Cost Optimization Approach Research Articles

An online cost optimization approach for edge resource provisioning in cloud gaming

Cloud gaming (CG), as an emergent computing paradigm, is revolutionizing the gaming industry. Currently, cloud gaming service providers (CGSPs) begin to integrate edge computing with cloud to provide services, with the aim of maximizing gaming service revenue while considering the costs incurred and the benefits generated. However, it is non-trivial to maximize gaming service revenue, as future requests are not known beforehand, and poor resource provisioning may result in exorbitant costs. In addition, the edge resource provisioning (ERP) problem in CG necessitates a trade-off between cost and inevitable queuing issues in CG systems. To address this issue, we propose ERPOL (ERP Online), a convenient and efficient approach to formulate ERP strategies for CGSPs, without requiring any future information. The performance of ERPOL has been theoretically validated and experimentally evaluated. Experiments driven by real-world traces show that it can achieve significant cost savings. The proposed approach has the potential to transform how CGSPs manage their infrastructure.

Design approach for carbon-neutral and resilient residential communities: Case study of Tabuk region

In this paper, a systematic analysis approach is outlined to design carbon neutral and resilient residential communities in Tabuk region, Saudi Arabia. The design used cost optimization approach to determine the required capacities for on-site power generation and energy storage using renewable energy resources to achieve desired carbon neutrality and energy resiliency thresholds. Moreover, different energy efficiency levels for the housing units are considered to design grid-connected residential communities based on prevalent grid electricity prices and carbon emissions. The analysis indicates that combinations of energy efficiency measures, PV systems, and storage batteries can be utilized to achieve both desired carbon neutrality and energy resiliency levels. The implementation of proven energy efficiency measures can reduce by over 45 % both the annual electricity demands, and the on-site PV capacities needed to reach carbon neutral designs for the residential communities. The cost-effectiveness of carbon-neutral and resilient designs depends significantly on installation costs of PV systems and wind turbines as well as electricity prices purchased and sold back to the grid. The levelized cost of energy for net-zero energy communities can be as low as 0.06 USD/kWh to reach carbon neutrality.

Cost-optimization based target reliabilities for design of structures exposed to fire

Adequacy of structural fire design in uncommon structures is conceptually ensured through cost-benefit analysis where the future costs are balanced against the benefits of safety investment. Cost-benefit analyses, however, are complicated and computationally challenging, and hence impractical for application to individual projects. To address this issue, design guidance proposes target reliability indices for normal design conditions, but no target reliability indices are defined for structural fire design. We revisit the background of the cost-optimization based approach underlying normal design target reliability indices then we extend this approach for the case of fire design of structures. We also propose a modified objective function for cost-optimization which simplifies the evaluation of target reliability indices and reduces the number of assumptions. The optimum safety level is expressed as a function of a new dimensionless variable named “Damage-to-investment indicator” (DII). The cost optimization approach is validated for the target reliability indices for normal design condition. The method is then applied for evaluating DII and the associated optimum reliability indices for fire-exposed structures. Two case studies are presented: (i) a one-way loaded reinforced concrete slab and (ii) a steel column under axial loading. This study thus provides a framework for deriving optimum (target) reliability index for structural fire design which can support the development of rational provisions in codes and standards.

Optimizing nano/micro satellite constellation lifecycle cost based on reliability after acceptance testing

Constellation lifecycle costs are an important metric for constellation designers. Recurring costs of mega constellations in particular are large relative to the non-recurring costs, yet little attention has been given to the reduction of recurring costs. Moreover, few studies exist on the impacts of pre-launch activities such as satellite testing on subsequent operational reliability. We thus study the lifecycle cost impacts of environmental stress screening during acceptance testing to minimize constellation lifecycle costs. Parallels are drawn with the field of electronics testing, and costs arising from burn-in, replacement and availability are considered in our formulation. A single-objective cost minimization problem and a multi-objective cost and unavailability minimization problem are presented to capture different mission applications that require different objectives. The effects of system homogeneity and the use of sparing strategies are investigated. We propose genetic algorithms to solve the optimization problems and give some insight into how burn-in should be carried out for minimizing total costs. The optimization is also applied to a case study of a proposed mission scenario and the constellation lifecycle costs of an optimized burn-in plan are compared to those of a typical ”reliability target” burn-in plan. The optimization resulted in lifecycle cost savings of around 19% while significantly reducing the burn-in duration, showing much promise in the burn-in cost optimization approach.

A digital twin model-based approach to cost optimization of residential community microgrids

A digital twin model-based approach to cost optimization of residential community microgrids

An approach for cost and configuration optimization of horizontal axis wind turbine (HAWT)

In addition to the environmental advantage of wind turbines, the modern wind turbine design is aiming to become more competitive by minimizing the cost of energy (COE). When evaluating any change to the design of a wind turbine, it is critical that the designer evaluates the impact of the design change on the system cost and performance. A typical problem when start a wind turbine design project is to determine the optimum configuration and operation parameters to minimize COE. In this article an optimization approach is adopted using COE as objective function with the rotor size, power rating and rated rotational velocity (RPM) as design variable. The cost of energy model of the National Renewable Energy Laboratory (NREL) is adopted with modifications to include the main components load level effects on the COE. An aerodynamic blade design is developed and used as base for evaluating the rotor performance. The blade is scaled to present different rotor size and operating conditions for each power rating. Analysis tool is developed to consider coupled interactions between power rating and the rotor size. The Blade Element Momentum (BEM) technique is used to evaluate the effect of configuration and operational conditions on the wind turbine load levels and the expected annual energy production (AEP). These are used as inputs for the proposed COE model in addition to main parameters presenting the manufacturing technology and site conditions. The COE model is based on several elements such initial capital cost (ICC), balance of station (BOS), operations and maintenance (O&M), levelized replacement cost (LRC), AEP and design load levels. A pattern search technique is adopted for the optimization and the approach is illustrated by means of a principle test examples for two types of turbines platform one for low wind onshore site and another for high wind offshore site.

Flexibility options in a decarbonising iron and steel industry

The decarbonisation of the iron and steel industry is expected to significantly increase its electricity consumption due to higher levels of electrification and the partial shift to hydrogen as iron reductant. With its batch processes, this industry offers large potential for the application of demand response strategies to achieve electricity cost savings. Previous research has primarily focused on investigating the demand response potential for currently operating manufacturing processes and partly for future low-carbon processes. This study aims to consolidate this knowledge and apply it to a modelling analysis that investigates the demand response potential of two new low-carbon technologies: the hydrogen-based direct reduction of iron with electric arc furnace technology (H2-DRI-EAF) and the blast furnace basic oxygen furnace technology retrofitted with carbon capture (BF-BOF-CCUS). A cost optimisation approach is applied to plant configurations with varying parameters relevant for flexibility, such as electrolyser and storage sizes, and in the context of future electricity prices. Multiple price profiles are selected to encompass uncertainties on the development of the power system. The potential for a H2-DRI-EAF plant is 3–27 times higher than for a BF-BOF-CCUS, with electricity costs savings potentials of 35% and 3%, respectively. The study finds that electricity prices have the most significant impact on the profitability of investing in electrolyser overcapacities, which enable operating costs reduction. Therefore, the profitability of these investments are strongly dependent on future power system configurations.

Application of Saving Matrix Approach for Minimize Distribution Cost and Route Optimization: A Literature Review

The objective of this research is to review the application of the savings method, to obtain optimal distribution routes. Data collection and observation of this literature review were obtained from scientific publications that were published from 2014 to 2022. The main purpose of this review study is to investigate, discuss, analyze, and the existence of various types of distribution routes problems and their solving methods. The application of the research methodology resulted in 20 articles, which aimed to identify the saving algorithms used, over the last decade. As a result, these articles were reviewed and selected using a table for further analysis This paper also presents the results of a quantitative and qualitative literature review and discusses scientific publications that have had a significant impact on logistics companies. Finally, findings show that using the savings matrix method, it is proven that this method generates a better route distribution.

Towards Proactive Risk-Aware Cloud Cost Optimization Leveraging Transient Resources

Low-cost transient resources such as Amazon's Elastic Compute Cloud (EC2) Spot instances can be opportunistically leveraged to reduce the ongoing costs of cloud applications. However, they are susceptible to unilateral revocations by the vendor making them a risky proposition for long-running applications with strict performance requirements. It is challenging to effectively balance the cost savings that transient resources provide with the associated revocation risk which, if realised, can impact application performance. To address this challenge, we propose an approach for risk-aware cloud cost optimization that is inspired by the concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">portfolio diversification</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contract diversification</i> mitigates the revocation risk by procuring the required compute capacity as a mixed portfolio of transient and non-transient resources. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Resource diversification</i> further diversifies the risk by using multiple transient resource types. Using our approach, consumers can leverage contract and resource diversification to proactively (re-)configure their application's resource portfolio to handle workload and resource price fluctuations while minimizing ongoing cost and keeping ongoing revocation risk within tolerable limits. Simulative evaluation using three real-world workload traces and Amazon's EC2 offerings demonstrate that our proposed approach can achieve meaningful cost savings compared to the baseline costs, while significantly reducing the portfolio's exposure to revocation risk.

M-dimension hybrid algorithm for scientific workflow in cloud computing

Cloud computing is emerging with growing popularity in workflow scheduling, especially for scientific workflow. With the emergence cloud computing, can benefit from virtually unlimited resources with minimal hardware investment. Scheduling the submitted Scientific Workflow Application (SWFA) tasks to the available computational resources while optimizing the cost of executing the SWFA is one of the most challenging processes of Workflow Management System (WfMS) in a cloud computing environment. Several cost optimization approaches have been proposed to improve the economic aspect of SWFS in cloud computing. The main goal of the paper is to present a new M-dimension hybrid algorithm, which uses a meta-heuristic algorithm such as Completion Time Driven Hyper-Heuristic (CTDHH), Hybrid Cost-effective Hybrid-Scheduling (HCHS), particle swarm optimization (PSO) and genetic algorithm (GA) and using heuristic algorithms such as the IC-PCPD2 and IC-Loss algorithms. Based on the results of the experimental comparison, the proposed method has proven to yield the most effective performance results for all considered experimental scenarios.

Real-Time Cost Optimization Approach Based on Deep Reinforcement Learning in Software-Defined Security Middle Platform

In today’s business environment, reducing costs is crucial due to the variety of Internet of Things (IoT) devices and security infrastructure. However, applying security measures to complex business scenarios can lead to performance degradation, making it a challenging task. To overcome this problem, we propose a novel algorithm based on deep reinforcement learning (DRL) for optimizing cost in multi-party computation software-defined security middle platforms (MPC-SDSmp) in real-time. To accomplish this, we first integrate fragmented security requirements and infrastructure into the MPC-SDSmp cloud model with privacy protection capabilities to reduce deployment costs. By leveraging the power of DRL and cloud computing technology, we enhance the real-time matching and dynamic adaptation capabilities of the security middle platform (Smp). This enables us to generate a real-time scheduling strategy for Smp resources that meet low-cost goals to reduce operating costs. Our experimental results demonstrate that the proposed method not only reduces the costs by 13.6% but also ensures load balancing, improves the quality-of-service (QoS) satisfaction by 18.7%, and reduces the average response time by 34.2%. Moreover, our solution is highly robust and better suited for real-time environments compared to the existing methods.

A Mathematical Approach for Sound Insulation Characteristics and Cost Optimization of Double-Layer Composite Structures

The compressor is the primary source of noise in a refrigeration system. Most compressors are wrapped with multi-layer sound insulation cotton for noise reduction and sound insulation. We explore the sound insulation law of different polyvinyl chloride thicknesses and non-woven fibers. Polyvinyl chloride with varying thicknesses and non-woven fibers are then combined by bonding to study the sound insulation characteristics of a two-layer composite structure. A sound insulation prediction model is established using the multi-parameter nonlinear regression method. An optimal cost mathematical model is established based on experimental and mathematical methods that can quickly determine the optimal cost scheme for different designs with the same effect.

Cost-Optimized Approach for Pavement Maintenance Planning of Low Volume Rural Roads: A Case Study in Himalayan Region

Cost-Optimized Approach for Pavement Maintenance Planning of Low Volume Rural Roads: A Case Study in Himalayan Region

Combined Economic Emission Dispatch of Microgrid with the Incorporation of Renewable Energy Sources Using Improved Mayfly Optimization Algorithm

Electricity can be provided to small-scale communities like commercial areas and villages through microgrid, one of the small-scale, advanced, and independent electricity systems out of the grid. Microgrid is an appropriate choice for specific purposes reducing emission and generation cost and increasing efficiency, reliability, and the utilization of renewable energy sources. The main objective of this paper is to elucidate the combined economic emission dispatch CEED problem in the microgrid to attain optimal generation cost. A combined cost optimization approach is examined to minimize operational cost and emission levels while satisfying the load demand of the microgrid. With this background, the authors proposed a novel improved mayfly algorithm incorporating Levy flight to resolve the combined economic emission dispatch problem encountered in microgrids. The islanded mode microgrid test system considered in this study comprises thermal power, solar-powered, and wind power generating units. The simulation results were considered for 24 hours with varying power demands. The minimization of total cost and emission is attained for four different scenarios. Optimization results obtained for all scenarios using IMA give a comparatively better reduction in system cost than MA and other optimization algorithms considered revealing the efficacy of IMA taken for comparison with the same data. The proposed IMA algorithm can solve the CEED problem in a grid-connected microgrid.

Structural brain splitting is a hallmark of Granulin-related frontotemporal dementia

Frontotemporal dementia associated with granulin (GRN) mutations presents asymmetric brain atrophy. We applied a Minimum Spanning Tree plus an Efficiency Cost Optimization approach to cortical thickness data in order to test whether graph theory measures could identify global or local impairment of connectivity in the presymptomatic phase of pathology, where other techniques failed in demonstrating changes.We included 52 symptomatic GRN mutation carriers (SC), 161 presymptomatic GRN mutation carriers (PSC) and 341 non-carriers relatives from the Genetic Frontotemporal dementia research Initiative cohort. Group differences of global, nodal and edge connectivity in (Minimum Spanning Tree plus an Efficiency Cost Optimization) graph were tested via Structural Equation Models.Global graph perturbation was selectively impaired in SC compared to non-carriers, with no changes in PSC. At the local level, only SC exhibited perturbation of frontotemporal nodes, but edge connectivity revealed a characteristic pattern of interhemispheric disconnection, involving homologous parietal regions, in PSC.Our results suggest that GRN-related frontotemporal dementia resembles a disconnection syndrome, with interhemispheric disconnection between parietal regions in presymptomatic phases that progresses to frontotemporal areas as symptoms emerge.

A Digital Environment for Empowering the Main Actors of the Deep Energy Retrofitting Value Chain

The H2020 HAPPEN project has defined a new approach to tackle significant unsolved bottlenecks affecting the energy renovation markets of the residential built stock in the Mediterranean area: the MedZEB approach (Mediterranean way to nearly Zero Energy Building), which backbone is the HAPPEN ICT Platform. The paper describes firstly the MedZEB concept as a holistic, flexible and cost-optimal approach especially tailored for the Med area. It then presents the HAPPEN Platform in terms of general architecture, workflows, resources and tools aimed at fostering the spread of the MedZEB approach by integrating technical, financial, environmental and operational aspects. A critical assessment on the state-of-the-art highlights the novelty of the HAPPEN Platform as a comprehensive and effective framework for empowering the evolution of the Med renovation markets, as means for fostering the progressive uptake of deep energy retrofitting in the Med area.

Role of energy efficiency and distributed renewable energy in designing carbon neutral residential buildings and communities: Case study of Saudi Arabia

The impacts of improving energy efficiency of housing units on designing carbon neutral grid-connected residential communities in Saudi Arabia are evaluated in the study summarized in this paper. The analysis utilizes both PV systems and wind turbines as on-site renewable power technologies to achieve the carbon neutrality status for residential communities. Various energy efficiency levels for the housing units that make the residential communities are considered in the analysis. For this study, the design of carbon neutral communities is performed using a cost optimization approach to account for the cost of on-site power generation as well as the grid electricity prices. The analysis indicated that while the carbon neutral residential communities can be achieved in all Saudi regions, their cost-effectiveness depend on the solar and wind resources as well as on the grid electricity prices and the installation costs of solar arrays and wind turbines. In addition, the analysis has indicated the cost-effectiveness of carbon neutral communities can be significantly improved including a reduction of capitals cost by more than 50% when electrical loads for the communities are lowered through energy efficiency actions on individual housing units.

The effect of increased coupling strength between electricity and heating systems in different climate scenarios for Europe

Over the last two centuries, the growth of fossil fuel combustion is the dominant cause of the increasing concentration of CO2 in the atmosphere. Timely mitigation strategies are therefore important and can be achieved through energy system transformation, but time is limited. We investigate into sector coupling as a first step towards a carbon neutral energy economy by imposing additional synergy between the European electricity and heating systems. Because future climate change is unpredictable, we follow three reconstructed concentration pathways (RCP2.6, RCP4.5 and RCP8.5) from the IPCC AR5 covering the years 2000–2100. Three-hourly time series of the national wind and solar capacity factors, electricity and space heat consumption, and coefficient of performance of heat pumps were generated from climate change affected weather data from an ensemble of nine climate models. By using a greenfield cost-optimisation approach, we find that net zero-emission scenarios as, e.g., by the end-century of RCP26 result in extremely electrified energy systems, which almost doubles the system costs compared to historical levels. Carbon intensive pathways as, e.g., the end-century of RCP8.5 result in fossil-fuel dominated energy systems with significant cost reductions. Independent of the climate outcome, the cost of a strongly coupled system is comparable to that of a weakly coupled system. This result states the importance of transforming energy systems towards higher electrification even if strong coupling technologies such as heat pumps and combined heat and power plants are not implemented at large scale.

Robust design of a future 100% renewable european energy supply system with hydrogen infrastructure

Variable renewable energy sources (VRES) will be the cornerstones of future energy supply systems. Nevertheless, their inherent intermittency remains an obstacle to their widespread deployment. Renewably-produced or ‘green’ hydrogen has been suggested as an energy carrier that could account for this in a sustainable manner. In this study, a fully VRES-based European energy system in the year 2050 is designed using an iterative minimal cost-optimization approach that ensures robust supply security across 38 weather-year scenarios (1980–2017). The impact of different power generation locations is factored in by defining exclusive VRES groups within each optimization region. From this, it can be seen that higher numbers of groups in each region offer cheaper electricity generation locations to the optimizer and thus decrease the system's total annual costs. Furthermore, the robust system design and impact of inter-annual variability is identified by iteratively combining the installed capacities of different system designs derived through the application of the 38 historical weather years. The system design outlined here has significantly lower capacities in comparison to the maximum regional capacities obtained in the first round of optimization.

A Genetic Algorithm-Based Metaheuristic Approach for Test Cost Optimization of 3D SIC

A Genetic Algorithm-Based Metaheuristic Approach for Test Cost Optimization of 3D SIC