Quality Of Service Optimization Problem Research Articles

Exploiting Anytime Algorithms for Collaborative Service Execution in Edge Computing

The diversity and scarcity of resources across devices in heterogeneous computing environments can impact their ability to meet users’ quality-of-service (QoS) requirements, especially in open real-time environments where computational loads are unpredictable. Despite this uncertainty, timely responses to events remain essential to ensure desired performance levels. To address this challenge, this paper introduces collaborative service execution, enabling resource-constrained IoT devices to collaboratively execute services with more powerful neighbors at the edge, thus meeting non-functional requirements that might be unattainable through individual execution. Nodes dynamically form clusters, allocating resources to each service and establishing initial configurations that maximize QoS satisfaction while minimizing global QoS impact. However, the complexity of open real-time environments may hinder the computation of optimal local and global resource allocations within reasonable timeframes. Thus, we reformulate the QoS optimization problem as a heuristic-based anytime optimization problem, capable of interrupting and quickly adapting to environmental changes. Extensive simulations demonstrate that our anytime algorithms rapidly yield satisfactory initial service solutions and effectively optimize the solution quality over iterations, with negligible overhead compared to the benefits gained.

Distributed Resource Optimization With Blockchain Security for Immersive Digital Twin in IIoT

Virtual Reality-embedded Digital Twin (VR-DT) service integrates digital twin with virtual reality (VR) to visualize the digital representation of real-world production, boosting the digital transformation of manufacturing industry in the Industrial Internet of Things (IIoT). Balanced against the advantages of the VR-DT service, its data-driven, computing-intensive and security-sensitive features bring challenges to the current IIoT. Therefore, we propose a blockchain-based distributed resource allocation (BDRA) scheme to improve the average Quality of Service (QoS) of the VR-DT services with regard to service delay and transaction throughput. We formulate the joint optimization of channel assignment, subframe configuration, computing capacity allocation and block size adjustment as a mixed-integer nonlinear programming problem. A Fully-Decentralized Multi-Agent Compound-Action Actor-Critic (FDMA-CA2C) algorithm is developed to solve the QoS optimization problem. Simulation results demonstrate that our proposed scheme can efficiently improve the average QoS of the VR-DT services in a realizable way as compared to existing schemes.

QoS-Aware Robotic Streaming Workflow Allocation in Cloud Robotics Systems

Computation offloading for cloud robotics is receiving considerable attention in academic and industrial communities. However, current solutions face challenges: 1) traditional approaches do not consider the characteristics of networked cloud robotics (NCR) (e.g., heterogeneity and robotic cooperation); 2) they fail to capture the characteristics of tasks in a robotic streaming workflow (RSW) (e.g., strict latency requirements and varying task semantics); and 3) they do not consider quality-of-service (QoS) issues for cloud robotics. In this paper, we address these issues by proposing a QoS-aware RSW allocation algorithm for NCR with joint optimization of latency, energy efficiency, and cost, while considering the characteristics of both RSW and NCR. We first propose a novel framework that combines individual robots, robot clusters, and a remote cloud for computation offloading. We then formulate the joint QoS optimization problem for RSW allocation in NCR while considering latency, energy consumption, and operating cost, and show that the problem is NP-hard. Next, we construct a data flow graph based on the characteristics of RSW and NCR, and transform the RSW allocation problem into a mixed-integer linear programming problem. To obtain a near-optimal solution in reasonable time, we also develop a heuristic algorithm. Experiments comparing our approach with others demonstrate significant performance gains, with improved QoS and reduced execution times.

Real-Time QoS Optimization for Vehicular Edge Computing With Off-Grid Roadside Units

To sustainably provide low-latency communication and edge computing for connected vehicles, a promising solution is using Solar-powered Roadside Units (SRSUs), which consist of small cell base stations and Mobile Edge Computing servers. However, due to the intermittent nature of solar power, SRSUs may suffer from a high risk of power deficiency, which will lead to severe disruption of vehicular edge computing applications. In this paper, we aim to address this challenge of Quality of Service (QoS) loss (i.e., edge computing service outage for vehicle users (VUs)). We formulate a QoS optimization problem for VUs and solve it in two phases: an offline solar energy scheduling phase, and an online user association and SRSU resource allocation phase. We simulate our proposed technique in a dense SRSU network environment with real-world urban vehicular traffic data and solar generation profile. The simulation results show that our proposed approach can significantly reduce QoS loss of vehicular edge computing applications using SRSUs, compared to existing techniques. Further, the results are beneficial to service providers and city planners to identify adequate SRSU configurations for expected solar energy generation and edge computing service demands.

Service and Resource Aware Flow Management Scheme for an SDN-Based Smart Digital Campus Environment

Recently, campuses have been embracing smart digital technologies in order to boost the efficiency of education and creativity. Thus, massive heterogeneous flows are generated as a result of multitude simultaneous access from several heterogeneous devices. This is putting pressure on campuses to make better management of their constrained resources and to ensure the required Quality of Service (QoS). In this paper, we propose a multi-flow management scheme over a software-defined smart digital campus network, named Service and Resource Aware Flow Management (SRAFM). Our approach offers a unified fully-programmable architecture, a distributed end-host-based flow characterization plane, and a centralized software-defined optimization model to efficiently manage heterogeneous flows. Network functionalities, including QoS aware routing and resource allocation optimization, are formulated as a mixed-integer linear programming problem. Due to its NP-hard complexity, we propose an approximation algorithm in a decomposed fashion based on Lagrangian Dual Decomposition (LDD) and subgradient methods to find an optimal solution for flow management. We evaluate our scheme from different aspects, including the number of simultaneous heterogeneous flows, QoS provisioning, characterization impacts, and network scalability. Compared to the well-known benchmarks in QoS aware routing and optimization problem, SWAY and LARAC, our simulation results conducted with a large number of flows over a small-scale network show promising performance. The proposed scheme significantly improves the cost reduction by 51% as compared to LARAC, the end-to-end delay by 21% and 34%, the bandwidth availability by 27% and 36%, and the QoS violation by 11% and 29% as compared to SWAY and LARAC, respectively.

Handoff Performance Improvements in an Integrated Train-Ground Communication System Based on Wireless Network Virtualization

In existing urban rail transit systems, the train-ground communication system for different subsystems is deployed independently. Investing and constructing the communication infrastructures repeatedly not only wastes substantial social resources, but it also is difficult to maintain all these infrastructures. In this paper, we propose an integrated train-ground communication system based on wireless network virtualization for urban rail transit systems. In order to improve the communication-based train control (CBTC) subsystem performance during handoff, we propose a novel handoff scheme to support handoff between virtual networks. The application-layer quality-of-service (QoS) parameters of the CBTC, passenger information system, and closed circuit television subsystems are used as the performance measures in the handoff design. We then formulate the QoS optimization problem in the proposed integrated train-ground communication system as an approximate dynamic programming (ADP) problem. The extensive simulation results show that the proposed integrated train-ground communication system QoS can be improved substantially with our ADP-based optimization model.

Transmission-Rate-Adaption Assisted Energy-efficient Resource Allocation with QoS Support in WBANs

With the advances in wearable sensors and wireless technologies, wireless body area network (WBAN) has become a promising network to provide health applications, such as ubiquitous e-Health services and real-time health monitoring. In these WBANs, quality of service (QoS) requirements, including packet loss rate, throughput, and delay, need to be guaranteed for providing reliable and real-time data transmission even under highly dynamic environment due to human mobility and postures. Meanwhile, energy efficiency is another key factor to consider for increasing the network lifetime in such a resource-constrained network. In this paper, a transmission-rate-adaption assisted and energy-efficient resource allocation scheme is proposed in which both constraints of QoS metrics and the characteristics of dynamic links are considered. Specifically, a QoS optimization problem is formulated to optimize the transmission power and time slots for each sensor, which minimizes energy consumption subjected to the QoS constraints. Due to the dynamic link characteristics, the link quality may become poor, and then the QoS requirements of normal packets and emergency packets cannot be satisfied. Besides, the emergency packets should receive more attentions with a high priority and high QoS requirements. To guarantee the QoS requirements when the link is very poor, a transmission rate adaption policy is proposed to carefully adjust the transmission rate at each sensor to assist the QoS optimization problem, and a priority-based retransmission strategy is designed to apply the retransmission strategy to further improve the performances of the emergency packets with a high priority. Numerical results demonstrate the effectiveness of the QoS optimization problem, the proposed transmission rate adaption policy, and the priority-based retransmission strategy.

Two-Stage Subspace Constrained Precoding in Massive MIMO Cellular Systems

We propose a subspace constrained precoding scheme that exploits the spatial channel correlation structure in massive MIMO cellular systems to fully unleash the tremendous gain provided by massive antenna array with reduced channel state information (CSI) signaling overhead. The MIMO precoder at each base station (BS) is partitioned into an inner precoder and a Transmit (Tx) subspace control matrix. The inner precoder is adaptive to the local CSI at each BS for spatial multiplexing gain. The Tx subspace control is adaptive to the channel statistics for inter-cell interference mitigation and Quality of Service (QoS) optimization. Specifically, the Tx subspace control is formulated as a QoS optimization problem which involves an SINR chance constraint where the probability of each user's SINR not satisfying a service requirement must not exceed a given outage probability. Such chance constraint cannot be handled by the existing methods due to the two stage precoding structure. To tackle this, we propose a bi-convex approximation approach, which consists of three key ingredients: random matrix theory, chance constrained optimization and semidefinite relaxation. Then we propose an efficient algorithm to find the optimal solution of the resulting bi-convex approximation problem. Simulations show that the proposed design has significant gain over various baselines.

Dynamic QoS Optimization Architecture for Cloud-based DDDAS

An emerging class of Dynamic Data Driven application systems heavily depends on cloud and Big Data. We refer to this class of DDDAS as cloud-based DDDAS. Despite the growing interest in marrying DDDAS with the cloud, there is a general lack for architectural frameworks explicating the cloud requirements, which can support cloud-based DDDAS. Given the unpredictable, dynamic and on-demand nature of the cloud, cloud-based DDDAS requires novel approaches for dynamic Quality of Service (QoS) optimization. This is important for providing timely and reliable predictions and for ensuring higher dependability in the solution, as it would be unrealistic to assume that optimal QoS can be achieved at design time. We propose a decentralized architectural style for cloud-based DDDAS, where dynamic QoS optimization is in the heart of the symbiotic adaptation. The architecture leverages on the classical DDDAS primitives to reach a refined decentralized style suited for the dynamic requirements of the cloud. We formulate the QoS optimization problem as a dynamic multi-objective optimization problem. We use a scenario to exemplify and evaluate the effectiveness of the style.

Application layer QoS optimization for multimedia transmission over cognitive radio networks

In cognitive radio (CR) networks, the perceived reduction of application layer quality of service (QoS), such as multimedia distortion, by secondary users may impede the success of CR technologies. Most previous work in CR networks ignores application layer QoS. In this paper we take an integrated design approach to jointly optimize multimedia intra refreshing rate, an application layer parameter, together with access strategy, and spectrum sensing for multimedia transmission in a CR system with time varying wireless channels. Primary network usage and channel gain are modeled as a finite state Markov process. With channel sensing and channel state information errors, the system state cannot be directly observed. We formulate the QoS optimization problem as a partially observable Markov decision process (POMDP). A low complexity dynamic programming framework is presented to obtain the optimal policy. Simulation results show the effectiveness of the proposed scheme.

Web Service Configuration Under Multiple Quality-of-Service Attributes

With the popularity of Internet technology, Web services are becoming the most promising paradigm for distributed computing. However, when a single Web service fails to meet service requestor's multiple function needs, web services need to be dynamically configured together to form a web service composition. Since there may be many configurations providing identical functionality with different quality-of-service (QoS), a choice needs to be made according to users' functional and nonfunctional requirements. In this paper, we formulate a Web service functional configuration problem by using Petri nets. The graph structure and algebraic properties of the model are analyzed in detail to show that a basis solution of a state-shift equation of the Petri net model corresponds to a realizable configuration process. This result is later used to formulate the multiple attribute QoS optimization problem to a linear programming problem. Finally, a case study is performed to show that the proposed analysis result can be effectively applied in practice.

A global optimization approach for three layers of computational grid stack

This paper presents a global optimization approach for three layers of grid stack. In grid environment, the Quality of Service (QoS) needs to be supported at every layer of the grid architecture. However, less attention has been paid to incorporating QoS at multiple layers of the grid architecture. The primary objective of most existing scheduling mechanisms is to improve QoS at single grid layer, while the QoS optimization at multiple grid layers is seldom considered. In the paper, we consider the requirements of fabric layer, collective layer and application layer at the same time, and propose a global optimization approach for three layers of grid stack. The paper deals with global optimization as optimization decomposition. The global optimization for three layers of grid stack can be decomposed into three sub-problems at different grid layers: resource allocation at the fabric layer, service integration at the collective layer, and application QoS optimization problem at the application layer. A distributed iterative algorithm for three-layer optimization is proposed. The convergence of the iterative algorithm is proved. The simulations are conducted to test Three-Layer Optimization Algorithm.