Long-term System Performance Research Articles

Wireless Powered Mobile Edge Computing: Dynamic Resource Allocation and Throughput Maximization

Wireless powered mobile edge computing (WP-MEC) has been widely studied as a promising technology to liberate wireless terminals from the computation-intensive and energy-consuming tasks. This article considers a WP-MEC system consisting of multiple base stations (BSs) and mobile devices (MDs), where the MDs offload tasks to the BSs for computational resources and the BSs charge the MDs using wireless power transfer (WPT). In practice, each BS and MD are equipped with a task buffer with limited size and a battery with limited capacity. First, we develop a time slotted WP-MEC system with task and energy queuing dynamics to study long-term system performance under time-varying fading channels and stochastic task and energy arrivals. Second, we propose a dynamic throughput maximum (DTM) algorithm based on perturbed Lyapunov optimization to maximize the system throughput under task and energy queue stability constraints, by optimizing the allocation of communication, computation, and energy resources. For the DTM algorithm, we characterize a throughput-backlog trade-off of [ <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(1/V)$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(V)$</tex-math></inline-formula> ] to indicate that the system throughput goes up as the queue backlog increases, where <inline-formula><tex-math notation="LaTeX">$V$</tex-math></inline-formula> is a control parameter between the system throughput and the queue backlog. However, we find that, as <inline-formula><tex-math notation="LaTeX">$V$</tex-math></inline-formula> goes large, the system throughput can be pushed arbitrarily close to the optimum at the cost of linearly increasing queue backlog (i.e., <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(V)$</tex-math></inline-formula> ). To reduce the cost, we further develop an improved dynamic throughput maximum (IDTM) algorithm, and verify that the IDTM algorithm can achieve a trade-off of [ <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(1/V)$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$\mathcal {O}((\log (V))^2)$</tex-math></inline-formula> ] between the system throughput and the queue backlog. The simulation results demonstrate that IDTM retains close system throughput to DTM with only <inline-formula><tex-math notation="LaTeX">$\mathcal {O}((\log (V))^2)$</tex-math></inline-formula> queue backlog.

A Distributed “Black Box” Audit Trail Design Specification for Connected and Automated Vehicle Data and Software Assurance

Automotive software is increasingly complex and critical to safe vehicle operation, and related embedded systems must remain up-to-date to ensure long-term system performance. Update mechanisms and data modification tools introduce opportunities for malicious actors to compromise these cyber-physical systems, and for trusted actors to mistakenly install incompatible software versions. A distributed and stratified audit trail for automotive software and data provenance is proposed to assure users, service providers, and original equipment manufacturers (OEMs) of vehicular software integrity and reliability. The proposed black box architecture is both layered and diffuse, employing distributed hash tables (DHT), a parity system and a public blockchain to provide high resilience, assurance, scalability, and efficiency for automotive and other high-assurance systems.

A Queueing Game Based Management Framework for Fog Computing With Strategic Computing Speed Control

In this paper, a novel management framework for fog computing with strategic computing speed control at fog nodes (FNs) is studied. In the considered model, mobile users declare requests of offloading resource-hungry computation tasks that are dynamically collected at a dedicated edge server (ES). Upon receiving these requests, the ES can decide to either self-process or delegate some workloads to third-party FNs for maximizing the overall management profit. Unlike the existing work, this paper takes into account strategic behaviors of FNs in computing speed control, i.e., each FN can strategically allocate its computing resource to maximize its utility, which consists of the benefit gained from executing offloaded tasks and the cost incurred by dissatisfied (delayed) service to its own subscribed tasks. To jointly address the long-term system performance and FNs&#x2019; strategic interactions, a scheduling mechanism integrating a noncooperative game and a queueing model is formulated. We then investigate two delegation reward settings, i.e., constant and utility-dependent delegation prices, and propose efficient adaptive algorithms to determine the optimal workload distribution at the ES and the computing speed equilibrium among FNs. Both theoretical analyses and simulations are conducted to evaluate the performance of the proposed solutions and demonstrate their superiorities over counterparts.

Long-term performance of a solar water heating system with a novel variable-volume tank

A novel variable-volume tank is lately designed to cater for the varying needs of tank volume. Based on the validated models of collector and the novel tank, a mathematical model for the solar heating system with the novel tank is proposed in this paper. The system is designed for space heating and consists of solar collector, variable-volume water tank and auxiliary heat source. A case of office building using the solar heating system for space heating is studied. The thermal behaviour of the system during a 4-month heating season is simulated and investigated. The studied case shows that the novel tank system has clear advantage in providing 11.6% more useful heat and reducing 19% system heat loss compared to a conventional system. A parametric study is also conducted to analyse the impacts of tank insulation, tank volume and collector size on the long-term system performance. The results show that the long-term performance of the novel tank system is more resilient to a degraded tank insulation or an oversized tank volume, which contributes to a robust heating system to cope with extreme operating conditions.

Deep Reinforcement Learning for Performance-Aware Adaptive Resource Allocation in Mobile Edge Computing

Mobile edge computing (MEC) enables to provide relatively rich computing resources in close proximity to mobile users, which enables resource-limited mobile devices to offload workloads to nearby edge servers, and thereby greatly reducing the processing delay of various mobile applications and the energy consumption of mobile devices. Despite its advantages, when a large number of mobile users simultaneously offloads their computation tasks to an edge server, due to the limited computation and communication resources of edge server, inefficiency resource allocation will not make full use of the limited resource and cause waste of resource, resulting in low system performance (the weighted sum of the number of processed tasks, the number of punished tasks, and the number of dropped tasks). Therefore, it is a challenging problem to effectively allocate the computing and communication resources to multiple mobile users. To cope with this problem, we propose a performance-aware resource allocation (PARA) scheme, the goal of which is to maximize the long-term system performance. More specifically, we first build the multiuser resource allocation architecture for computing workloads and transmitting result data to mobile devices. Then, we formulate the multiuser resource allocation problem as a Markova Decision Process (MDP). To achieve this problem, a performance-aware resource allocation (PARA) scheme based on a deep deterministic policy gradient (DDPG) is adopted to derive optimal resource allocation policy. Finally, extensive simulation experiments demonstrate the effectiveness of the PARA scheme.

Long-term care coverage and its relationship with hospital care: Lessons from Italy on coordination among care-settings.

Long-term care for the elderly is one among the most important challenges for welfare and health care system across the world. Demographic and epidemiological trends are signalling that demand for long-term care will continue increasing in the next future, while public systems investments and efforts to cope with this issue are not enough. One possible strategy could be to reinforce integration between different care settings so to have positive spill over effects. The paper is focussed on Italian long-term care system analysing and assessing its performance at the regional level both in terms of answering citizens' long-term care needs and integrating with hospital care. The study is based on National health care records and regional data concerning long-term care to assess the state of the arts of in-kind services, and on qualitative focus groups with care providers and policy makers to provide interpretation about the Italian long-term care system performance and weaknesses. Results show that, due to a widespread and important lack of supply and inability to answer to citizens' needs, integration between long-term care and hospitals is not working, and substitution effect following investment in long-term care settings is not present. The paper introduces different interpretations of the causes of this phenomenon, suggesting to policy makers and managers the possible solutions to be implemented.

Developing a framework for performance assessment of the public long-term care system in Korea: methodological and policy lessons

BackgroundLimited evidence exists on how to assess long-term care system performance. This study aims to report on the process and results of developing a performance assessment framework to evaluate the long-term care system financed by the public long-term care insurance in South Korea.MethodsThe framework was developed through a six-step approach, including setting the goals and scope of performance assessment in the given policy context, reviewing existing performance frameworks, developing a framework with a wide range of potential indicators, refining the framework through a series of Delphi surveys and expert meetings, examining the feasibility of generated indicators through a pilot test, receiving the comments of stakeholders, and finalising the performance framework.ResultsThe finalised framework has 4 domains – coverage, quality of care, quality of life and system sustainability – and 28 indicators, including 10 core indicators to monitor long-term care system performance. Usability and feasibility along with policy relevance were important criteria in selecting these indicators. The proposed framework can be used to assess the performance of the long-term care system in Korea, and the framework and its methodological approach can be benchmarks for other countries developing their own framework.ConclusionsIt is critical to reconcile and prioritise various stakeholders’ views and information needs as well as to balance methodological rigor with practical usefulness and feasibility in the development and implementation of a long-term care performance monitoring system.

A Deep Reinforcement Learning Based Switch Controller Mapping Strategy in Software Defined Network

Software defined network (SDN) is a promising technology which can reduce network management complexity through the decoupling of the control plane and data plane. Due to large number of switches in the data plane, distributed and multiple controllers are necessary in the control plane for managing the switches. The switch controller mapping strategy for identifying the mapping relationships between the switch and controller is crucial in order to optimize the network performance. Considering the dynamics of the network behavior, it is quite important and challenging to develop models to reflect the network topology dynamics and to propose method for solving the long-term network performance optimization. Inspired by the recent advances in Artificial Intelligence (AI), in this paper, we propose a Deep Reinforcement Learning (DRL) based strategy for solving the switch controller mapping problem. A DRL based mapping strategy is proposed, in which Markov Decision Process (MDP) formulation is devised and Deep $Q$ -network (DQN) is proposed to achieve the maximization of long-term system performance by leveraging network latency, load balancing and system stability. Extensive simulations show that the DQN based algorithm can achieve the best system stability results while maintaining moderate switch controller latency and system equilibrium performance comparing with the optimization which only considers current system performance for switch controller mapping decision, and the optimization approaches which generate mapping decisions purely based on latency or load balancing separately.

Evaluation and augmentation of traffic data including Bluetooth detection system on arterials

Recently, traffic agencies have implemented Bluetooth detection systems (BDS) on arterials to collect traffic data and purchased data from private sector companies such as HERE, INRIX, and TomTom for real-time traffic monitoring and long-term traffic system management and performance evaluation. However, the quality and reliability of the aforementioned two data sources are subject to rigorous evaluation. This study utilized high-resolution GPS trajectory to evaluate data from private sector, HERE and BDS of a principal arterial corridor in Orlando, FL, USA. The results showed that the accuracy and reliability of BDS data are better than private sector data on the study corridor, which might be credited to a better presentation of the bimodal traffic flow pattern on signalized arterials. In order to improve the quality of private sector data, information about bimodal traffic flow was extracted by a finite mixture model from historical BDS data and incorporated with the real-time private sector data by a Bayesian inference framework. The evaluation of the augmented data showed that the augmentation framework is effective for the most part of the corridor except for segments highly influenced by traffic from or to the expressway ramps where evaluation datasets might have some bias.

The electrolyte matters: Stable systems for high rate electrochemical CO2 reduction

Products from electrochemical CO2 reduction have the potential to replace fossil resources in the chemical industry by using renewable energies. However, stable electrolyte systems are an ongoing challenge for long-term electrolysis operation. We report the stability of different electrolyte combinations for two different electrochemical membrane reactor (ecMR) designs. Gas diffusion electrodes (GDEs) with different composition and manufacturing processes were subsequently tested in a stable electrolyte. Electrolytes in cation exchange membrane based ecMRs are only stable, if the anolyte is an acid with only protons as cations. Pure water usage is possible if a zero-gap assembly is used on the anode side. The supporting catholyte can be chosen freely as long as it is not electrochemically active and does not react with CO2. In a stable electrolyte system, pressed and non-pressed Nafion- and PTFE-bonded GDEs with copper and silver as catalyst are compared for current densities up to −300 mA cm−2. The results highlight the importance of the individual optimization of the GDE network in terms of porosity and hydrophobicity. An C2H4 current efficiency of 51% for a copper GDE and a C:H synthesis gas ratio of 2:1 with a silver GDE are measured for −300 mA cm−2. Further, experiments with highly acidic and highly buffered electrolytes, respectively electrolytes with a high ionic strength, show a strong decrease in cathodic overpotential. This work gives a clear guideline for ecMR electrolyte operation for a long-term stable system performance. We highlight the need for a deeper understanding of interfacial phenomena to maximize energetic efficiency.

An integrated critic-actor neural network for reinforcement learning with application of DERs control in grid frequency regulation

As the electronically-interfaced distributed energy resources (DERs) grow rapidly in power grid, power demand satisfaction and frequency regulation are two main challenges in control area. However, it is difficult to model the analysis of a large-scale grid as well as design a stable and optimal control scheme. With the support of DERs, this paper proposes an actor-critic neural network that integrates a distributed reinforcement learning control scheme to compensate frequency regulation of power grid. The short-term performance and stability is improved by a deterministic learning algorithm that is used to obtain the approximation of desired control output. Meanwhile, a long-term strategic utility function is estimated by the integrated actor-critic neural network. The mapping from system state and control output to the strategic utility function value is identified by neural network, as well as utilized in sub-optimal control learning for further improvement of long-term system performance. Theoretical analysis guarantees the stability. Frequency deviation, tie-line power flow, and long-term cost are coincident with uniform ultimate boundness (UUB). In addition, the upper bound of long-term system cost is also reckoned. The effectiveness and advantages of proposed scheme are illustrated in two case studies. The simulation results indicate that the proposed scheme has better performance under certain condition, compared with some actor-critic network control schemes in frequency regulation of power grid.

Cross-Layer Resource Allocation for Multihop V2X Communications

Inspired by the increasingly mature vehicle-to-everything (V2X) communication technology, we propose a multihop V2X downlink transmission system to improve users’ quality of experience (QoE) in hot spots. Specifically, we develop a cross-layer resource allocation algorithm to optimize the long-term system performance while guaranteeing the stability of data queues. Lyapunov optimization is employed to transform the long-term optimization problem into a series of instantaneous subproblems, which involves the joint optimization of rate control, power allocation, and mobile relay selection at each time slot. On one hand, the optimization of rate control is decoupled and carried out independently. On the other hand, a low-complexity pricing-based stable matching algorithm is proposed to solve the joint power allocation and mobile relay selection problem. Finally, simulation results demonstrate that the proposed algorithm can achieve superior performance and simultaneously guarantee queue stability.

Performance Study on an Unglazed Photovoltaic Thermal Collector Running in Sichuan Basin

A performance study with experiments and TRNSYS simulations was conducted for a roll-bond water-type photovoltaic thermal (PVT) collector installed in Chengdu, Sichuan Basin. The measured ambient data and operation data were input to two TRNSYS models and their results were compared. The two numerical models have their own advantages in the simulation of thermal and electrical properties of the PVT collector. By calibrating the models with experimental data, the deviation between the simulated and the measured values was further reduced. In addition, the excellent performance of the roll-bond PVT absorber was also validated by the steady-state test results. However, the long-term system performance is subject to further investigations.

Compact, Automated, Inexpensive, and Field-Deployable Vacuum-Outlet Gas Chromatograph for Trace-Concentration Gas-Phase Organic Compounds.

The identification and quantification of gas-phase organic compounds, such as volatile organic compounds (VOCs), frequently use gas chromatography (GC), which typically requires high-purity compressed gases. We have developed a new instrument for trace-concentration measurements of VOCs and intermediate-volatility compounds of up to 14 carbon atoms in a fully automated (computer-free), independent, low-cost, compact GC-based system for the quantitative analysis of complex mixtures without the need for compressed, high-purity gases or expensive detectors. Through adsorptive analyte preconcentration, vacuum GC, photoionization detectors, and need-based water-vapor control, we enable sensitive and selective measurements with picogram-level limits of detection (i.e., under 15 ppt in a 4 L sample for most compounds). We validate performance against a commercial pressurized GC, including resolving challenging isomers of similar volatility, such as ethylbenzene and m/ p-xylene. We employ vacuum GC across the whole column with filtered air as a carrier gas, producing long-term system stability and performance over a wide range of analytes. Through theory and experiments, we present variations in analyte diffusivities in the mobile phase, analyte elution temperatures, optimal linear velocities, and separation-plate heights with vacuum GC in air at different pressures, and we optimize our instrument to exploit these differences. At 2-6 psia, the molecular diffusion coefficients are 6.4-2.1 times larger and the elution temperatures are 39-92 °C lower than with pressurized GC with helium (at 30 psig) depending on the molecular structure, and we find a wide range of optimal linear velocities (up to 60 cms-1) that are faster with broader tolerances than with pressurized-N2 GC.

Optimal Power Allocations for Non-Orthogonal Multiple Access Over 5G Full/Half-Duplex Relaying Mobile Wireless Networks

This paper investigates the power allocation problems for non-orthogonal multiple access with coordinated direct and relay transmission (CDRT-NOMA), where a base station (BS) communicates with its nearby user directly, while communicating with its far user only through a dedicated relay node (RN). The RN is assumed to operate in either half-duplex relaying (HDR) mode or full-duplex relaying (FDR) mode. Based on instantaneous channel state information (CSI), the dynamic power allocation problems under HDR and FDR schemes are formulated respectively, with the objective of maximizing the minimum user achievable rate. After demonstrating the quasi-concavity of the considered problems, we derive the optimal closed-form power allocation policies under the HDR scheme and the FDR scheme. Then, a hybrid relaying scheme dynamically switching between HDR and FDR schemes is further designed. Moreover, we also study the fixed power allocation problems for the considered CDRT-NOMA systems based on statistical CSI so as to optimize the long-term system performance. The simulations show that our proposed power allocation policies can significantly enhance the performance of CDRT-NOMA systems.

Computation Peer Offloading for Energy-Constrained Mobile Edge Computing in Small-Cell Networks

The (ultra-)dense deployment of small-cell base stations (SBSs) endowed with cloud-like computing functionalities paves the way for pervasive mobile edge computing (MEC), enabling ultra-low latency and location-awareness for a variety of emerging mobile applications and the Internet of Things. To handle spatially uneven computation workloads in the network, cooperation among SBSs via workload peer offloading is essential to avoid large computation latency at overloaded SBSs and provide high quality of service to end users. However, performing effective peer offloading faces many unique challenges in small cell networks due to limited energy resources committed by self-interested SBS owners, uncertainties in the system dynamics and co-provisioning of radio access and computing services. This paper develops a novel online SBS peer offloading framework, called OPEN, by leveraging the Lyapunov technique, in order to maximize the long-term system performance while keeping the energy consumption of SBSs below individual long-term constraints. OPEN works online without requiring information about future system dynamics, yet provides provably near-optimal performance compared to the oracle solution that has the complete future information. In addition, this paper formulates a novel peer offloading game among SBSs, analyzes its equilibrium and efficiency loss in terms of the price of anarchy in order to thoroughly understand SBSs' strategic behaviors, thereby enabling decentralized and autonomous peer offloading decision making. Extensive simulations are carried out and show that peer offloading among SBSs dramatically improves the edge computing performance.

Production quality improvement during manufacturing systems ramp-up

In the current manufacturing context, characterized by short product life-cycles, large product variety, product customization and short innovation cycles, achieving target production quality performance is challenging, especially due to the frequent ramp-up phases the system undergoes along its life-cycle. Available production quality methods focus on high-volume productions and long-term system performance, while they lose effectiveness during the system ramp-up, where instability and unknown disturbances affect the system dynamics. This paper proposes a reference framework for improving production quality performance during the system ramp-up phase. Two strategies for properly dealing with this problem are discussed, consisting in anticipating ramp-up problems during the design phase and performing continuous improvement of production quality performance measures during the system ramp-up. The most effective approaches following these strategies are revised and future research directions in this new research area are drawn.

Non-linear system identification of a latent heat thermal energy storage system

Latent heat storage systems utilising phase change materials have potential to offer several advantages over sensible heat storage, including higher energy storage densities and thermal modulation. Despite these advantages, only a few commercialised products incorporating this technology exist due to several engineering challenges. One problem is how to model this technology in a computationally efficient manner which allows simulating this technology with variable heat sources such as solar thermal and heat pump systems and assess their long-term system performance. In this study, the application of a dynamic neural network for this purpose is investigated, where a Layered Digital Dynamic Neural (LDDN) type network is trained using experimental data to approximate the outlet fluid temperature of a latent heat storage system given inlet fluid temperature and mass flow rate.To acquire the training data necessary for the neural network, an experimental apparatus was designed, built and operated under laboratory conditions. Twenty experiments were conducted to obtain training data where the latent heat storage system was charged to different operating temperatures ranging from 25 to 70 °C. The mass flow rate of the heat exchanger fluid flowing through the heat exchanger was also varied: 0.045 and 0.05 kg/s such that the flow of heat exchanger fluid remained turbulent. These data were then presented to the network for training and optimisation of the network architecture using the Bayesian Regularization training algorithm. It was found, that the LDDN type architecture was suitable to characterise the thermal operational behaviour of a latent heat storage system with good accuracy and with little computational effort once trained. Based on an energy analysis, the neural network response predicted the quantity of energy stored and discharged with approximately 5% and 7% accuracy respectively when presented with data not used during the training process. These results indicate that a dynamic neural network may be a computationally efficient method to model the non-linear operational characteristics of a latent heat storage system. It may therefore be implemented within a simulation environment such as TRNSYS or Simulink.

Five-year performance monitoring of a high-density polyethylene (HDPE) cover system at a reclaimed mine waste rock pile in the Sydney Coalfield (Nova Scotia, Canada).

Cover systems are commonly placed over waste rock piles (WRPs) to limit atmospheric water and oxygen ingress and control the generation and release of acid mine drainage (AMD) to the receiving environment. Although covers containing geomembranes such as high-density polyethylene (HDPE) exhibit the attributes to be highly effective, there are few, if any, published studies monitoring their performance at full-scale WRPs. In 2011, a HDPE cover was installed over the Scotchtown Summit WRP in Nova Scotia, Canada, and extensive field performance monitoring was conducted over the next fiveyears. A range of parameters within the atmosphere, cover, waste rock, groundwater and surface water, were monitored and integrated into a comprehensive hydrogeochemical conceptual model to assess (i) atmospheric ingress to the waste rock, (ii) waste rock acidity and depletion and (iii) evolution of groundwater and surface water quality. Results demonstrate that the cover is effective and meeting site closure objectives. Depletion in oxygen influx resulted in slower sulphide oxidation and AMD generation, while a significant reduction in water influx (i.e. 512 to 50mm/year) resulted in diminished AMD release. Consistent improvements in groundwater quality (decrease in sulphate and metals; increase in pH) beneath and downgradient of the WRP were observed. Protection and/or significant improvement in surface water quality was evident in all surrounding watercourses due to the improved groundwater plume and elimination of contaminated runoff over previously exposed waste rock. A variably saturated flow and contaminant transport model is currently being developed to predict long-term cover system performance.

Towards long-term infrastructure system performance

Infrastructure is presenting some significant global challenges. One issue is that, while it is often seen as performing well, it tends to do so against only against limited terms of reference and short-term objectives. The available literature suggests that, to date, performance has largely been approached from the point of view of a project, or through addressing latent failures arising from specific sources of shock or failure. In contrast, the literature is sparse in which these matters are examined from the perspective of service-led delivery at the system level (the system comprising assets, projects and networks each at different stages of their life cycle and variously affecting one another as they develop, then age). Yet this is arguably the level relevant to, and the reality of, much of the realm of day-to-day public infrastructure management. This apparent gap in infrastructure research has been explored through a series of interviews that looked at different levels in organisations, sectors and countries. These interviews test the presumption that a problem therefore exists, and sought to identify some of the factors involved. In so doing, this research highlights an issue with the alignment between infrastructure governance and day-to-day operations, specifically the feedforward/feedback mechanisms that span the governance/operational interface.