QoS-aware Resource Allocation Research Articles

QoS-Aware Resource Allocation Scheme for Improved Transmission in 5G Networks with IOT

QoS-Aware Resource Allocation Scheme for Improved Transmission in 5G Networks with IOT

QoS-aware resource allocation method for the internet of things using triplet and heterogeneous earliest finish time algorithms

QoS-aware resource allocation method for the internet of things using triplet and heterogeneous earliest finish time algorithms

Spectrum-Efficient User and Qos-Aware Resource Allocation With Enhanced Uplink Transmission in U-LTE Networks Co-Occurrence With Wi-Fi by CRN

Spectrum-Efficient User and Qos-Aware Resource Allocation With Enhanced Uplink Transmission in U-LTE Networks Co-Occurrence With Wi-Fi by CRN

RETRACTED ARTICLE: A novel approach to QoS-aware resource allocation in NOMA cellular HetNets using multi-layer optimization

RETRACTED ARTICLE: A novel approach to QoS-aware resource allocation in NOMA cellular HetNets using multi-layer optimization

QoS-Aware Resource Allocation with Pilot-Aided Channel Estimation for Heterogeneous Wireless Networks

The deployment of heterogeneous networks (HetNets) is a way to increase the network capacity and release part of the traffic generated by users inside a cell to small-scale wireless networks for service. In this context, the main problem is managing the interference due to the coexistence of small cells and macro cells. In this paper, a QoS-aware Resource Allocation (RA) algorithm jointly working with admission control (AC) over a two-tier HetNet scenario is investigated in the presence of both the pilot-symbols for channel estimation and the channel estimation error. The RA algorithm allows two users, the macro cell user (CU) and small cell user (SU), to simultaneously share the same resource block. Moreover, system performance and fairness are improved by including adaptive power allocation to users over resource blocks. In the framework of RA with proportional rate constraints, a novel algorithm is designed by including the effects of pilot-aided channel estimation. The algorithm is able to distribute the same proportional rate to all CUs and SUs, even in the presence of channel estimation error. Relevant numerical results for the downlink of a two-tier HetNet with pilot-aided channel estimation show that the rate dispersion is driven to zero while the sum-rate is maximized, and the average user rate penalty with respect to a perfect-CSI scenario may rise to 20%.

Interference and QoS-Aware Resource Allocation Considering DAS Behavior for C-RAN Power Minimization

This work aims to minimize the power consumption of cloud virtualization components by addressing the important challenge of designing allocation schemes that cater for both radio and computational resources in a virtualized cloud-based radio environment. Unlike previous efforts, we consider the realistic behavior of radio resource heads (RRHs) associated with one baseband unit (BBU), acting as a DAS. We first formulate the admission control (AC) and the RRH–BBU mapping problem, subject to constraints on user throughput requirements, computational capacity in the BBU pool, and according to the assumption of DAS behavior. As the optimal solution is practically intractable for large-scale dynamic networks, we propose a two-level resource allocation framework, based on two developed algorithms: a first one for AC at the radio level and a second one for RRH–BBU mapping at the computational level. The two algorithms are designed to allow both the levels to consider each other’s constraints and particularities. Results obtained from an extensive simulation-based performance evaluation show the high performance of our proposal in terms of radio-related metrics, number of accepted users, power saving, and energy and spectrum efficiency, compared with three baseline reference schemes.

Scalable and QoS-Aware Resource Allocation to Heterogeneous Traffic Flows in 5G

Networks of new generations are increasingly involved in transporting heterogeneous flows. Indeed, in addition to the usual data and multimedia traffic, the Internet of Things (IoT) smart applications are creating new traffic types and relationships involving billions of active nodes like sensors and actuators. This traffic raises a problem of scale, particularly for resource management and decision-making mechanisms. The present work addresses for the first time the joint problem of mapping heterogeneous flows from multiple users and applications to transport blocks, and then packing these blocks into the rectangular grid of time–frequency resources within a flexible 5G new radio frame. Our solution is based on a quality-of-service-based classification of flows followed by an offline construction of two databases. The first one enumerates all possible configurations of transport blocks and the second enumerates all possible configurations of frames. Thus, the sole online processing that remains to be done is to find the optimal block configurations that satisfy a given request vector. Hence, the resolution of this complex joint mapping and packing problem is reduced to a simple resolution of a linear problem, which consists in finding the best configurations. A thorough numerical study shows that our configuration-based solution can map, within few tens of milliseconds, more than 100 flow connections to transport blocks incurring only 3% of overallocation, and then pack these blocks into the grid leading to an upper bound on the optimality gap as low as 2.8%.

PACTA: A Portfolio Theory Based Approach for QoS Aware Resource Allocation in mmWave Networks

An ultra-dense millimetre wave (mmWave) access network operating on IEEE 802.15.3c based simultaneously operated piconet (SOP) architecture offers an effective way to operate high data-rate IoT-networks while overcoming the challenges of beam-blockage and high propagation loss which characterize these systems. The SOP architecture permits coexistence of multiple mmWave piconets by allowing a piconet controller (PNC) to share its channel time with multiple PNCs, using periodically updated private channel time allocations (p-CTAs). However, due to beam blockage, the p-CTA demands within the piconets change dynamically and often cause a mismatch between the demanded and the assigned p-CTAs leading to suboptimal resource allocation and wastage of network resources. To overcome these problems, this letter proposes a QoS aware risk-sensitive approach for p-CTA assignment. It uses multiple QoS parameters as performance metrics and employs the portfolio allocation theory to dynamically determine the optimal p-CTA distribution. By judiciously allocating p-CTAs over a portfolio of SOPs, the proposed approach minimizes the risk of p-CTA mismatch for each piconet and mitigates the wastage of network resources.

Joint Task Offloading and QoS-Aware Resource Allocation in Fog-Enabled Internet-of-Things Networks

Fog computing is an advanced technique to enhance the Quality of Service (QoS), decrease network latency and energy consumption for Internet-of-Things devices (IDs). In this article, to minimize the overhead of the fog computing network, including the task process delay and energy consumption, while ensuring multiply QoS requirements of different types of IDs, we propose a QoS-aware resource allocation scheme, which jointly considers the association between fog nodes (FNs) and IDs, transmission and computing resource allocation to optimize the offloading decisions while minimizing the network overhead. First, an analytic hierarchy process-based evaluation framework is established to find the preference of QoS parameters and the priority of different types of ID tasks. Second, we introduce a resource block (RB) allocation algorithm to allocate RBs to IDs based on the IDs priority, satisfaction degree, and the quality of RBs. Moreover, a QoS-aware bilateral matching game is introduced to optimize the association between FNs and IDs. Finally, the offloading decisions are based on the previous steps to minimize the network overhead. The simulation results demonstrate that the proposed scheme could efficiently ensure the loading balance of the network, improve the RB utilization, and reduce the network overhead.

Coordinated Caching and QoS-Aware Resource Allocation for Spectrum Sharing

5G cellular networks will heavily rely on the use of techniques that increase the spectral efficiency (SE) to meet the stringent capacity requirements of the envisioned services. To this end, the use of coordinated multi-point (CoMP) as an enabler of underlay spectrum sharing promises substantial SE gains. In this work, we propose novel low-complexity coordinated resource allocation methods based on standard linear precoding schemes that not only maximize the sum-SE and protect the primary users from harmful interference, but they also satisfy the quality-of-service demands of the mobile users. Furthermore, we devise coordinated caching strategies that create joint transmission (JT) opportunities, thus overcoming the mobile backhaul/fronthaul throughput and latency constraints associated with the application of this CoMP variant. Additionally, we present a family of caching schemes that outperform significantly the “de facto standard” least recently used (LRU) technique in terms of the achieved cache hit rate while presenting smaller computational complexity. Numerical simulations indicate that the proposed resource allocation methods perform close to their interference-unconstrained counterparts, illustrate that the considered caching strategies facilitate JT, highlight the performance gains of the presented caching schemes over LRU, and shed light on the effect of various parameters on the performance.

QoS and traffic aware greedy resource allocation in foggy internet of things

QoS and traffic aware greedy resource allocation in foggy internet of things

A swarm intelligence-based quality of service aware resource allocation for clouds

The growing popularity of cloud computing results in very large data centres around the world with vast amount of energy requirements and CO2 emissions. These large sized data centres demand efficient management of resources to conserve energy while satisfying quality of service (QoS) requirements of the end users. In this paper, a QoS-aware resource allocation approach using ant colony optimisation is proposed. The proposed approach is implemented in CloudSim and comprehensive performance analysis shows upto 12% energy saving.

A swarm intelligence-based quality of service aware resource allocation for clouds

The growing popularity of cloud computing results in very large data centres around the world with vast amount of energy requirements and CO2 emissions. These large sized data centres demand efficient management of resources to conserve energy while satisfying quality of service (QoS) requirements of the end users. In this paper, a QoS-aware resource allocation approach using ant colony optimisation is proposed. The proposed approach is implemented in CloudSim and comprehensive performance analysis shows upto 12% energy saving.

QoS and traffic aware greedy resource allocation in foggy Internet of Things

Fog computing is a response to the increasing growth of data generated by the internet of things (IoT) applications developed in the cloud computing platform. It is a solution to materialise the IoT and helps to improve the resource allocation problem. In this paper, we propose an effective method to conquer the resource allocation problem in IoT applications. Our method defines two important objectives: 1) reducing the internet core traffic; 2) serving subscribers with the appropriate quality of service level. The experimental results, which are reported in form of numerous and various scenarios, indicate that the suggested MMGM method can reduce the internet core traffic up to 50%. Also, the minimum deviation from the required or the contracted quality level of service is realised with the help of the Most Graceful Matching (MGM) assignment method.

A QoS-aware resource allocation framework in virtualised cloud environments

In cloud platforms, resource allocation service plays an important role for running user applications efficiently. However, current allocation mechanism in many cloud platforms only provides best-effort service for user's jobs, which means that user's quality-of-service (QoS) requirements can not be well guaranteed. In this paper, we present a novel QoS-aware resource allocation framework, which applies feedback control technique to achieve the goal of fair resource allocation between multiple virtual machine (VM) instances. Theoretical analysis of the proposed control model has proven that it can meet the feasibility and stability requirements. Experimental results conducted on a real-world cloud platform show that the proposed resource allocation framework can significantly improve the effective resource utilisation as well as the overall task execution efficiency. In addition, the proposed framework also shows better robustness when the cloud platform is in presence of highly dynamic workload.

QoS-Aware Resource Allocation for Network Virtualization in an Integrated Train Ground Communication System

Urban rail transit plays an increasingly important role in urbanization processes. Communications-Based Train Control (CBTC) Systems, Passenger Information Systems (PIS), and Closed Circuit Television (CCTV) are key applications of urban rail transit to ensure its normal operation. In existing urban rail transit systems, different applications are deployed with independent train ground communication systems. When the train ground communication systems are built repeatedly, limited wireless spectrum will be wasted, and the maintenance work will also become complicated. In this paper, we design a network virtualization based integrated train ground communication system, in which all the applications in urban rail transit can share the same physical infrastructure. In order to better satisfy the Quality of Service (QoS) requirement of each application, this paper proposes a virtual resource allocation algorithm based on QoS guarantee, base station load balance, and application station fairness. Moreover, with the latest achievement of distributed convex optimization, we exploit a novel distributed optimization method based on alternating direction method of multipliers (ADMM) to solve the virtual resource allocation problem. Extensive simulation results indicate that the QoS of the designed integrated train ground communication system can be improved significantly using the proposed algorithm.

A QoS Aware Resource Allocation Strategy for Mobile Graphics Rendering With Cloud Support

With the rapid development of cloud technology, many services have been transferred from local computers to the cloud-based platform, which decreases the amount of computation done on the former. The local computer could thus be developed in the direction of portability and power saving. Graphics processing, apart from providing user interfaces featuring diversified special effects, is also significant in terms of application programs and play interactions. It is exactly on the basis of the concept of graphics processing that cloud-support rendering is developed, which is aimed to improve the graphics efficiency in mobile devices, via the graphics processing units in the cloud-based platform. The cloud-based platform and the mobile devices are usually connected by the Internet; however, as remote rendering might call for greater network bandwidth, its efficiency will be compromised if the network bandwidth is not stable. Given this limitation, this paper sets out to propose a quality-of-service-aware resource allocation strategy for mobile 3D graphics rendering, which is a hybrid rendering technology combining the client-side graphics processing capabilities with the graphics processing units in the cloud-based platform. When network bandwidth is not stable, the technology is able to assess the current network bandwidth, and dynamically configure the rendered frames on the client side and cloud-based platforms. Even when the client side could not access the network, it would still be possible to carry out the drawing through the graphics processing units on the local computer. Three applications are tested in this research: the technology can increase the frame rate by an average of 44.99% when the bandwidth is 10% greater than the minimum limit, by an average of 44.57% when the bandwidth is less than the minimum limit, by an average of 30.86% when the bandwidth is 10% less than the minimum limit, and by an average of 33.74% when the bandwidth is not stable.

QoS-Aware Resource Allocation for Device-to-Device Communications With Channel Uncertainty

In device-to-device (D2D) communications, channel state information (CSI) is exploited to manage the interference between D2D users and regular cellular users (CUs) and improve system performance. However, obtaining the accurate CSI is usually difficult and causes high overhead, particularly when the links are not connected to the base station (BS), such as the links between regular CUs and D2D receivers (CU-D links). In this paper, we investigate the signaling overhead and performance tradeoff in D2D communications with channel uncertainty. To limit interference to regular CUs, we only allow the resource of a CU to be reused by, at most, one D2D pair. We also assume that only partial CSI of the CU-D links is available at the BS and develop two different strategies to deal with the channel uncertainty, namely, probabilistic and partial feedback schemes. We first derive a probability-based resource-allocation scheme by utilizing channel statistical characteristics to maximize the overall throughput of the CUs and admissible D2D pairs while guaranteeing their quality of service (QoS) in terms of signal-to-interference-plus-noise ratio (SINR) and outage probability, respectively. Then, we propose an efficient feedback scheme to reduce the overhead of CSI feedback while providing near-optimal performance. In addition, we propose a combined scheme to take advantages of both probabilistic and partial feedback schemes. It is shown by simulation that there exists an optimal threshold of the outage probability for probabilistic scheme while the partial feedback scheme is robust to the channel models. Furthermore, the combined scheme outperforms the probabilistic and the partial feedback schemes in terms of overall throughput.

An Appraisal of Meta-Heuristic Resource Allocation Techniques for IaaS Cloud

Background/Objectives: This appraisal investigates the meta-heuristics resource allocation techniques for maximizing financial gains and minimizing the financial expenses of cloud users for IaaS in cloud computing environment. Methods/Statistical Analysis: Overall, a total of ninety-one studies from 1954 to 2015 have been reviewed in this paper. However, twenty-three studies are selected that focused on the meta-heuristic algorithms for their research. The selected papers are categorized into eight groups according to the optimization algorithms used. Findings: From the analytical study, we pointed out the various issues addressed (optimal and dynamically resource allocation, energy and QoS aware resource allocation, VM allocation and placement) through resource allocation meta-heuristics algorithms.Whereas, the improvement shows better performance concerns minimizing the execution and response time, energy consumption and cost while enhancing the efficiency and QoS in this environment. The comparison parameters (makespan 35%,execution time 13%, response time 26%, cost 22%, utilization21% and other 13% including energy, throughput etc) and also the experimental tools (CloudSim 43%, GridSim 5%, Simjava 9%, Matlab 9% and others 13%) used for evaluation of the various techniques for resource allocation in IaaS cloud computing. Applications/Improvements: The comprehensive review and systematic comparison of meta-heuristic resource allocation algorithms described in this appraisal will help researchers to analyze different techniques for future research directions.

QoS-aware dynamic resource allocation for wireless broadband access networks

Abstract The development of the advanced wireless access technologies is focusing on the enhancement of mobile user satisfaction in terms of quality of service (QoS). As the number of mobile users increases, the amount of traffic passing through base station (BS) significantly increases so that the preplanned capacity of downlink or uplink can be exceeded from time to time resulting in the degradation of users’ QoS satisfaction. A way to utilize the downlink (DL) and the uplink (UL) of an orthogonal frequency division multiple access frame efficiently is therefore needed subject to users’ bandwidth demand which changes continuously over the time. In this paper, we propose a QoS-aware dynamic resource allocation (QDRA) scheme which dynamically adjusts the DL/UL ratio to allocate bandwidth for QoS support to users based on the usage statistics. The performance of the proposed QDRA scheme was examined by applying to the WiMAX standard specifications, to show its superiority of maintaining a higher QoS support for various classes of services compared to the pre-reported adaptive method.