Quality Of Service Constraints Research Articles

From Resource Auction to Service Auction: An Auction Paradigm Shift in Wireless Networks

In 5G and beyond, the newly emerging services, such as edge computing/intelligence services, may demand the provision of heterogeneous communications, computing, and storage (CCS) resources on and across network entities multihop apart. In such cases, traditional resource-oriented auction schemes, where buyers place bids on resources, may not be effective in providing end-to-end (E2E) quality-of-service (QoS) guarantees. To overcome these limitations, in this article, we coin the concept of E2E service auction where the auction commodities are E2E services rather than certain resource. Under this framework, buyers simply bid for services with E2E QoS requirements without having to know the inner working (which resources are behind). To guarantee E2E QoS for winning bids while ensuring essential economic properties, E2E service auction requires addressing the joint problem of network optimization and auction design with both economical and QoS constraints. To substantiate the mechanism design, we illustrate how to devise E2E service auctions for edge computing systems under various scenarios. We also identify the research opportunities on E2E service auction mechanism design for other critical use cases, including edge intelligence.

Deep Learning-Aided User Association and Power Control With Renewable Energy Sources

The renewable energy source (RES)-powered small cell base station (SBS) is a promising technology for the next-generation networks because RESs provide sustainable energy without being depleted. In RES-assisted networks, joint optimization of user association (UA) and power control (PC) is required to enhance the sum-rate performance by reducing inter-cell interference between SBSs. This paper tackles the UA and PC optimization for the sum-rate maximization under quality-of-service (QoS) and backhaul constraints. We formulate the problem as mixed-integer non-linear programming, in which UA and PC variables of different time slots are tightly coupled due to the RES energy dynamics model. Hence, designing a dynamic policy-based UA and PC with consideration of the future environments such as the quantity of channel gain and energy harvesting remains a challenge. First, we propose a deep unsupervised learning (DUL)-based UA scheme for a fixed PC variable. To lower the computational complexity and accelerate the convergence of the proposed learning-based optimization, we relax the UA variable, which originally has an extremely high dimension, into a low-dimensional continuous variable inspired by the Lagrangian method. Next, we propose a deep reinforcement learning (DRL)-based PC scheme, in which the stringent QoS and backhaul constraints are considered penalty terms on the reward design. The proposed DRL-based PC scheme facilitates dynamic PC inferring the relationship between the future environment and current PC. Simulation results demonstrate that the proposed scheme enhances the sum-rate by 10%, accommodates 3.3 percent point (%p) more QoS-qualified users, and reduces the computation time by 20 times compared to the conventional optimization-based method.

Rate-Aware Fuzzy Clustering and Stable Sensor Association for Load Balancing in WSNs

In wireless sensor networks (WSNs), topology control is of great significance for energy efficiency. While various clustering algorithms have been proposed to handle the topology control of WSNs, the joint optimization of fairness, Quality of Service (QoS), load balancing, and congestion management has received much less attention. Additionally, the constrained resources and processing capacity of sensor nodes complicate the topology control problem. This article proposes a three-layer framework based on joint rate-aware fuzzy clustering and stable sensor association that considers various factors of sensor energy efficiency. First, a rate-aware fuzzy clustering method is proposed for initial clustering, and energy-aware cluster head (CH) selection is applied considering the total energy consumption and the residual energy of the potential CHs to improve the energy efficiency of WSNs. After that, the payoff functions of the CHs and the cluster members are constructed for sensor association with resource capacity limitations and QoS constraints. The optimal sensor association is obtained to maximize the payoffs. Furthermore, a low-complexity suboptimal sensor association approach is proposed to reduce the complexity with a tolerable performance gap. Finally, a congestion factor is introduced to balance the load of CHs, and the Gale–Shapley (GS) algorithm is used to avoid unstable sensor association caused by the processing capacity constraints. The simulation results show that the proposed algorithms can effectively improve the resource utilization and the energy efficiency of the WSNs.

Reconfigurable Intelligent Surface Aided Non-Orthogonal Unicast-Multicast Secure Transmission

In this letter, a reconfigurable intelligent surface (RIS) is employed to enhance the secure performance of non-orthogonal unicast-multicast (NOUM) transmissions, where the propagation environment can be manipulated to achieve communication security while maintaining the quality of service (QoS) requirements of the NOUM system. Our objective is to maximize the secrecy rate of the unicast users under the QoS constraints on the multicast transmissions, where the multicast users are eavesdropping on the unicast messages. The joint optimization of the non-orthogonal power splitting and the RIS reflection matrix leads to a non-convex problem, to which two alternating optimization (AO) based solutions are developed based on the semi-definite programming (SDP) and penalty-based successive convex approximation (SCA). Our results demonstrate that secure transmission can be guaranteed in the RIS-aided NOUM system. Furthermore, the developed efficient solutions together with the RIS significantly improve the achievable security.

Construction of College English Data Resources and Change of Teacher Positioning Using QoS Constraints

Cultivating students' autonomous learning abilities has become a research hotspot in CE (College English) reform, and the new CE autonomous learning teaching mode with students at the center also requires teachers to change traditional teaching ideas. The traditional teaching mode and teacher orientation have changed dramatically as a result of the new situation of CE teaching reform. The design and implementation of a CE data resource base based on a web database demonstrates that these technologies provide people with greater convenience and practicality. A GA (Genetic Algorithm) based on a QoS (Quality of Service) constraint is proposed on this basis. This algorithm can find a suitable resource node in the computing resources for each subtask that meets the constraint conditions and assign it to the corresponding task. Information-based education has become a powerful driving force for educational revolution, with computer technology and network information technology making English classroom teaching richer, more flexible in form, rich in content, and searchable in data.

An approach based on genetic algorithms and neural networks for QoS-aware IoT services composition

In the Internet of Things (IoT) environment, the services provided by the connected objects are published as services through the web. This allows to machines to interact between them and, makes the IoT services composition possible. However, the vast proliferation of smart object generates services with the same functionalities but different in terms of quality of services (QoS) proprieties. This makes the satisfaction of the user requirements often complex and a NP-hard problem. Indeed, respecting the QoS constraints (user preferences in terms of QoS) is a challenge, due to the high number of candidate services for the composition. This challenge consists of selecting the most appropriate services so that the composite service must meet both the functional and the non-functional requirements of the user. To deal with this challenge, we propose an approach based on Genetic Algorithms (GA) and Neural Networks (NN) for QoS-aware IoT Services Composition in the context of large-scale environments. The combination between GA and NN allows finding the quasi-optimal IoT service composition. This latter is based on global QoS optimization. To reach this objective, the QoS intervals are decomposed into M QoS-levels to engage them into the theoretical composition. Then, the proposed first GA is used to obtain the ideal theoretical composition with an overall QoS optimization. Afterward, the proposed NN is used to eliminate the inappropriate concrete IoT services, and retain only the services having the same categories of the atomic theoretical services composing the ideal theoretical composition. This allows us to optimize the search space as well as the execution time. Finally, we apply the second GA on the concrete services of the retained categories, in order to obtain the IoT service concrete composition with an overall QoS optimization. The simulation results show that the proposed approach has the best composition time, the best Hypervolume indicator and the best compositional optimality compared to SC-FLA, Improved GA and MGA approaches. On another side, it has almost the same performances compared to TS-QCA and, it finds the near-optimal composition in a very short time compared to PSA, which is an optimal approach. Thus, the obtained results show the effectiveness of our approach.

Resource matching algorithm under delay QoS constraint for uplink visible light communication based on supermartingale theory

We propose a supermartingale-based resource matching algorithm to guarantee delay quality of service (QoS) for the visible light communication uplink system. A network queuing model with multiple bursty arrivals is established, and multipacket reception technology is considered to alleviate terminal collisions. We introduce the supermartingale theory to evaluate the delay QoS performance, and a tighter delay-violation probability bound is derived. Relying on the supermartingale analysis, we design a search algorithm to achieve resource matching under a delay QoS constraint. The simulation results prove that the proposed algorithm guarantees delay QoS and saves service resources.

Joint Power and User Grouping Optimization in Cell-Free Massive MIMO Systems

To relieve the stress on channel estimation and decoding complexity in cell-free massive multiple-input multiple-output (MIMO) systems, user grouping problem is investigated in this paper, where access points (APs) based on time-division duplex (TDD) are considered to serve users on different time resources and the same frequency resource. In addition, when quality of service (QoS) requirements are considered, widely-used max-min power control is no longer applicable. We derive the minimum power constraints under diverse QoS requirements considering user grouping. Based on the analysis, we formulate the joint power and user grouping problem under QoS constraints, aiming at minimizing the total transmit power. A generalized benders decomposition (GBD) based algorithm is proposed, where the primal problem and master problem are solved iteratively to approach the optimal solution. Simulation results demonstrate that by user grouping, the number of users served in cell-free MIMO systems can be as much as the number of APs without increasing the complexity of channel estimation and decoding. Furthermore, with the proposed user grouping strategy, the power consumption can be reduced by 2–3 dB compared with the reference user grouping strategy, and by 7 dB compared with the total transmit power without grouping.

Task-RM: A Resource Manager for Energy Reduction in Task-Parallel Applications under Quality of Service Constraints

Improving energy efficiency is an important goal of computer system design. This article focuses on a general model of task-parallel applications under quality-of-service requirements on the completion time. Our technique, called Task-RM , exploits the variance in task execution-times and imbalance between tasks to allocate just enough resources in terms of voltage-frequency and core-allocation so that the application completes before the deadline. Moreover, we provide a solution that can harness additional energy savings with the availability of additional processors. We observe that, for the proposed run-time resource manager to allocate resources, it requires specification of the soft deadlines to the tasks. This is accomplished by analyzing the energy-saving scenarios offline and by providing Task-RM with the performance requirements of the tasks. The evaluation shows an energy saving of 33% compared to race-to-idle and 22% compared to dynamic slack allocation (DSA) with an overhead of less than 1%.

An efficient virtualized network function deployment scheme for service function chain using deep Q‐network

SummaryVirtualized network functions (VNFs) are virtualized network services running on the network functions virtualization infrastructure in order to replace physical hardware. Integrating with software defined networking and network functions virtualization, internet service providers (ISPs) can chain VNFs together, called service function chain (SFC), to provide various network service demands with virtual links. Many studies have proposed for the VNFs deployment under different objectives, for example, energy, delay, quality of service (QoS), and quality of experience (QoE). From the aspects of both ISPs and customers, both energy consumption and QoE are the primary concerns for the purpose of operating expense reduction and users' satisfaction. This paper proposes a QoS/QoE/energy‐aware deep Q‐network (DQN)‐based scheme, called DQN‐QQE for VNFs deployment under the consideration of both energy consumption and QoE requirement with QoS constraints. The reward of the proposed scheme is formulated by the Weber‐Fechner law and the exponential interdependency of QoE and QoS (IQX) hypothesis. Afterwards, we compared the proposed DQN‐QQE to the QoS/QoE‐aware approach DQN‐Q2‐SFC, brute force, and randomness in terms of energy, QoE, error rate, and processing time. The simulation results revealed that DQN‐QQE is more stable and is better than DQN‐Q2‐SFC and randomness approach in terms of energy, QoE, and error rate. The average processing time and energy consumption of the proposed DQN‐QQE was 43% and 11% less than DQN‐Q2‐SFC, respectively. Although brute force approach is better than others in terms of energy, QoE, and error rate, the processing time of brute force approach is nearly 20 times larger than the proposed DQN‐QQE.

Relay Placement Algorithms for IoT Connectivity and Coverage in an Outdoor Heterogeneous Propagation Environment

A vast majority of the Internet of Things (IoT) devices will be connected in a topology where the edge-devices push data to a local gateway, which forwards the data to a cloud for further processing. In sizeable outdoor deployment regions, the edge-devices may experience poor connectivity due to their distant locations and limited transmission power. Repeaters or relays must be placed at a few locations to ensure reliable connectivity to either a gateway or another node in the network. A big challenge in achieving reliable connectivity and coverage is the outdoor propagation environment being heterogeneous. Engineers often deploy networks based on resource-intensive field visits, detailed surveys, measurements, initial test deployments, followed by fine-tuning. For scalability to large scale IoT deployments, automated network planning tools are essential. Such tools should predict connectivity based on the edge-device locations using available Geographical Information System (GIS) data, identify the need for relays/repeaters, and, if needed, suggest the number of relays needed with their locations. Furthermore, such tools should also be extended to suggest the minimum number and locations of base stations that maximise coverage. In this paper, we propose an automated network deployment framework using a black box received signal strength estimation oracle that provides signal strength estimates between candidate pairs of transceiver locations in a heterogeneous deployment region. Our proposed methodology uses either Ant Colony Optimisation (ACO) or Differential Evolution (DE) to identify the number and locations of relays for meeting specified quality of service constraints. We discuss adaptations of our techniques to handle scenarios with multiple gateways. Further, we show the effectiveness of these algorithms to find suitable candidate base station locations to provide coverage in a heterogeneous propagation environment that meets the specified quality of service constraints. We then demonstrate the effectiveness of our algorithms in two deployment regions.

Predictive Hybrid Autoscaling for Containerized Applications

One of the main challenges in deploying container service is providing the scalability to satisfy the service performance and avoid resource wastage. To deal with this challenge, Kubernetes provides two kinds of scaling mode: vertical and horizontal. Several existing autoscaling methods make efforts to improve the default autoscalers in Kubernetes; however, most of these works only focus on one scaling mode at the same time, which results in some limitations. Only horizontal scaling may lead to low utilization of containers due to the fixed amount of resources for each instance, especially in the low-request period. In contrast, only vertical scaling may not ensure the quality of service (QoS) requirements in case of bursty workload due to reaching the upper limit. Besides, it is also necessary to provide burst identification for auto-scalers to guarantee service performance. This paper proposes a hybrid autoscaling method with burst awareness for containerized applications. This new approach considers a combination of both vertical and horizontal abilities to satisfy the QoS requirement while optimizing the utilization of containers. Our proposal uses a predictive method based on the machine learning technique to predict the future demand of the application and combines it with a burst identification module, which makes scaling decisions more effective. Experimental results show an enhancement in maintaining the response time below the QoS constraint whereas remaining high utilization of the deployment compared with existing baseline methods in single scaling mode.

Resource Allocation for D2D Cellular Networks With QoS Constraints: A DC Programming- Based Approach

Device-to-device (D2D) communications provide efficient ways to increase spectrum utilization ratio with reduced power consumption for proximity wireless applications. In this paper, we investigate resource allocation strategies for D2D communications underlaying cellular networks. To be specific, we study the centralized resource allocation algorithm for controlling transmit powers of the underlying D2D pairs in order to maximize the weighted sum-rate while guaranteeing the quality of service (QoS) requirements for both D2D pairs and cellular users (CUs). A novel DC (difference of convex function) programming-based method, called alternative DC (ADC) programming, is introduced to effectively solve this complicated resource allocation problem. Through updating each D2D pair’s power alternatively, the QoS requirement for each D2D pair can be solvable and incorporated systematically to the introduced ADC programming framework. The simulation results show that the introduced ADC programming achieves the highest weighted sum-rate compared to the state-of-the-art methods while ensuring that the QoS of each D2D pair and CU are satisfied.

A Genetic Algorithm-Based Approach for Fluctuating QoS Aware Selection of IoT Services

In recent years, the Internet of Things (IoT) has evolved at an exceptional speed, which enables to interconnect a very large number of heterogeneous, distributed, and mobile devices. This number will exceed 70 billion by 2025 according to Statista. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">^a</xref> with this huge amount of connected objects, the fulfillment of complex IoT applications, which usually requires a combination of several IoT objects, remains a real challenge. Besides, several requirements of Quality of Service (QoS) must be fulfilled, which makes the problem of selecting the appropriate IoT services NP-hard. In the literature, two main techniques for QoS-driven service selection are proposed: global selection characterized by a poor performance in dynamic and distributed huge environments and local selection which considers pre-defined local QoS constraints. Mainly, the existing works consider static QoS. However, in real life scenarios, QoS of IoT services can be fluctuating. To enhance the reliability of IoT applications, it is of paramount importance to consider the fluctuation dimension. In this context, we propose a QoS fluctuation-aware selection approach of IoT services. To do so, we propose a near-to optimal distributed approach that relies on decomposing the global QoS into distinct local constraints that serve as upper/lower bounds for selection while enhancing the reliability of the resulting composition by considering the QoS fluctuation of the candidate IoT services. The approach, we propose is based on a multi-objective evolutionary algorithm (MOEA) to solve the global QoS decomposition problem. Then a local selection using the obtained local constraints is performed in a parallel and distributed way. The performance of the proposed approach is evaluated and validated via experiment series. <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label>^a</label> <uri>https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/</uri> </fn>

Robust secure beamforming algorithm for intelligent reflecting surface-assisted satellite-terrestrial integrated networks

For intelligent reflection surface (IRS) assisted satellite-terrestrial integrated network (STIN), a robust secure beamforming method based on the imperfect channel state information (CSI) of eavesdroppers is proposed. First of all, considering that the satellite uses spot beam technology to serve the earth station, while the ground base station (BS) serves multiple ground users through multicast technology, and in the case of frequency spectrum shared between the two networks, a joint optimization problem is formulated to minimize the total transmit power of STIN while satisfying the quality of service constraints of the BS users and the achievable secrecy rate constraints of the earth station. Secondly, in order to solve this non-convex problem, the upper bound and the lower bound of the eavesdroppers’ output signal-to-noise ratio under the condition of imperfect CSI are derived by employing triangle inequality together with Holder inequality. Then, a joint optimization scheme of robust BF and power control based on semi-positive definite programming and penalty function method is proposed to realize the secure and reliable transmission in STIN. Finally, the simulation results verify the effectiveness and superiority of the proposed algorithm.

Low-Complexity ADMM-Based Algorithm for Robust Multi-Group Multicast Beamforming in Large-Scale Systems

We design an efficient robust multi-group multicast beamforming scheme for massive multiple-input multiple-output (MIMO) systems. Assuming only estimates of the channel covariance matrices are available at the base station with a bounded error, we formulate the robust quality-of-service (QoS) problem, which is to minimize the transmit power subject to the worst-case minimum signal-to-interference-plus-noise-ratio (SINR) guarantee. We directly solve the worst-case SINR problem and convert the robust QoS constraint into a number of non-convex constraints. Based on the recent convergence result of the alternating direction method of multipliers (ADMM) for non-convex problems, we develop an ADMM-based fast algorithm to directly tackle the reformulated non-convex problem with a convergence guarantee. The algorithm contains two layers of ADMM procedures. We design the outer-layer ADMM to decompose the problem into three convex subproblems and solve them alternatingly. We further develop an inner-layer consensus-ADMM-based algorithm to efficiently solve one subproblem. By exploring each subproblem structure and developing the special optimization techniques, we obtain closed-form or semi-closed-form solutions to each subproblem. These results lead to a fast iterative algorithm, which is guaranteed to converge to a stationary point of the original robust QoS problem. Simulation shows that our proposed algorithm provides a favorable performance compared with existing alternative methods with magnitudes of computational complexity reduction.

HPC Data Center Participation in Demand Response: an Adaptive Policy with QoS Assurance.

Demand response programs help stabilize the electricity grid by providing monetary stimulus to consumers if they regulate their power consumption following market requirements. Regulation service, a market that requires participants to regulate power by following a signal updated every few seconds, is particularly beneficial to HPC data centers since data centers are capable of increasing/decreasing power consumption owing to the flexibility in running workloads and the availability of power control mechanisms. While prior works have explored how data centers can provide regulation service reserves, Quality-of-Service (QoS) provisioning for the jobs running at the data centers has not been considered. In this work, we propose an Adaptive policy with QoS Assurance that enables data centers to participate in regulation service programs with assurance on job QoS. Our policy regulates data center power through job scheduling and server power capping. QoS assurance is achieved by applying a queueing-theoretic result to our job scheduling strategy. We evaluate our policy by experiments on a real cluster. Our results demonstrate that the proposed policy reduces electricity costs by 25-56% while providing QoS assurance. On the other hand, the baseline policies cannot meet QoS constraints in 9 of the 14 workload traces tested.

Rate maximization in full‐duplex systems: Gains and complexity of employing adaptive power allocation in downlink and/or uplink

AbstractThe next generations of mobile communications should deal with a set of new use cases, leading to a higher demand for spectral efficiency. Full‐duplex (FD) arises as a promising technology since it has the potential to double the system capacity without additional spectrum. However, FD networks are hindered by self‐interference (SI) and co‐channel interference caused by the reuse of frequency resources. Therefore, radio resource allocation (RRA) in FD networks by means of user pairing, frequency assignment, and power allocation are of utmost importance in order to improve spectral/energy efficiency and ensure quality of service (QoS). Although previous works investigated RRA to improve spectral efficiency, the fundamental question of where adaptive power allocation should be employed in the uplink and/or downlink of FD networks in a cost‐effective way remains unanswered. This article addresses this question in the context of total data rate maximization with QoS constraints where we propose four optimization problems using different power allocation strategies. The branch and bound method is employed to optimally solve the four formulated problems, which were used as upper bound solutions for benchmarking purposes. Moreover, we propose a low computational complexity solution able to adaptively switch the power allocation strategy depending on the scenario. Detailed system‐level simulations indicate that the best node to employ adaptive power allocation (mobile terminal or base station) depends on the cell radius and SI level. Also, our results show that, compared to existing solutions, the proposed low‐complexity is a great and intelligent strategy in terms of system performance and computational complexity considering both perfect and imperfect channel state information.

MQDS: An energy saving scheduling strategy with diverse QoS constraints towards reconfigurable cloud storage systems

Nowadays, cloud storage systems are usually constructed by heterogeneous disks with reconfigurable speed for energy efficiency. Scheduling tasks with diverse QoS (Quality of Service) requirements to disks with different speeds is seldom considered in state-of-the-art mainstream disk scheduling algorithms. This paper proposes a multi-QoS disk scheduling strategy (MQDS), aiming to support energy-saving and diverse QoS constraints towards reconfigurable heterogeneous cloud storage systems. Three algorithms are designed in MQDS: Time Prior Disks Algorithm (TPDS) for time-sensitive tasks, Cost Prior Disks Algorithm (CPDS) for cost-sensitive tasks, and Benefit Function-based Disks Algorithm (BFDS) for tasks with time-varying QoS requirements. CloudSimDisk simulator is extended to evaluate the response time, energy consumption and cost expenditure performance of the proposed algorithms, for comparison with the extended Robin Round Disk scheduling algorithm (E-RRDS). Extensive experimental results demonstrate that the three proposed algorithms outperform E-RRDS. TPDS consumes shortest response time and CPDS has the least cost; and BFDS achieves balance between TPDS and CPDS, which provide more options to users. It is worth mentioning that all the algorithms in MQDS are more energy efficient than E-RRDS. As a whole, the proposed MQDS is energy efficient and able to accommodate diverse QoS constraints well.

Latency-aware service provisioning in survivable multilayer IP-over-elastic optical networks to support multi-class of service transmission

To meet the requirements of the emerging applications, future networks must transmit multiple class of services traffic with diverse quality of service (QoS) requirements. In this paper, we formulate an integer linear programming (ILP) to design multilayer internet protocol (IP) over elastic optical networks (EONs), IP-over-EONs to support multi-class services with different latency and availability requirements. The objective of the ILP is to minimize capital expenditure (CAPEX) and spectrum usage while satisfying all QoS constraints in terms of latency and survivability mechanism. Furthermore, a heuristic algorithm is proposed to solve this problem in large-scale networks. We compare the results of the ILP formulation and the proposed heuristic algorithm in a 5-node network, where our results reveal that the performance gaps between the ILP and heuristic algorithm are on average 2.4% and 9.8% in terms of CAPEX and occupied spectrum, respectively, while the heuristic algorithm has a lower run time. Moreover, we quantitatively analyze the effects of end-to-end latency on the CAPEX of multilayer IP-over-EONs. Our results reveal that serving latency-sensitive demands increases the CAPEX of the network. This is because all-optical connections (i.e., single-hop IP connections) must be established to meet the delay constraints, which in turn leads to a higher CAPEX and occupied spectrum. Furthermore, we compare the effects of using different types of transponders in three practical topologies and evaluate the impacts of the length of the topology links on CAPEX and spectrum usage. Finally, we propose the best transponder types that could be used for each topology based on the gathered results.