Heterogeneous QoS Research Articles

Cooperative UAV Resource Allocation and Task Offloading in Hierarchical Aerial Computing Systems: A MAPPO-Based Approach

This paper investigates a hierarchical aerial computing system, where both HAPs and UAVs provision computation services for ground devices (GDs). Different from the existing works which ignored UAV task offloading to HAPs and suffered long transmission delay between HAPs and GDs, in our system, UAVs are responsible for collecting the tasks generated by GDs. Considering limited resources and constrained coverage, UAVs need to cooperatively allocate their resources (including spectrum, caching, and computing) to GDs. After collecting GD tasks, UAVs are allowed to offload part of these tasks to the HAP, in order to minimize task processing delay and then better satisfy GD delay requirement. Our objective is to maximize the amount of computed tasks while satisfying tasks heterogeneous QoS requirements through the joint optimization of UAV resource allocation and task offloading. To this end, a joint optimization problem is first formulated as a partially observable Markov decision process (POMDP) under the constraints of available resources, UAV energy, and collision avoidance. Then, we design a multi-agent proximal policy optimization (MAPPO)-based algorithm to solve the optimization problem. By introducing the centralized training with decentralized execution framework, UAVs acting as agents can cooperatively make decisions on ground devices association, resource allocation, and task offloading according to their local observations. In addition, state normalization and action mask are also adopted to improve training efficiency. Experiment results verify the efficiency of the proposed algorithm and the system performance is also analyzed by the numerical results.

ViTaLS—A Novel Link-Layer Scheduling Framework for Tactile Internet Over Wi-Fi

The pioneering field of Tactile Internet (TI) will enable the transfer of human skills over long distances through haptic feedback. Realizing this demands a roundtrip latency of sub-5 ms. In this work, we investigate the capability of Wi-Fi 6 and existing TI scheduling/multiplexing schemes in meeting this stringent latency constraint. Taking the concrete example of the state-of-the-art Video-Haptic multiplexer (VH-multiplexer), we highlight the pitfalls of relying on the existing Wi-Fi 6 systems for TI communication. To circumvent this, we propose Video-Tactile Latency Scheduler (ViTaLS) -a novel link layer framework for tuning the video-tactile frame transmissions to suit their heterogeneous QoS requirements. We present a mathematical model to characterize the packet transmission duration of Vi-TaLS. Using a custom simulator, we validate our model and measure the objective performance improvement of ViTaLS over VH-multiplexer. We also present ViTaLS-optimal -a variant of ViTaLS, for further reducing the tactile latency. Objectively, we show that ViTaLS-optimal yields a latency improvement of up to 82%. Based on experiments conducted on a real TI testbed, we subjectively demonstrate that ViTaLS-optimal outperforms the VH-multiplexer.

Decision making framework for heterogeneous QoS information: an application to cloud service selection

Decision making framework for heterogeneous QoS information: an application to cloud service selection

Distributed Subcarrier Assignment and Discrete Power Allocation for Multi-UAV Millimeter-Wave Cooperative OFDMA Networks With Heterogeneous QoS Consideration

Distributed Subcarrier Assignment and Discrete Power Allocation for Multi-UAV Millimeter-Wave Cooperative OFDMA Networks With Heterogeneous QoS Consideration

Resource Pricing and Demand Allocation for Revenue Maximization in IaaS Clouds: A Market-Oriented Approach

With more users outsourcing their applications to the cloud, resource pricing becomes an important issue for IaaS cloud management. Jointly considering her own bidding budget and the price of cloud resources, each user is self-motivated to purchase cloud resources according to her resource demand which maximizes her own utility. Meanwhile, the cloud service provider (CSP) regulates the price of cloud resources with a certain profitability objective achieved. With an elaborate resource pricing strategy, the goals from users and the CSP are balanced and respectively satisfied to some extent. This article provides an insight into the market-oriented cloud pricing strategy. In specific, we propose an auction market in the IaaS cloud, where multiple users with heterogeneous bidding budgets and QoS requirements subscribe cloud resources according to their resource demands. The resource pricing and demand allocation scheme targeting revenue maximization also satisfies essential properties including budget feasibility, incentive compatibility and envy-freeness. To attack the NP-hardness and non-convexity of revenue maximization problem, we design a price-incentive resource auction mechanism namely RARM, which preserves an ( 1+α) approximation ratio on revenue maximization. Finally, we evaluate our RARM mechanism based on the real-world dataset to certify the efficacy of our proposed approach.

Application-aware resource allocation and data management for MEC-assisted IoT service providers

To support the growing demand for data-intensive and low-latency IoT applications, Multi-Access Edge Computing (MEC) is emerging as an effective edge-computing approach enabling the execution of delay-sensitive processing tasks close to end-users. However, most of the existing works on resource allocation and service placement in MEC systems overlook the unique characteristics of new IoT use cases. For instance, many IoT applications require the periodic execution of computing tasks on real-time data streams that originate from devices dispersed over a wide area. Thus, users requesting IoT services are typically distant from the data producers. To fill this gap, the contribution of this work is two-fold. Firstly, we propose a MEC-compliant architectural solution to support the operation of multiple IoT service providers over a common MEC platform deployment, which enables the steering and shaping of IoT data transport within the platform. Secondly, we model the problem of service placement and data management in the proposed MEC-based solution taking into account the dependencies at the data level between IoT services and sensing resources. Our model also considers that caches can be deployed on MEC hosts, to allow the sharing of the same data between different IoT services with overlapping geographical scope, and provides support for IoT services with heterogeneous QoS requirements, such as different frequencies of periodic task execution. Due to the complexity of the optimisation problem, a heuristic algorithm is proposed using linear relaxation and rounding techniques. Extensive simulation results demonstrate the efficiency of the proposed approach, especially when traffic demands generated by the service requests are not uniform.

Hybrid Multiple Access and Service-Oriented Resource Allocation for Heterogeneous QoS Provisioning in Machine Type Communications

Hybrid Multiple Access and Service-Oriented Resource Allocation for Heterogeneous QoS Provisioning in Machine Type Communications

A Heterogeneous QoS-Based Cloud Service Selection Approach Using Entropy Weight and GRA-ELECTRE III

With the development of cloud computing, more and more resources are provided in the form of cloud services. Then how to select suitable cloud service for users without professional knowledge has become an important issue. Existing cloud service selection models are usually considered as QoS-aware evaluation focused models. In practice, the QoS attributes have problems like subjectivity, vagueness, and uncertainty, and a range of formats are involved to describe QoS attributes. Therefore, it is necessary to consider the heterogeneous formats of QoS attributes in cloud service selection process. The aim of this paper is to develop a novel cloud service selection approach using entropy weight and GRA-ELECTRE III that can handle heterogeneous QoS attributes simultaneously. In the proposed approach, heterogeneous QoS attributes are handled simultaneously by being transformed into intuitionistic fuzzy numbers; the relative weights of QoS attributes are calculated objectively by the extended entropy measure method under intuitionistic fuzzy environment; and cloud services are evaluated by GRA-ELECTRE III integrated method under intuitionistic fuzzy environment. Experimental results show that the proposed approach has good stability and discrimination in dealing with heterogeneous data and can effectively avoid compensation between attributes.

Edge-centric Resource Allocation for Heterogenous IoT Applications using a CoAP-based Broker

Edge/fog computing is an emerging paradigm that exploits computing and management at the edge of the network to improve QoS of traditional cloud-centric IoT applications. In large-scale IoT deployments, multiple applications are expected to access the same constrained resource (e.g., a sensor) with heterogeneous QoS requirements. However, IoT applications may be unable to adapt their access patterns to cope with bandwidth limitations. To address this issue, we propose a fog-based IoT broker that determines suitable notification rates to access IoT resources while maximising applications' QoS and avoiding network congestion. We first formalise the rate selection as an optimisation problem, and then we propose a practical algorithm that leverages transmission reliability to dynamically select notification periods. We developed a CoAP-based prototype and validated our proposal through simulations and experiments in a real IoT testbed. Our broker provides higher QoS satisfaction, throughput and energy efficiency compared to a conventional CoAP proxy.

Latency and Reliability-Aware Workload Assignment in IoT Networks With Mobile Edge Clouds

Along with the dramatic increase in the number of IoT devices, different IoT services with heterogeneous QoS requirements are evolving with the aim of making the current society smarter and more connected. In order to deliver such services to the end users, the network infrastructure has to accommodate the tremendous workload generated by the smart devices and their heterogeneous and stringent latency and reliability requirements. This would only be possible with the emergence of ultra reliable low latency communications (uRLLC) promised by 5G. Mobile Edge Computing (MEC) has emerged as an enabling technology to help with the realization of such services by bringing the remote computing and storage capabilities of the cloud closer to the users. However, integrating uRLLC with MEC would require the network operator to efficiently map the generated workloads to MEC nodes along with resolving the trade-off between the latency and reliability requirements. Thus, we study in this paper the problem of Workload Assignment (WA) and formulate it as a Mixed Integer Program (MIP) to decide on the assignment of the workloads to the available MEC nodes. Due to the complexity of the WA problem, we decompose the problem into two subproblems; Reliability Aware Candidate Selection (RACS) and Latency Aware Workload Assignment (LAWA-MIP). We evaluate the performance of the decomposition approach and propose a more scalable approach; Tabu meta-heuristic (WA-Tabu). Through extensive numerical evaluation, we analyze the performance and show the efficiency of our proposed approach under different system parameters.

Energy-Efficient Resource Allocation for OFDMA Heterogeneous Networks

We proposed several energy-efficient resource allocation algorithms for the downlink of an orthogonal frequency-division-multiple-access (OFDMA) based femtocell heterogeneous networks (HetNets). Heterogeneous QoS and fairness in rate are investigated in the proposed resource allocation problem. A dense deployment of femtocells in the coverage area of a central macrocell is considered and energy usage of both femtocell and macrocell users are optimized simultaneously. We aim to maximize the weighted sum of the individual energy efficiencies (WSEEMax) and the network energy efficiency (NEEMax) while satisfying the following: (1) minimum throughput for delay-sensitive (DS) users, (2) fairness constraint for delay-tolerant (DT) users, (3) required constraints of OFDMA systems. The problem is formulated in three different forms: mixed 0–1 integer programming formulation, time-sharing formulation and sparsity-inducing formulation. The proposed resource block (RB) and power optimization problems are combinatorial and highly non-convex due to the fractional form of the objective function, the integer constraint of OFDMA RBs and non-affine fairness. We adopt the successive convex approximation (SCA) approach and transform the problems into a sequence of convex subproblems. With the proposed algorithms, we show that the overall joint RB and power allocation schemes converge to suboptimal solutions. Numerical examples confirm the merits of the proposed algorithms.

A Self-aware Resource Management Framework for Heterogeneous Multicore SoCs with Diverse QoS Targets

In modern heterogeneous MPSoCs, the management of shared memory resources is crucial in delivering end-to-end QoS. Previous frameworks have either focused on singular QoS targets or the allocation of partitionable resources among CPU applications at relatively slow timescales. However, heterogeneous MPSoCs typically require instant response from the memory system where most resources cannot be partitioned. Moreover, the health of different cores in a heterogeneous MPSoC is often measured by diverse performance objectives. In this work, we propose the Self-Aware Resource Allocation framework for heterogeneous MPSoCs. Priority-based adaptation allows cores to use different target performance and self-monitor their own intrinsic health. In response, the system allocates non-partitionable resources based on priorities. The proposed framework meets a diverse range of QoS demands from heterogeneous cores. Moreover, we present a runtime scheme to configure priority-based adaptation so that distinct sensitivities of heterogeneous QoS targets with respect to memory allocation can be accommodated. In addition, the priority of best-effort cores can also be regulated.

Effective BigData-Space Service Selection over Trust and Heterogeneous QoS Preferences

As the number of Cloud services is growing at a tremendous speed, there is an increasing number of service providers offering similar functionalities. Selecting services with user desired non-functional properties (NFPs) becomes of significant importance but triggers a number of Big Data related research issues. First, the selection decision should deal with a large volume of service NFPs data. Second, service selection needs to reflect diverse user preferences, including both qualitative and quantitative ones. Third, the uncertainty of the network and service load leads to high variability in NFPs. Fourth, as the trust values of service NFPs are collected via historic user's feedbacks,it brings the veracity dimension to the NFPs of services. Fifth, multiple and sometimes conflicting decision objectives for optimal service selection should be balanced. An effective service selection mechanism is in demand that can tackle all the above Big Data challenges in an integrated way to handle the highly diverse QoS with significant variability along with the trust related issues giving rise to data veracity. Existing investigations focus on either users’ QoS preferences or their trust concerns but fail to provide a systematic solution to integrate both criteria in the selection process. In this paper, we tackle heterogeneous preference- and trust-based service selection by developing a novel multi-objective optimization approach to make trade-off decision between service's trust value and user's QoS preference to rank candidate Cloud services based on their match degrees with users’ requirements. We conduct extensive experiments to evaluate the effectiveness and efficiency of the proposed approach.

QoS-Aware Energy and Jitter-Efficient Downlink Predictive Scheduler for Heterogeneous Traffic LTE Networks

Energy-efficient communications have become one fundamental aspect for today's cutting-edge wireless technologies due to its valuable impact on the environment. In this paper, we augment our earlier study for the user equipment's (UE) energy efficiency (EE) in the long-term evolution (LTE) downlink by looking at real-time heterogeneous traffic QoS requirements. In particular, we utilize the previously proposed cloud radio access network (C-RAN) and ray tracing (RT)-based scheduling model to optimize both of the EE and the packet delay jitter for real-time applications with fixed packet delay budget subject to other traffic types requirements. Using the utility-based scheduling approach, we formulate the resource allocation problem as a weighted sum binary integer programming (BIP) problem. Due to the inherent complexity of the problem formulation which hinders finding its solution directly, four heuristic algorithms are proposed to solve the optimization problem. Numerical simulations are conducted on three different traffic types each belonging to one of the popular QoS classes; best-effort class, rate, and delay-constrained classes. The obtained results demonstrate a substantial improvement in the system's performance achieved by our proposed schemes compared to other existing schemes.

Elastic Power-Aware Resource Provisioning of Heterogeneous Workloads in Self-Sustainable Datacenters

While major Cloud service operators have taken various initiatives to operate their datacenters with renewable energy partially or completely, it is challenging to effectively utilize the renewable energy since its generation depends on dynamic natural conditions. In this paper, we propose and develop an elastic power-aware resource provisioning approach (ePower) for heterogeneous workloads in self-sustainable datacenters that completely rely on renewable energy. We aim to maximize the system goodput and control the system power consumption with respect to green power supply. ePower takes challenges and advantages of dynamic power supply, heterogeneous workload characteristics and QoS requirements, and automatically optimizes elastic resource allocations to workloads. The core of ePower design is a novel power-aware simulated annealing algorithm with fuzzy performance modeling for the efficient search of an optimal resource allocation. We have implemented ePower in a university cloud testbed hosting Gridmix2 and RUBiS benchmark applications. We utilize real weather data traces to simulate the green power generation and supply in the experiments. Experimental results demonstrate ePower can achieve near-to-optimal system performance while being resilient to dynamic power availability. It outperforms a representative resource provisioning approach for heterogeneous workloads by at least 24% in improving system goodput and 35 percent in reducing QoS violations.

Joint Power Control and Time Switching for SWIPT Systems With Heterogeneous QoS Requirements

The problem of joint power control and time switching is studied for a simultaneous wireless information and power transferring system that supports wireless nodes (WNs) with heterogeneous traffic. Using the stochastic optimization theory, a dynamic algorithm, which can trade average power consumption for WNs’ delay, is proposed to allocate the transmission power and time switching factor jointly. A control parameter is defined in the dynamic algorithm to facilitate the tradeoff.

Toward a Completely Softwareized Optical Network [Invited

There is no denying the tremendous success of current optical network technologies. However, the existing optical network infrastructures are unable to support independent evolution and innovation of physical, transport, and network layer functionalities, protocols, and services. Nor can they support the increasing bandwidth demands of evolving applications and their heterogeneous resource usage and QoS requirements. This paper addresses this problem by proposing a completely softwareized optical network infrastructure and the key technology enablers, including an open and programmable optical white box, optical transport and switching technology abstractions, and a compute-aware optical network virtualization mechanism. The technology enablers are evaluated by experimental demonstration and simulation.

Heterogeneous QoS Provision Schemes over WLANs with the Equivalent Spectral Bandwidth Distribution

A call admission control (CAC) protocol is proposed for integrated service WLAN networks in this paper. A popular admission protocol is to admit the incoming calls into the WLAN networks whenever it is available so as to take advantage of low cost and large bandwidth of the WLANs. However, the popular protocol can’t guarantee the QoS of the calls. In order to guarantee the QoS, the proposed protocol maps the characteristics (such as bit rate, bit error rate) of different services into the equivalent spectral bandwidth, and dynamically distributes the equivalent bandwidth to different calls. As a result, the QoS of the calls can be guaranteed, and the spectral efficiency has been developed.

Heterogeneous statistical QoS provisioning over 5G mobile wireless networks

As a critical step towards the next new era of mobile wireless networks, recently 5G mobile wireless networks have received significant research attention and efforts from both academia and industry. The 5G mobile wireless networks are expected to provide different delay-bounded QoS guarantees for a wide spectrum of services, applications, and users with extremely diverse requirements. Since the time-sensitive services in 5G multimedia wireless networks may vary dramatically in both a large range from milliseconds to a few seconds and diversity from uniform/ constant delay-bound to different/variable delay-bound guarantees among different wireless links, the delay-bound QoS requirements for different types of services promote the newly emerging heterogeneous statistical delay-bounded QoS provisioning over 5G mobile wireless networks, which, however, imposes many new challenging issues not encountered before in 4G wireless networks. To overcome these new challenges, in this article we propose a novel heterogeneous statistical QoS provisioning architecture for 5G mobile wireless networks. First, we develop and analyze the new heterogeneous statistical QoS system model by applying and extending the effective capacity theory. Then, through the wireless coupling channels, we apply our proposed heterogeneous statistical QoS architecture to efficiently implement the following powerful 5G-candidate wireless techniques: 1) device-to-device networks; 2) full-duplex networks; and 3) cognitive radio networks, respectively, for providing heterogeneous statistical delay-bounded QoS guarantees. Finally, using the simulation experiments we show that our proposed architecture and schemes significantly outperform the existing traditional statistical delay-bounded QoS provisioning schemes in terms of satisfying the heterogeneous delay-bounded QoS requirements while maximizing the aggregate system throughput over 5G mobile wireless networks.

Applied-Information Technology in Improving Bloom Filter-Based Discovery Protocol for DDS Middlewarein Consideration of QoS

The basic RTPS implementation for discovery protocol need to waste a lot of memory space and network traffic. SDPBloommake use of Bloom Filter to reduce the memory requirement and network load in thediscovery process,but it can not deal withthe scenarios that many Endpoints with common Topics but with heterogeneous andincompatible QoS settings. To solve the problem,this paper propose a improved discoveryprotocol called SDPBloom_QoS. SDPBloom_QoS give a method that can put QoSinformation into Bloom Filter. Then provide both analytical andexperimental studies. Results show that SDPBloom_QoSis more powerful than SDPBloom.