User QoS Research Articles

Improving Network Service Quality in parts of Sampang City: QoS Evaluation and User Perception of QoE

Improving Network Service Quality in parts of Sampang City: QoS Evaluation and User Perception of QoE

TEE-AODV Trust-based Route Selection and Improving Energy Efficiency in MANET

Mobile ad hoc networks (MANETs) are the future communication infrastructure for all devices that operate without the elaborate design found in wired infrastructure networks. Constructing a routing protocol that helps MANETs to achieve (QoS). It is quite challenging to achieve energy-efficient routing, due to its limited resources and node mobility concerns, which would impact node resource stability and lead to congestion and lower user QoS. A novel Trustworthy Energy Efficient AODV protocol has been proposed for improving path selection and locating trusted stations to maximize QoS. This approach employs location data, transmission speed, and directions to estimate the energy usage and trustworthiness of distinct nodes. The proposed TEE-AODV protocol includes two new control information namely Trust Request packet and Trust Reply. (T-RREP) AODV's packet includes a neighbor list and channel trustworthiness. The number of working neighbors in every path is counted, and this information is used to efficiently choose paths. The TEE-AODV calculates a QoS supporting metric for each detected route to identify a traffic-free path at any moment and determines whether it is a trusted route. The NS-2 simulator has been used to assess the suggested approach in terms of specific characteristics, including energy consumption (EC), (PDR), end-to-end delay (), and number of dead nodes. According to the experimental results, the suggested framework enhances reliable route selection while lowering e nd-to-end latency and improving energy efficiency. The experimental findings indicate that the suggested TEE-AODV protocol achieves high energy efficiency rate in the selection of trustworthy route and minimizing the E D .

Multi-dimensional resource allocation strategy for LEO satellite communication uplinks based on deep reinforcement learning

In the LEO satellite communication system, the resource utilization rate is very low due to the constrained resources on satellites and the non-uniform distribution of traffics. In addition, the rapid movement of LEO satellites leads to complicated and changeable networks, which makes it difficult for traditional resource allocation strategies to improve the resource utilization rate. To solve the above problem, this paper proposes a resource allocation strategy based on deep reinforcement learning. The strategy takes the weighted sum of spectral efficiency, energy efficiency and blocking rate as the optimization objective, and constructs a joint power and channel allocation model. The strategy allocates channels and power according to the number of channels, the number of users and the type of business. In the reward decision mechanism, the maximum reward is obtained by maximizing the increment of the optimization target. However, during the optimization process, the decision always focuses on the optimal allocation for current users, and ignores QoS for new users. To avoid the situation, current service beams are integrated with high- traffic beams, and states of beams are refactored to maximize long-term benefits to improve system performance.Simulation experiments show that in scenarios with a high number of users, the proposed resource allocation strategy reduces the blocking rate by at least 5% compared to reinforcement learning methods, effectively enhancing resource utilization.

Service Function Chain Deployment Algorithm Based on Deep Reinforcement Learning in Space–Air–Ground Integrated Network

SAGIN is formed by the fusion of ground networks and aircraft networks. It breaks through the limitation of communication, which cannot cover the whole world, bringing new opportunities for network communication in remote areas. However, many heterogeneous devices in SAGIN pose significant challenges in terms of end-to-end resource management, and the limited regional heterogeneous resources also threaten the QoS for users. In this regard, this paper proposes a hierarchical resource management structure for SAGIN, named SAGIN-MEC, based on a SDN, NFV, and MEC, aiming to facilitate the systematic management of heterogeneous network resources. Furthermore, to minimize the operator deployment costs while ensuring the QoS, this paper formulates a resource scheduling optimization model tailored to SAGIN scenarios to minimize energy consumption. Additionally, we propose a deployment algorithm, named DRL-G, which is based on heuristics and DRL, aiming to allocate heterogeneous network resources within SAGIN effectively. Experimental results showed that SAGIN-MEC can reduce the end-to-end delay by 6–15 ms compared to the terrestrial edge network, and compared to other algorithms, the DRL-G algorithm can improve the service request reception rate by up to 20%. In terms of energy consumption, it reduces the average energy consumption by 4.4% compared to the PG algorithm.

GREEN COMMUNICATIONS

Green communication is an innovative research area to find radio communication and networking solutions that can significantly improve energy efficiency and resource efficiency of wireless communications without compromising the QoS of users. It contributes to global environment improvement and achieves commercial benefits for telecommunication operators. The main goal of designing green base stations is to save energy and reduce power consumption while guaranteeing user service and coverage and ensuring the base station's capability for evolution. In a wireless network base station, power consumption is the biggest issue. With global warming and energy crises becoming the most compelling environmental challenges, green solutions are a common issue to be handled. Hence, the primary focus of the “Green cellular network” is saving power in base stations to “care for planet and operator’s valet.” we reviewed a few techniques for saving power consumption and improving energy efficiency in base stations. However, many technical challenges for base station architecture redesign, heterogeneous network deployment, radio resource management, etc., need to be addressed for energy-efficient base stations.

QoS driven cost-efficient resource allocation in edge computing: A distributed game theoretic approach

In edge computing, app vendors provide task-offloading services to small intelligent devices with limited resources via edge servers. Users of these devices aim to utilize a better quality of service at a lower cost, and the app vendor tries to maximize their user base. In this paper, we propose an edge computing resource allocation technique to address the critical issue of maintaining a trade-off between user cost and QoS while increasing the number of users served by the app vendor. In the proposed technique, we formulate the problem as an Edge Server Selection Game (ESSGame), where users can optimize individual objective functions. An Edge Server Selection Algorithm (ESSA) is presented by which the system quickly converges to a pure Nash equilibrium (PNE). It is proved that ESSAGame is a potential game with at least one PNE. The quality of the PNE in ESSGame is also determined in terms of the Price of Stability (PoS). Numerical results are also presented for the performance of the proposed technique.

Preference-Aware User Access Control Policy in Internet of Things.

There are multiple types of services in the Internet of Things, and existing access control methods do not consider situations wherein the same types of services have multiple access options. In order to ensure the QoS quality of user access and realize the reasonable utilization of Internet of Things network resources, it is necessary to consider the characteristics of different services to design applicable access control strategies. In this paper, a preference-aware user access control strategy in slices is proposed, which can increase the number of users in the system while balancing slice resource utilization. First, we establish the user QoS model and slice QoS index range according to the delay, rate and reliability requirements, and we select users with multiple access options. Secondly, a user preference matrix is established according to the user QoS requirements and the slice QoS index range. Finally, a preference matrix of the slice is built according to the optimization objective, and access control decisions are made for users through the resource utilization state of the slice and the preference matrix. The verification results show that the proposed strategy not only balances slice resource utilization but also increases the number of users who can access the system.

User Fairness and QoS Aware based Effective Resource Allocation for Downlink NOMA Cellular Systems

User Fairness and QoS Aware based Effective Resource Allocation for Downlink NOMA Cellular Systems

Efficiency in the serverless cloud paradigm: A survey on the reusing and approximation aspects

SummaryServerless computing along with Function‐as‐a‐Service (FaaS) is forming a new computing paradigm that is anticipated to found the next generation of cloud systems. The popularity of this paradigm is due to offering a highly transparent infrastructure that enables user applications to scale in the granularity of their functions. Since these often small and single‐purpose functions are managed on shared computing resources behind the scene, a great potential for computational reuse and approximate computing emerges that if unleashed, can remarkably improve the efficiency of serverless cloud systems—both from the user's QoS and system's (energy consumption and incurred cost) perspectives. Accordingly, the goal of this survey study is to, first, unfold the internal mechanics of serverless computing and, second, explore the scope for efficiency within this paradigm via studying function reuse and approximation approaches and discussing the pros and cons of each one. Next, we outline potential future research directions within this paradigm that can either unlock new use cases or make the paradigm more efficient.

A game-theoretical constructive approach for the multi-objective frequency assignment problem

This study presents the game modeling of the multi-objective frequency assignment problem (FAP) in cellular networks considering two conflicting objectives: interference and separation costs, while respecting separation constraints, and thus improving the QoS for end users. Two types of cooperative games are suggested depending on the TRX and frequency selection strategies each using two players, one for each considered objective. A feasible solution consisting of a frequency assignment plan is built during the constructive process. At each step of the game, a player assigns a frequency to a TRX according to a given strategy. The proposed solution being part of both multi-objective optimization (MOO) and game theory (GT), we used the most important measures in these two fields: the hypervolume (HV) and the Nash value, to evaluate the performance of our solutions to able to compare our results with those of these two communities: MOO and GT. The proposed games achieve a good trade-off between the two considered objectives corroborated by tests on two real-world instances, Denver and Seattle, demonstrating the performance of the proposed approach in solving the problem. Furthermore, the proposed performance evaluation method is innovative and also generic, since it can be adapted to any algorithm based on both GT and MOO, taking into account these two aspects.

Distributed load balancing algorithm considering QoS for next generation multi-RAT HetNets

In this paper, we propose a distributed load balancing algorithm for multi-radio access technology (multi-RAT) heterogeneous networks (HetNets). The random deployment of small cells and the user mobility make the network load distribution uneven, which degrades overall network capacity and the QoS for users. Furthermore, the disparate capabilities of multiple RATs such as the different propagation delays in terrestrial and non-terrestrial RATs affect the latency requirements. To balance the network load in multi-RAT HetNets, we propose a distributed game-theoretic algorithm considering QoS for users. To that end, a cost function is defined to reflect the cell load status, required data rates, and delay constraints. In the proposed algorithm, overloaded cells iteratively minimize user cost in a distributed manner by associating a user with a less-loaded cell based on its cost. We provide analysis to show that minimizing each user cost in the game theory setting achieves a balanced load distribution among cells. We show that the proposed algorithm brings each cell load to a balanced state in a finite number of iterations. Via simulation, we show that the proposed algorithm achieves superior performance in terms of even load distribution, network throughput, and the number of users with satisfactory QoS.

Energy‐efficient task scheduling and resource management in a cloud environment using optimized hybrid technology

AbstractCloud computing is a significant platform emerging for IT enterprises, business applications, and mobile computing. It is a dynamic environment where efficiently and properly allocating resources in such an environment becomes a tedious task. Most existing approaches cannot guarantee energy‐efficient resource management due to the existence of time‐dependent tasks. Cloud resources consume a huge amount of energy, which may reduce the Makespan of the entire network. The process of appropriate task scheduling may satisfy the user's requirement. Reducing energy consumption by satisfying the user's QoS requirements is essential to ensure each user's service level agreement (SLA). Therefore, to achieve an energy‐efficient and unique task scheduling, a technique for order preference by similarity to ideal solution (TOPSIS) based unique ranking (uRank‐TOPSIS) and a Hybrid state‐action‐reward‐state‐action (SARSA), Reinforcement Learning with Black widow Algorithm (HSRLBA) are proposed in this paper. Here, the task scheduling process is carried out using the uRank‐TOPSIS process by producing a set of unique weights and ranking the alternatives. Also, HSRLBA is used to perform resource allocation, which uses the Black widow Algorithm (BWA) to rapidly converge parallel agents in the SARSA model of RL. The Cloudsim simulator is used for experiments. The efficiency of the proposed framework is measured in terms of Makespan, Task Completion Ratio (TCR), total energy consumption, response time and resource utilization. The proposed uRank‐TOPSIS and HSRLBA achieved an energy consumption of 325 KWh, response time of 15.42 s, Makespan of the 1150 s, TCR of 98% and resource utilization of 92%. The proposed framework maximizes TCR and resource utilization and minimizes the Makespan, energy consumption, and response time.

HGPSO: An efficient scientific workflow scheduling in cloud environment using a hybrid optimization algorithm

Cloud technology has raised significant prominence providing a unique market economic approach for resolving large-scale challenges in heterogeneous distributed systems. Through the use of the network, it delivers secure, quick, and profitable information storage with computational capability. Cloud applications are available on-demand to meet a variety of user QoS standards. Due to a large number of users and tasks, it is important to achieve efficient scheduling of tasks submitted by users. One of the most important and difficult non-deterministic polynomial-hard challenges in cloud technology is task scheduling. Therefore, in this paper, an efficient task scheduling approach is developed. To achieve this objective, a hybrid genetic algorithm with particle swarm optimization (HGPSO) algorithm is presented. The scheduling is performed based on the multi-objective function; the function is designed based on three parameters such as makespan, cost, and resource utilization. The proper scheduling system should minimize the makespan and cost while maximizing resource utilization. The proposed algorithm is implemented using WorkflowSim and tested with arbitrary task graphs in a simulated setting. The results obtained reveal that the proposed HGPSO algorithm outperformed all available scheduling algorithms that are compared across a range of experimental setups.

Latency Equalization Policy of End-to-End Network Slicing Based on Reinforcement Learning

Network slicing can provide logically isolated networks on the shared network infrastructure by invoking multiple technologies and administrative domains to fulfill end-to-end (E2E) service level agreements (SLAs). To guarantee the E2E service communication quality in the sliced network, an SLA-based cross-domain orchestration framework is proposed in this paper. The framework includes an E2E cross-domain coordination orchestrator at the upper layer and multiple subordinate domain controllers. Furthermore, we design two latency equalization policies applied to the upper layer orchestrator to divide the latency budget for each lower layer domain. Based on the reinforcement learning approach, Double Deep Q-Network with Prioritized Experience Replay (DDQN-PER) and Pointer Network SFC Mapping (PN-SFC), intra-domain resource allocation/mapping algorithms are designed independently for the lower radio access network (RAN) and core network (CN) domain controllers, respectively. The above algorithms are used to jointly optimize the enhanced mobile broadband (eMBB) users service satisfaction level and maximize the number of E2E accessed users. Simulation results show that our proposed algorithm can effectively guarantee the eMBB users QoS and improve the network capacity.

D2D Communication Network Interference Coordination Scheme Based on Improved Stackelberg

The sudden explosive growth of data in intelligent devices and existing communication networks has brought great challenges to existing communication networks. On the one hand, D2D (device to device) technology greatly improves the utilization of spectrum resources; on the other hand, it improves the communication quality of users. It has become an important part of the future communication network. Aiming at the problem that the existing D2D communication network system has complex user interference, and the communication quality of cellular users is difficult to guarantee, a D2D communication network interference coordination scheme based on improved Stackelberg is proposed. Using resource allocation and power control to solve the interference coordination problem, this paper proposes an improved Stackelberg model based on DQN (deep Q network), establishes the master–slave game between cellular users and multiplexing resource users (D2D users; relay communication users), optimizes the cost parameters in the Stackelberg mode and improves the transmission power and resource allocation scheme of multiplexing resource users. The simulation results show that compared with similar algorithms, the algorithm proposed in this paper has the best performance in guaranteeing the QoS of cellular users in the system and has good interference management capability for D2D communication networks.

Joint Optimization Across Timescales: Resource Placement and Task Dispatching in Edge Clouds

The proliferation of Internet of Things (IoT) data and innovative mobile services has promoted an increasing need for low-latency access to resources such as data and computing services. Mobile edge computing has become an effective computing paradigm to meet the requirement for low-latency access by placing resources and dispatching tasks at the edge clouds near mobile users. The key challenge of such solution is how to efficiently place resources and dispatch tasks in the edge clouds to meet the QoS of mobile users or maximize the platform's utility. In this paper, we study the joint optimization problem of resource placement and task dispatching in mobile edge clouds across multiple timescales under the dynamic status of edge servers. We first propose a two-stage iterative algorithm to solve the joint optimization problem in different timescales, which can handle the varieties among the dynamic of edge resources and/or tasks. We then propose a reinforcement learning (RL) based algorithm which leverages the learning capability of Deep Deterministic Policy Gradient (DDPG) technique to tackle the network variation and dynamic as well. The results from our trace-driven simulations demonstrate that both proposed approaches can effectively place resources and dispatching tasks across two timescales to maximize the total utility of all scheduled tasks.

Optimized multi‐objective Q‐learning with enhanced beetle swarm optimization based scientific workflows scheduling on cloud computing environment

SummaryCloud technology has raised significant prominence providing a unique market economic approach for resolving large‐scale challenges in heterogeneous distributed systems. Through the use of the network, it delivers secure, quick, and profitable information storage with computational capability. Cloud applications are available on‐demand to meet a variety of user QoS standards. Due to the large number of users and tasks, it is important to achieve efficient planning of tasks submitted by users. One of the most important and difficult nondeterministic polynomial‐hard challenges in cloud technology is task scheduling. To overcome the problem, in this article, optimized multi‐objective Q‐learning with EBSO‐based task scheduling on the cloud is proposed. Q‐learning is one of the machine learning algorithms that can solve these types of problems. The proposed approach is divided into two processes: task prioritization and resource selection. In this article, initially, the tasks are prioritized by using the Q‐learning algorithm. After the prioritization, the tasks are assigned to resources by using the enhanced beetle swarm optimization (EBSO) algorithm. To achieve this, the multi‐objective function is designed based on makespan, cost, and resource utilization. The effectiveness of the suggested method is evaluated using a variety of criteria such as makespan, resource utilization, and cost.

Complementary in Time and Space: Optimization on Cost and Performance with Multiple Resources Usage by Server Consolidation in Cloud Data Center

The recent COVID-19 pandemic has accelerated the use of cloud computing. The surge in the number of users presents cloud service providers with severe challenges in managing computing resources. Guaranteeing the QoS of multiple users while reducing the operating cost of the cloud data center (CDC) is a major problem that needs to be solved urgently. To solve this problem, this paper establishes a cost model based on multiple computing resources in CDC, which comprehensively considers the hosts’ energy cost, virtual machine (VM) migration cost, and SLAV penalty cost. To minimize this cost, we design the following solution. We employ a convolutional autoencoder-based filter to preprocess the VM historical workload and use an attention-based RNN method to predict the computing resource usage of the VMs in future periods. Based on the predicted results, we trigger VM migration before the host enters an overloaded state to reduce the occurrence of SLAV. A heuristic algorithm based on the complementary use of multiple resources in space and time is proposed to solve the placement problem. Simulations driven by the VM real workload dataset validate the effectiveness of our proposed method. Compared with the existing methods, our proposed method reduces the energy consumption of the hosts and SLAV and reduces the total cost by 26.1~39.3%.

A novel approach to energy-aware resource management: Toward green NOMA heterogeneous networks

The dense deployment of small cell networks is a key feature of next generation mobile networks aimed at providing the necessary capacity increase. In order to reach an acceptable performance in such ultra-dense networks, real-time resource management is of great importance. Therefore, self-optimization networking is proposed as the only viable solution to increase the networks’ utility. This paper proposed a self-optimizing model to enhance network performance and guarantee the users’ QoS requirements by considering limited resources and using effective user association, carrier scheduling and handover optimization algorithms. In order to maximize the network performance, we applied the smart backhauling technique in order to analyze the signaling to increase the validity of the decision making process. Based on the semantic information extracted from the access layer, the network decision-making center is able to adjust the network parameters and resource allocation effectively. The goal function is defined as maximizing the total energy efficiency by considering the transmission power, energy harvesting capability and the user QoS constraints so that the idle small cells are considered turned off temporarily to boost the power efficiency. Although the optimization problem is non-convex, a quadratic mixed-integer function is solved to obtain a global optimal solution. Since the actual implementation of the real-time algorithm has high computational complexity, two algorithms with different complexity levels are proposed. These algorithms use the carrier matching feature and optimal transmission power for problem-solving. The simulation results prove that, despite the increased computational complexity, effective resource allocation and optimal HO relations made the proposed approach capable to increase performance indices such as network throughput by up to 30%.

SABRE: Swarm-Based Aerial Beamforming Radios: Experimentation and Emulation

We propose a novel distributed beamforming framework for UAVs, called SABRE, wherein airborne transmitters synchronize their operations for data communication with target receivers. SABRE chooses the best-suited subset of transmitters that maximizes user-defined QoS, considering relative distances from receivers, traffic characteristics, cumulative SNR desired at the receiver, and individual SNR estimated for each link. This paper makes three main contributions: (i) It shows how to achieve distributed beamforming in challenging, aerial hovering conditions by accurately synchronizing start-times and eliminating relative clock offsets. (ii) It proposes an algorithm with polynomial complexity that groups transmitters and chooses the receiver, maximizing the number of satisfied receivers in each round. (iii) It experimentally validates the concept of aerial beamforming in a testbed composed of four DJI-M100 UAVs in realistic outdoor environments. We follow this up with at-scale emulation involving beamforming with multiple candidate UAV transmitters in Colosseum, the world’s largest RF emulator. SABRE keeps the overall network frame error rate below 10% with a probability of 0.95 and manifests a 40% improvement in meeting user QoS thresholds over classical resource allocation methods. From a community viewpoint, the beamforming code, UAV interfacing designs, and the Colosseum container will be released publicly, allowing further independent investigations.