoS requirements of users with a minimum number of active remote radio heads under a traffic model defined to simulate a real-world scenario. A weighted TL algorithm has been developed in DRL to validate sample data from a source task. Extensive simulations reveal that the proposed scheme based on the adaptive reward has better performance in balancing the QoS requirement of users and system energy consumption. Finally, based on our simulation results, we conclude that combining DRL with TL speeds up the learning process.

Cloud manufacturing service composition in IoT applications: a formal verification-based approach

Nowadays, one of the new solutions to develop Internet of Things (IoT) applications is cloud manufacturing. By increasing the number of actuators, industrial devices and sensors in industry 4.0 technology, the IoT is going to improve the intelligence of the enterprise information systems with a fast transition towards flexible, smarter, automated and reactive services. In order to respond the needs more quickly or matching the cloud services with modified requirements, cloud manufacturing service composition is used to take advantage of several services available in the Industrial IoT applications. The cloud-based manufacturing services can be characterized, published, shared, and retrieved over the cloud infrastructure to provide integrated services according to the user QoS requirements in a collaborative environment. In this work, a service composition approach is provided for cloud manufacturing in IoT applications. For evaluating the correctness of the proposed solution, a formal verification method is presented based on Labeled Transition System (LTS). Also, Whale Optimization Algorithm (WOA) is utilized to enhance the Quality of Service (QoS), when the number of cloud services have been increased. Finally, deadlock-free and reachability factors as important logical problems are satisfied in verification of the proposed approach. The verification results obtained under different Linear Temporal Logic (LTL) properties and various number of cloud providers indicate that the proposed approach is a correct and valid solution and it outperforms in terms of the reachability, verification time, deadlock, and memory usage.

Energy Efficiency Maximization in NOMA Enabled Backscatter Communications With QoS Guarantee

Energy efficiency (EE) is an important performance metric in communication systems. However, to the best of our knowledge, the energy-efficient resource allocation (RA) problem in non-orthogonal multiple access enabled backscatter communication networks (NOMA-BackComNet) comprehensively considering the user's quality of service (QoS) has not been investigated. In this letter, we present the first attempt to solve the EE-based RA problem for NOMA-BackComNet with QoS guarantee. The objective is to maximize the EE of users subject to the QoS requirements of users, the decoding order of successive interference cancellation and the reflection coefficient (RC) constraint, where the transmit power of the base station and the RC of the backscatter device are jointly optimized. To solve this non-convex problem, we develop a novel iteration algorithm by using Dinkelbach's method and the quadratic transformation approach. Simulation results verify the effectiveness of the proposed scheme in improving the EE by comparing it with the other schemes.

Enhanced Spectrum Access for QoS Provisioning in Multi-Class Cognitive D2D Communication System

Integration of Device-to-Device (D2D) communication into the cellular network can greatly enhance the spectrum utilization as well as creating additional communication opportunities. D2D can be combined with cognitive radio to further enhance spectrum utilization and cellular network performance. Unlike the traditional D2D communication approach which employs only cellular spectrum, cognitive D2D can enable sensing and utilizing non-cellular spectrum opportunistically as well, thus allowing to offload cellular base station traffic to non-cellular spectrum such as WiFi, Bluetooth, or TV white-spaces. Cognitive D2D users (cDUs) must vacate channel for primary users and hand-off to another secondary channel which renders it quite challenging to meet Quality of Service (QoS) requirements of multi-class cDUs in the presence of higher primary network load. In this work, an enhanced hybrid spectrum access scheme has been developed based on non-switching spectrum hand-off for multi-class DUs, utilizing both interweave and hybrid interweave underlay spectrum access strategies. Further, lower priority cDUs with non-real-time traffic remains in the system and wait for channels to become available, rather than being dropped from the system due to lack of secondary channels. A Continuous-Time Markov Chain (CTMC) has been developed to analyze the performance of the proposed scheme. For comparison, several cases, ranging from the simple cellular network to complex cellular-cognitive-D2D with hybrid-spectrum-access, have been analyzed. The main focus of the analysis is to compare the efficacy of enhanced hybrid spectrum access scheme with individual interweave and hybrid interweave underlay spectrum access strategies in terms of QoS provisioning for multi-class cDUs. The results depict improvement in throughput, spectrum utilization, and extended data delivery time for the proposed scheme and validate the suitability of the proposed scheme to meet QoS requirements for both delay-sensitive and delay-tolerant users of the multi-class cognitive D2D communication system.

3D Location and Resource Allocation Optimization for UAV-Enabled Emergency Networks Under Statistical QoS Constraint

Unmanned aerial vehicles (UAVs) as aerial base stations have attracted great attention in emergency communication networks due to flexible deployment. With the popularization of smart devices, the demand for multimedia services is increasing in disaster relief. Therefore, it is very important to significantly improve throughput of unmanned aerial vehicle base station (UAV-BS) while ensuring quality-of-service (QoS) of multimedia traffic. In this paper, we consider a UAV-BS to serve a group of users in the downlink who have different statistical delay-bound QoS requirements in an emergency situation. We formulate a problem to maximize the sum statistical-QoS-guaranteed throughput (effective capacity) of all users by jointly optimizing the UAV's 3D location, power and bandwidth allocation under each user's statistical QoS requirement constraint. The formulated problem is a non-linear non-convex optimization problem, which is very difficult to solve. To this end, we propose an efficient iteration algorithm based on the block coordinate descent and successive convex optimization techniques to solve it. Specifically, we decouple the primary problem into three sub-problems, which can be approximated as easy-to-solve convex optimization problems. In each iteration, three sub-problems are alternately optimized. Finally, numerical simulation results prove the effectiveness of our proposed algorithm compared with benchmarks.

Application of binary PSO for public cloud resources allocation system of video on demand (VoD) services

Video streaming, whether on demand or live, has become one of the most popular internet applications. However, financial investments required for it is a severe problem since it needs more real time storage, higher data transfer and a significant amount of computation than other kinds of multimedia data. To tackle this problem, cloud computing, offering services without investing in hardware or software, emerges as a preferred technology. However, there are a large number of cloud service providers and they offer different pricing strategies for various applications in various regions. Therefore, it is of great importance for them that incoming service requests are assigned to the appropriate cloud services at minimum cost and provide maximum user satisfaction [quality of service (QoS) attributes]. Due to the issues, such as multiple cloud providers, different QoS requirements, different service level agreements and uncertainties in demand, price and availability, the optimization of resource allocation present further challenges. The objective of our study is to optimize the cost and performance of video on demand applications using cloud content delivery networks, storage and transcoders based on the QoS requirements of users. To solve the NP-hard problem, Particle Swarm Optimization (PSO) technique is used due to the easiness in its concept and coding, less sensitive to the nature of the objective function, limited number of parameters and generating high quality solution within a short time. We propose a new method in which the optimum solution is affected not only by the best solution of the particle and global best solution but also by the best solution of the neighborhood particles in that iteration. This ternary approach is implemented into the well-known discrete and constrained PSO, achieving the minimum cost with user satisfaction for allocation of video requests to cloud resources. Although the proposed method yields better results in terms of accuracy, execution time of the algorithm is not reasonable. To overcome this inefficiency; ternary approach is embedded into multi-swarm PSO and it is parallelized and combined with greedy heuristic algorithms. The results of the comparison with the benchmarking algorithms show that our proposed method yields better results from the standpoint of both accuracy and execution time.