Heterogeneous QoS Requirements 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.

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.

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.

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.

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.

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.

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 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.

A packet scheduling scheme for 4G wireless access systems aiming to maximize revenue for the telecom carriers

Efficient utilization of network resources is a key goal for emerging BWAS. This is a complex goal to achieve due to the heterogeneous service nature and diverse QoS requirements of various applications that BWAS support. Packet scheduling is an important activity that affects BWAS QoS outcomes. This paper proposes a new packet scheduling mechanism that improves QoS in mobile wireless networks which exploit IP as a transport technology for data transfer between BWAS base stations and mobile users at the radio transmission layer. In order to improve BWAS QoS the new packet algorithm makes changes at both the IP and the radio layers. The new algorithm exploits handoff priority scheduling principles and takes into account buffer occupancy and channel conditions. The packet scheduling mechanism also incorporates the concept of fairness. The algorithm offers an opportunity to maximize the carriers' revenue at various traffic situations. Simulation results were compared to well-known algorithms which demonstrated the new packet scheduling algorithm is able to provide a low handoff packet drop rate, low packet forwarding rate, low packet delay, ensure fairness amongst the users of different services and generates higher revenue. Furthermore this research proposes a new and novel measure named "Satisfaction Factor" to measure the efficacy of various scheduling schemes and finally proposes four performance metrics for NodeB's of in Next Generation Wireless Networks.

Scalable Quasi-Dynamic-Provisioning-Based Admission Control Mechanism in Differentiated Service Networks

The architecture in a differentiated services (diffServ) network is based on a simple model that applies a per-class service in the core node of the network. However, because the network behavior is simple, the network structure and provisioning is complicated. If a service provider wants dynamic provisioning or a better bandwidth guarantee, the differentiated services network must use a signaling protocol with QoS parameters or an admission control method. Unfortunately, these methods increase the complexity. To overcome the problems with complexity, we investigated scalable dynamic provisioning for admission control in DiffServ networks. We propose a new scalable qDPM 2 mechanism based on a centralized bandwidth broker and distributed measurement-based admission control and movable boundary bandwidth management to support heterogeneous QoS requirements in DiffServ networks.