Quality Of Service Constraints Research Articles

Optimal location of base stations for cellular mobile network considering changes in users locations

In the past few decades, the demand for cellular networks has grown tremendously due to the growing demand for data. However, the vast majority of the users are dynamic since they use their smart phones and tablets to connect to these networks when they move to one location to another to attend sport games or other events for example. The location of these events might not cover the large demand. In this paper, we address the classical problem of locating base stations for a mobile cellular network to serve mobile users in a given geographical area considering the users' movements within the network. We developed a mixed integer programming model to provide the optimal location of base stations at different time periods with the network's minimum total cost (i.e., installation and operation of base stations). This paper considers the COST-231-Walfisch-Ikegami propagation model for the path loss calculation with a non-line-of-site situation. To demonstrate the capability of the proposed model, it is experimented with a real-world problem on the Red Sea coast of Saudi Arabia. The model was capable of finding the optimal base station locations with minimum installation and operational costs considering the capacity and quality of service constraints.

Efficient machine learning‐based hybrid resource allocation for device‐to‐device underlay communication in 5G wireless networks

SummaryDevices in close proximity can directly communicate with each other through device‐to‐device (D2D) communication, bypassing the need for base stations. To minimize the delay for D2D active clients, we use a sequential approach to reuse cellular‐type resources. Unlike conventional methods that focus solely on uplink or downlink resource distribution, our study introduces a novel optimization technique to maximize network throughput. Our proposal incorporates a hybrid approach that combines both downlink and uplink techniques for resource allocation. We utilize random forest and game theory algorithms to ensure smooth D2D communication while reducing interference between cellular and D2D pairings. Our hybrid structure effectively addresses challenges arising from intra‐ and inter‐cell interferences resulting from spectrum reusability and deployment. This approach also improves power control and quality of service. Traditionally, NP‐hard optimization solutions are proposed for mixed‐integer nonlinear problems. In our study, the critical stages include channel assignment and energy allocation. The objective problem in resource allocation considers parameters such as the transmission power of D2D active clients, cellular users, connection distance, base stations, and quality of service constraints. Our proposed hybrid method aims to enhance overall spectrum efficiency and network throughput. Simulated results demonstrate the superiority of our modified hybrid technique compared to existing joint resource allocation methods. This comprehensive approach represents a significant advancement in addressing the complexities associated with D2D communication, offering improved efficiency and performance in contemporary network environments.

SA-Based QoS Aware Workflow Scheduling of Collaborative Tasks in Grid Computing

Scheduling workflow tasks in grid computing is a complex process, especially if it is associated with satisfying the user's requirements to complete tasks within a specified time, with lowest possible cost. This paper presents a proposed Simulated Annealing (SA) based Grid Workflow Tasks Scheduling Approach (SA-GWTSA) that takes into account users’ QoS (quality of service) constraints in terms of cost and time. For a given set of inter-dependent workflow tasks, it generates an optimal schedule, which minimizes the execution time and cost, such that the optimized time is within the time constraints (deadline) imposed by the user. In SA-GWTSA, the workflow tasks, which are modeled as a DAG, are divided into task divisions, each of which consists of a set of sequential tasks. Then, the optimal sub-schedules of all task divisions are computed applying SA algorithm, and used to obtain the execution schedule of the entire workflow. In the proposed algorithm, the sub-schedule of each branch division is represented by a vector, in which each element holds the ID of the service provider chosen from a list of service providers capable of executing the corresponding task in the branch. The algorithm uses a fitness function that is formulated as a multi-objective function of time and cost, which gives users the ability to determine their requirements of time against cost, by changing the weighting coefficients in the objective function. The paper also exhibits the experimental results of assessing the performance of SA-GWTSA with workflows samples of different sizes, compared to different scheduling algorithms: Greedy-Time, Greedy-Cost, and Modified Greedy-Cost.

Decentralized Scheduling with QoS Constraints: Achieving O(1) QoS Regret of Multi-Player Bandits

We consider a decentralized multi-player multi-armed bandit (MP-MAB) problem where players cannot observe the actions and rewards of other players and no explicit communication or coordination between players is possible. Prior studies mostly focus on maximizing the sum of rewards of the players over time. However, the total reward maximization learning may lead to imbalanced reward among players, leading to poor Quality of Service (QoS) for some players. In contrast, our objective is to let each player n achieve a predetermined expected average reward over time, i.e., achieving a predetermined level of QoS. We develop a novel decentralized MP-MAB algorithm to accomplish this objective by leveraging the methodology of randomized matching. We prove that our decentralized algorithm can ensure that all players have an O(1) QoS regret. We also reveal an analog between our MP-MAB model and the online wireless queuing systems, which builds a connection between QoS in MP-MAB learning and stability in queuing theory.

Mobility and energy efficient services composition algorithm with QoS guarantee for large scale Cyber–Physical–Social Systems

Due to the mobile and random nature of services in cyber–physical–social systems (CPSSs), developing service composition approaches that ensure high availability, minimal energy consumption, and high quality of service (QoS) remains a complex challenge. Over the last two decades, several service composition approaches have been proposed in the literature to deal with this challenge. Nevertheless, the existing approaches have certain limitations, particularly in situations where services may move from one location to another, become unavailable due to intensive battery usage, encounter failures, or undergo a decline in quality. These limitations often arise because these approaches do not simultaneously integrate mobility, energy, and QoS constraints while defining the user’s movement in a random manner. In this paper, the learning-based swarm optimization-aware service composition algorithm (LS-SCA) is proposed to overcome the aforementioned shortcoming. This approach surpasses existing ones by accounting simultaneously for the user’s mobility, energy, and QoS criteria during the service composition process. First, the Small World in Motion (SWIM) mobility model is employed in this study to determine the user’s mobility traces, avoiding the random generation of users’ traces. Second, an energy consumption model is proposed to increase the energy efficiency by avoiding the overuse of the devices’ batteries that can reduce the availability of services and lead to the composition failure. Third, the two-phase learning-based swarm optimizer (TPLSO) method is used in the composition process to find the sub-optimal composition that satisfies the global QoS constraints with the highest utility in terms of mobility, energy, and QoS. Unlike the most existing metaheuristic-based service composition approaches where the overall composition population is improved over a given number of iterations, the TPLSO method is exploited in this paper to improve only a subset of compositions, which reduces the composition time and increases the QoS utility of the composition. The simulation scenarios using two real datasets demonstrate that the LS-SCA approach outperforms six baselines in terms of energy consumption, QoS utility, and availability of composition. This notable performance makes the proposed approach more suitable for real-world applications where energy efficiency, QoS, and availability are crucial factors to consider.

Network sum-rate maximization for network-coded clustered uplink NOMA networks with SWIPT-enabled relays

This paper considers network sum-rate maximization for network-coded clustered uplink non-orthogonal multiple-access (NOMA) networks with simultaneous wireless information and power transfer (SWIPT)-enabled relays. Specifically, the aim is to perform joint power allocation, power-splitting, and relay selection (J-PA-PS-RS), subject to quality-of-service (QoS) requirements. The formulated problem is excessively computationally-intensive, as it is a non-convex optimization problem. To efficiently solve it, it is split into two sub-problems: (1) joint power allocation and power-splitting per relay, and (2) relay selection. Particularly, a solution procedure is proposed, where the joint user and relay power allocation, and power-splitting per relay sub-problem is solved via a low-complexity iterative three-layer algorithm, and then followed by optimal relay selection. Simulation results have revealed that the proposed solution procedure can efficiently be used to maximize the network sum-rate, achieve near-optimal solutions, and outperform other benchmark schemes, while satisfying QoS constraints.

A modified LSTM with QoS aware hybrid AVO algorithm to enhance resource allocation in D2D communication

In communication technologies, device-to-device (D2D) communication is essential for resource management and power control, which are major research concerns nowadays. D2D resource allocation involves dividing vital resources, such as time, power, and spectrum, among several devices. Each device can connect to other devices via one or more frequency channels. D2D communication shares the cellular user resources, while signal power transmission causes interference to the users who share the same channel. So, there is a need to control the power of the D2D device to prevent interference. For proper power control and optimization of multi-channel D2D communication, which is a challenging task, we proposed a deep learning approach incorporating a hybrid resource allocation framework. This framework aims to increase the sum rate of D2D user equipment (DUE) while considering quality of service (QoS) factors like limiting interference to cellular user equipment (CUE) and guaranteeing individual DUE rates above a certain threshold. The proposed resource allocation scheme combines two methods, namely a metaheuristic hybrid particle swarm Cauchy approach to African vulture optimization (HPSCAV) and a modified long short-term memory (MLSTM) based approach. The HPSCAV scheme helps to ensure that the QoS constraints are met, while the MLSTM-based approach is utilized for efficient resource allocation by optimizing the power and improving it with HPSCAV. Simulation results validate that the proposed model achieved better performance in various metrics such as system capacity, power consumption, spectral efficiency (SE), and energy efficiency (EE).

Resource allocation for integrated satellite‐terrestrial networks based on RSMA

AbstractTo achieve high data rate transmission of virtual reality (VR) applications globally, a resource allocation algorithm based on Rate‐Splitting Multiple Access (RSMA) in an integrated satellite‐terrestrial network (ISTN) is proposed. In this network, a user selection strategy is proposed. The maximum transmission power budgets of the base station and satellite, as well as the service quality constraints for all users, are considered. Then, a joint resource allocation problem is formulated. To tackle the non‐convex problem, auxiliary variables are introduced and the first‐order Taylor expansion method is used to convert it into a convex one. An iterative algorithm is proposed based on the successive convex approximation (SCA) to allocate rate and power resources. Simulation results demonstrate that the proposed algorithm significantly enhances the transmission rate and effectively satisfies the VR transmission rate requirements, when compared to benchmark algorithms.

Energy-Constrained Scheduling for Weakly Hard Real-Time Systems Using Standby-Sparing

For real-time embedded systems, QoS (Quality of Service), fault tolerance, and energy budget constraint are among the primary design concerns. In this research, we investigate the problem of energy constrained standby-sparing for both periodic and aperiodic tasks in a weakly hard real-time environment. The standby-sparing systems adopt a primary processor and a spare processor to provide fault tolerance for both permanent and transient faults. For such kind of systems, we firstly propose several novel standby-sparing schemes for the periodic tasks which can ensure the system feasibility under tighter energy budget constraint than the traditional ones. Then based on them integrated approachs for both periodic and aperiodic tasks are proposed to minimize the aperiodic response time whilst achieving better energy and QoS performance under the given energy budget constraint. The evaluation results demonstrated that the proposed techniques significantly outperformed the existing state-of-the-art approaches in terms of feasibility and system performance while ensuring QoS and fault tolerance under the given energy budget constraint.

GA-based QOS-aware workflow scheduling of deadline tasks in grid computing

Grid computing is the aggregation of the power of heterogeneous, geographically distributed computing resources to provide high-performance computing. To benefit from the grid computing capabilities, effectual scheduling algorithms are primarily essential. This paper presents a GA-based approach, called Grid Workflow Tasks Scheduling Algorithm (GWTSA), for scheduling workflow tasks on grid services based on users’ QoS (quality of service) constraints in terms of cost and time. For a given set of inter-dependent workflow tasks, it generates an optimal schedule, which minimizes the execution time and cost, such that the optimized time be within the time constraints (deadline) imposed by the user. In GWTSA, the workflow tasks are modeled as a DAG, which is divided, then the optimal sub-schedules of all task divisions are computed and used to obtain the execution schedule of the entire workflow. A GA-based technique is employed in GWTSA to compute the optimal execution sub-schedule for each branch division that consists of a set of sequential tasks. In this technique, the chromosome represents a branch division, where each gene holds the id of the service provider chosen to execute the corresponding task in the branch; and the fitness function is formulated as a multi-objective function of time and cost, this gives users the ability to determine their requirements if speed against cost or vice versa, by changing the weighting coefficients in the fitness function. The paper also exhibits the experimental results of assessing the performance of GWTSA with workflow samples of different sizes.

QoS-driven resource allocation in fog radio access network: A VR service perspective.

While immersive media services represented by virtual reality (VR) are booming, They are facing fundamental challenges, i.e., soaring multimedia applications, large operation costs and scarce spectrum resources. It is difficult to simultaneously address these service challenges in a conventional radio access network (RAN) system. These problems motivated us to explore a quality-of-service (QoS)-driven resource allocation framework from VR service perspective based on the fog radio access network (F-RAN) architecture. We elaborated details of deployment on the caching allocation, dynamic base station (BS) clustering, statistical beamforming and cost strategy under the QoS constraints in the F-RAN architecture. The key solutions aimed to break through the bottleneck of the network design and to deep integrate the network-computing resources from different perspectives of cloud, network, edge, terminal and use of collaboration and integration. Accordingly, we provided a tailored algorithm to solve the corresponding formulation problem. This is the first design of VR services based on caching and statistical beamforming under the F-RAN. A case study provided to demonstrate the advantage of our proposed framework compared with existing schemes. Finally, we concluded the article and discussed possible open research problems.

Joint design of transmit precoding and antenna selection for multi-user multi-target MIMO DFRC

Dual-function radar and communications (DFRC) systems based on multiple-input multiple-output (MIMO) arrays have received considerable attention in recent years due to their excellent ability to alleviate spectrum congestion. The MIMO DFRC systems enable high-resolution detection of multiple targets while communicating with multiple users simultaneously. However, MIMO arrays require numerous radio frequency (RF) units and suffer a strong mutual coupling among antennas, resulting in significant system overhead and performance degradation, respectively. In light of this, this paper investigates the joint optimization of transmit precoding and antenna selection for MIMO DFRC systems, aiming to improve the angular ambiguity function with a guaranteed communication quality of service (QoS) using a small number of antennas. To address the resultant non-convex optimization problem, both the indirect and direct precoding methods are proposed. In the former, the waveform covariance and antenna selection vector are first jointly optimized via a promoted sparsity along the covariance diagonal, followed by the precoding matrix indirectly derived from the optimal covariance. In the latter, the precoding matrix is directly optimized via an imposed group sparsity under the communication QoS and power constraints. Simulation results demonstrate that the proposed sparse MIMO DFRC system with fewer active antennas can achieve comparable dual-functional performance to that of the full array system.

Constrained DRL for Energy Efficiency Optimization in RSMA-Based Integrated Satellite Terrestrial Network.

To accommodate the requirements of extensive coverage and ubiquitous connectivity in 6G communications, satellite plays a more significant role in it. As users and devices explosively grow, new multiple access technologies are called for. Among the new candidates, rate splitting multiple access (RSMA) shows great potential. Since satellites are power-limited, we investigate the energy-efficient resource allocation in the integrated satellite terrestrial network (ISTN)-adopting RSMA scheme in this paper. However, this non-convex problem is challenging to solve using conventional model-based methods. Because this optimization task has a quality of service (QoS) requirement and continuous action/state space, we propose to use constrained soft actor-critic (SAC) to tackle it. This policy-gradient algorithm incorporates the Lagrangian relaxation technique to convert the original constrained problem into a penalized unconstrained one. The reward is maximized while the requirements are satisfied. Moreover, the learning process is time-consuming and unnecessary when little changes in the network. So, an on-off mechanism is introduced to avoid this situation. By calculating the difference between the current state and the last one, the system will decide to learn a new action or take the last one. The simulation results show that the proposed algorithm can outperform other benchmark algorithms in terms of energy efficiency while satisfying the QoS constraint. In addition, the time consumption is lowered because of the on-off design.

Optimal Service Migration for Data and Reasoning Fabric

In this paper we consider migration problem for Data and Reasoning Fabric (DRF)-enabled airspace operations assuming a fixed cloud/edge infrastructure with allocated computing, storage, and power resources, where cloud/edge servers and communication stations are in a wired connected network, while vehicles use a wireless network for communication. The objective is to automatically select the best location for the requested service execution, which achieves minimum cost while satisfying the user quality of service (QoS) and available resources constraints. To this end, estimates of the response time, consumed energy, and total cost are defined for each potential compute location. A mixed-integer linear program is then formulated and solved to identify optimal compute locations given QoS constraints and network infrastructure limitations, with worst-case vehicle positioning. The approach is applied to trajectory replanning use case to avoid a collision with an emergency vehicle in real time.

MADDPG-empowered slice reconfiguration approach for 5G multi-tier system

With the advancement of fifth-generation (5G) communication technology, plenty of vertical applications have emerged. These applications usually center on highly different quality of service (QoS) requirements, posing challenges for the supporting communication network. To address these challenges, network slicing has been proposed to partition the underlying network into multiple tailored logical networks, called as network slices. Each slice corresponds to a specific application. However, fluctuations in user activities for applications may lead to workload variation across corresponding slices, which compromising the consistent QoS provisioning. In this context, slice reconfiguration becomes imperative to ensure satisfactory user experiences. In this work, we introduce the multi-agent deep reinforcement learning method to tackle the slice reconfiguration problem and propose the multi-agent deep deterministic policy gradient-empowered slice reconfiguration (MADDPG-SR) algorithm. In MADDPG-SR, each network function is abstracted as an agent that learns to optimize its own migration in the multi-layer network to minimize the preference-weighted processing resource cost while adhering QoS and resource constraints. The proposed method is compared with four other benchmarks, and the comparison results demonstrate that MADDPG-SR can significantly reduce both the preference-weighted resource cost, and the running time for acquiring slice reconfiguration decisions while ensuring the QoS performance of slices.

Integration of NOMA With Reflecting Intelligent Surfaces: A Multi-Cell Optimization With SIC Decoding Errors

Reflecting intelligent surfaces (RIS) has gained significant attention due to its high energy and spectral efficiency in next-generation wireless networks. By using low-cost passive reflecting elements, RIS can smartly reconfigure the signal propagation to extend the wireless communication coverage. On the other hand, non-orthogonal multiple access (NOMA) has been proven as a key air interface technique for supporting massive connections over limited resources. Utilizing the superposition coding and successive interference cancellation (SIC) techniques, NOMA can multiplex multiple users over the same spectrum and time resources by allocating different power levels. This paper proposes a new optimization scheme in a multi-cell RIS-NOMA network to enhance the spectral efficiency under SIC decoding errors. In particular, the power budget of the base station and the transmit power of NOMA users while the passive beamforming of RIS is simultaneously optimized in each cell. Due to objective function and quality of service constraints, the joint problem is formulated as non-convex, which is very complex and challenging to obtain the optimal global solution. To reduce the complexity and make the problem tractable, we first decouple the original problem into two sub-problems for power allocation and passive beamforming. Then, the efficient solution of each sub-problem is obtained in two-steps. In the first-step of For power allocation sub-problem, we transform it to a convex problem by the inner approximation method and then solve it through a standard convex optimization solver in the second-step. Accordingly, in the first-step of passive beamforming, it is transformed into a standard semi-definite programming problem by successive convex approximation and different of convex programming methods. Then, penalty based method is used to achieve a Rank-1 solution for passive beamforming in second-step. Numerical results demonstrate the benefits of the proposed optimization scheme in the multi-cell RIS-NOMA network.

An Energy-Efficient Dynamic Scheduling Method of Deadline-Constrained Workflows in a Cloud Environment

With the rapid development of cloud applications, the computing requests of cloud data centers have increased significantly, consuming a lot of energy, making cloud data centers unsustainable, which is very unfavorable from both the cloud provider’s point of view and the environmental point of view. Therefore, it is crucial to minimize energy consumption and improve resource utilization while ensuring user service quality constraints. In this paper, we propose a hybrid workflow scheduling algorithm (Online Hybrid Dynamic Scheduling, OHDS), which aims to minimize the energy consumption of tasks and maximize service resource utilization while satisfying the sub-deadline and data dependency constraints of workflow tasks. Firstly, the data dependencies between workflow tasks are considered for multi-task merging, and sub-deadline constraints are assigned to workflow tasks based on task priority. Secondly, based on the independent nature of the tasks of different workflows, a hybrid scheduling of multiple workflows is performed to reduce service idle time. Then, the workflow task scheduling priority and its sub-deadlines are dynamically adjusted, and the service status is sensed by the CPU utilization of the service, and the workload on the overloaded/underloaded service is balanced by dynamic migration of virtual machines. Finally, the OHDS method is compared with three existing scheduling methods to verify its better performance in terms of scheduling energy consumption, scheduling success rate and service resource utilization.

A load-balanced algorithm for Internet Gateway placement in Backbone Wireless Mesh Networks

The placement of Internet Gateways (IGWs) is crucial in designing Backbone Wireless Mesh Networks (BWMNs) as it establishes the connection between the BWMN and the wired backbone. Efficient placement ensures high-bandwidth network coverage and utilization of the BWMN architecture. Despite considerable research efforts devoted to the IGW placement problem in WMNs, further research is still needed to tackle IGW reduction, improve Quality of Service (QoS), and achieve load balancing among IGWs and Wireless Mesh Routers (WMRs). In this paper, we propose a Load-balanced Algorithm for IGW placement in BWMN to achieve high-bandwidth network coverage while satisfying QoS constraints. The algorithm distributes WMRs among IGWs based on their location and computation degree, considering the load balance between IGWs and WMRs in each cluster. Experimental results demonstrate that our algorithm outperforms existing algorithms in terms of the number of IGWs placed in various scenarios. Our algorithm achieves an impressive reduction of more than 20% in the number of IGWs required in small typologies, and the improvement further escalates in larger topologies. To accomplish this, we utilize the innovative IGW Reallocation() algorithm, which accurately calculates the load ratio for each WMR within each cluster, facilitating the selection of the most suitable WMR to be designated as the new IGW. Additionally, our algorithm effectively addresses traffic load balancing for both IGWs and the entire network system by skillfully leveraging the relay links and delay hops present within WMRs forming the clusters. The outcomes of our research conclusively demonstrate that our Load-balanced Algorithm significantly enhances IGW placement efficiency, network coverage, and traffic load balancing, leading to improved performance, scalability, and a noteworthy contribution to the design and optimization of BWMNs.

Energy‐efficient machine type communication in HetNets under statistical QoS guarantees

AbstractThere has been an unprecedented rise in the Internet‐of‐Things (IoT) in the past decade. One of the underlying reasons for this exponential growth is that IoT allows for massive machine‐type communication (mMTC), which facilities machine‐to‐machine (M2M) communication with minimal human intervention. However, mMTC devices are characterized by resource and energy limitations, and thus require powerful and efficient power allocation methods that do not jeopardize quality‐of‐service (QoS) performance. In this work, a statistical QoS analysis of M2M communication in heterogeneous wireless networks is performed. We propose a communication mode selection (CMS) scheme that selects an uplink transmission link from the available candidate links based on pathloss criteria. The closed‐form expressions for the Effective Capacity and effective energy efficiency (Effective‐EE) of the M2M communication in the heterogeneous wireless network are derived and verified via simulations. The optimal fixed transmission rate that maximizes the Effective Capacity and Effective‐EE is also analytically analyzed. Furthermore, four scenarios are adopted to study the overall Effective Capacity and Effective‐EE of a two‐hop M2M communication link where the transmitter and access point operate under independent and identical QoS constraints. This article also investigates the impact of circuit power and the efficiency of the power amplifier on the achievable Effective‐EE of the communication link.

Electric vehicle charging network design with capacity and service considerations

Electric Vehicles (EVs) have a critical role in the future of sustainable transportation systems. Recently, improving the infrastructure for EVs has been a major focus of organizations around the world, such as local and federal governments, energy companies, and automotive original equipment manufacturers. In this article, we present a service network design framework to improve the infrastructure for EVs with quality of service constraints taken into consideration. We present a novel charging station location model with capacity allocation. The model maximizes the EV traffic flow captured and minimizes the average service time for drivers. The model formulation allows for assessing capacity and quality of service trade-offs, and operational insights are presented to reflect drivers’ expectations. We demonstrate an application for the solution approach on a real road network and evaluate the performance based on both computational efficiency and solution quality. Our results highlight how charging times at stations affect the quality of service in the whole service system by quantifying the improvements in flow coverage while reducing waiting times. The value of faster chargers in increasing coverage and reducing the average waiting times is also highlighted in relation to a service provider’s budget constraints.