Edge Cloud Computing in Telecommunications: Case Studies on Performance Improvement and TCO Saving

In recent years, there is development in the field of computing devices; Internet of Things (IoT) becomes the latest trend. IoT comprises ubiquitous things that are associated with day-to-day life of individuals like smartphones, smart TV, laptops, and now vehicles too. Internet of Vehicles (IoV) has become the latest area of research used to develop applications in the field of traffic management and road safety. A collaborative approach of cloud and edge computing is termed cloud-edge computing. To manage the enormous amount of IoT devices and the coordination among IoT, the cloud and edge concept of computation offloading is required. In the process of computation offloading, tasks are computationally offloaded to the cloud data center that enhances the resource utilization of the cloud server and minimizes the energy utilization for the tasks. This paper represents the literature review related to various computational offloading schemes in cloud-edge computing proposed as part of the study. The resources comprise of related book chapters and research papers from different publishers of international and national reputes. The study is carried out with the analysis of various computation offloading schemes in cloud-edge computing for the Internet of Vehicles. In addition, computing technologies like cloud computing, edge computing, and computation offloading for the Internet of Vehicles (IoV) were also discussed.KeywordsInternet of Things (IoT)Edge computingCloud computingComputation offloadingInternet of Vehicles (IoV)

Today, the edge-cloud computing paradigm starts to gain increasing popularity, aiming to enable short latency, fast decision-making and intelligence at the network edge, especially for industrial applications. The container-based virtualization technology has been put on the roadmap by the industry to implement edge-cloud computing infrastructures. Has the performance of the container-based edge-cloud computing stacks reached industry requirement? In this paper, from the industrial client perspective, we provide a performance evaluation methodology and apply it to the state-of-the-art containerization-based edge-cloud computing infrastructures. The influences of the message sending interval, payload, network bandwidth and concurrent devices on full stack latency are measured, and the processing capability of executing machine learning tasks are benchmarked. The results show that containerization on the edge does not introduce noticeable performance degradation in terms of communication, computing and intelligence capabilities, making it a promising technology for the edge-cloud computing paradigm. However, there is a large room for performance improvement between current implementation of the edge-cloud infrastructure and the demanding requirements anticipated by time-critical industrial applications. We also emphasize and showcase that partitioning of an industrial application into microservices throughout the whole stack can be considered during solution design. The proposed evaluation methodology can be a reference to users of edge-cloud computing as well as developers to get a client perspective overview of system performance.

Total cost of ownership is a concept which strives to analyze and understand the true cost of doing business with a supplier, going well beyond price. This paper briefly explores trends which have increased interest in total cost of ownership analysis. Barriers and benefits of total cost of ownership are also discussed. The main thrust of the article is to present an eight stage framework for total cost of ownership implementation. This framework was developed based on case studies of seven firms which use total cost of ownership in purchasing. The framework includes a number of decisions that firms considering total cost of ownership must face, and a discussion of how these decisions were resolved by some of the case study companies. The paper concludes with an evaluation of the framework, and some suggestions for future total cost of ownership research directions.

The well-established Total Cost of Ownership (TCO) concept has been applied to several durable goods industries, including machinery. However, none of the existing TCO models explicitly focus on such highly energy-intensive equipment as metal melting furnaces. In this paper, an application of the TCO concept to aluminium melting furnaces is explored. A TCO model is created and tested through seven case studies in the aluminium die casting industry. Results indicate that the capital expenditure (CAPEX) incurred by the sample companies accounts for only 3–5% of a furnace TCO. Moreover, the melting technology implemented in the furnace highly impacts its TCO, as both the furnace’s thermal efficiency and melting loss (i.e., the fraction of aluminium burnt during the melting process) significantly affect the costs incurred. Moreover, the sample furnaces’ cost effectiveness clearly relies on scale. This evaluation leads to identify technological and managerial levers to reduce a furnace TCO, e.g., by adopting energy-efficient furnaces and by installing centralized, large-sized furnaces to pursue scale economies.

The evolution of telecommunication networks toward the fifth generation of mobile services (5G), along with the increasing presence of cloud-native applications, and the development of Cloud and Mobile Edge Computing (MEC) paradigms, have opened up new opportunities for the monitoring and management of logistics and transportation. We address the case of distributed streaming platforms with multiple message brokers to develop an optimisation model for the real-time assignment and load balancing of event streaming generated data traffic among Edge Computing facilities. The performance indicator function to be optimised is derived by adopting queuing models with different granularity (packet- and flow-level) that are suitably combined. A specific use case concerning a logistics application is considered and numerical results are provided to show the effectiveness of the optimisation procedure, also in comparison to a “static” assignment proportional to the processing speed of the brokers.

The possibility of electric vehicles to technically replace internal combustion engine vehicles and to deliver economic benefits still mainly depends on the battery size and the charging infrastructure costs as well as on annual mileage (utilizing the lower variable costs of electric vehicles). Current studies on electric vehicles’ total cost of ownership often neglect two important factors that influence the investment decision and operational costs: firstly, the trade-off between battery and charging capacity; secondly the uncertainty in energy consumption. This paper proposes a two-stage stochastic program that minimizes the total cost of ownership of a commercial electric vehicle under uncertain energy consumption and available charging times induced by mobility patterns and outside temperature. The optimization program is solved by sample average approximation based on mobility and temperature scenarios. A hidden Markov model is introduced to predict mobility demand scenarios. Three scenario reduction heuristics are applied to reduce computational effort while keeping a high-quality approximation. The proposed framework is tested in a case study of the home nursing service. The results show the large influence of the uncertain mobility patterns on the optimal solution. In the case study, the total cost of ownership can be reduced by up to 3.9% by including the trade-off between battery and charging capacity. The introduction of variable energy prices can lower energy costs by 31.6% but does not influence the investment decision in this case study. Overall, this study provides valuable insights for real applications to determine the techno-economic optimal electric vehicle and charging infrastructure configuration.

Total Cost of Ownership (TCO) is a key optimization metric for the design of a datacenter. This paper proposes, for the first time, a framework for modeling the implications of DRAM failures and DRAM error protection techniques on the TCO of a datacenter. The framework captures the effects and interactions of several key parameters including: the choice of DRAM protection technique (e.g. single vs dual channel Chipkill), device width (×4 or ×8), memory size, power, FITs for various failure modes, the performance, power and temperature overheads of a protection technique for a given service and mixes of collocated services. The usefulness of the proposed framework is demonstrated through several case studies that identify the best DRAM protection technique in each case, in terms of TCO. Interestingly, our analysis reveals that among the three DRAM protection techniques considered, there is no one that is always superior to all the others. Moreover, each technique is better than the others for some cases. This underlines the importance and the need of the proposed framework for making optimal memory protection data-center design decisions. As part of this work, we analyze and report the performance and power with single channel and dual channel Chipkill on real hardware when running a web search benchmark alone and collocated with benchmarks of varying memory intensity. This analysis reveals that the choice of memory protection can have serious performance and TCO ramifications depending on the memory characteristics of collocated services. Other analysis reveals that, for the datacenter and services assumed in this study, when using Chipkill protection it can be beneficial for TCO to use DRAM with 100× the failure rate of a baseline DRAM as long as the cost per DIMM is at least a dollar less compared to the baseline.

Purpose: The purpose of the article is to determine and compare Total Cost of Ownership (TCO) metrics of buses operating in public transportation system depending on their powertrain. TCO is widely used method supporting decision making in purchases, taking into account all operational and extraordinary costs of delivery, operations, maintenance and liquidation of an asset in its lifecycle. It will provide answer to the question of sustainability of the business in the time horizon reflecting economic life on an asset, in this case – the vehicle. Design/methodology/approach: Main methods used in the paper are critical literature review concerning TCO models and its application for rolling stock, case study research conducted in the Upper Silesian and Zagłębie Metropolis aimed at collection of financial data. Gathered figures were used to develop comparative calculations of TCO for electric (EV) and fuel cell (FCEV) buses. Findings: In course of the research and analyses it was confirmed that the Total Cost of Ownership of the EV is significantly lower, comparing to FCEV, and only political decisions and significant public support of investments in FCEV buses may equalize TCO values for both types of powertrain. Sensitivity of TCO calculated against fuel and electricity prices is very low, and such depreciation of hydrogen fuel is very unlikely to take place. Research limitations/implications: Data gathered for EVs are real life, as carriers have long term experience in their use, whereas the data for FCEVs comes primarily from test drives and road tests carried out by the suppliers and carriers. Practical implications: Development of hydrogen technology still requires massive public financial support, otherwise the costs of bus operations with this type of powertrain is uncompetitive comparing to battery (EV) powertrain. Decarbonisation of the economy is then highly dependent on political priorities, since the businesses may not demonstrate sufficient interest in participation in this process, primarily due to higher costs and lack of attractive incentives. Originality/value: Originality of this approach results from rather infrequent use of this method, comparing to other, i.e. NPV. Use of TCO may facilitate decision making process as it does not require differential approach, comparing to NPV, however, for comparing different possible choices, such comparison of values may be also applied. Keywords: total cost of ownership, economic performance, public transportation, FCEV, EV. Category of the paper: conceptual paper, case study.

Named data networking (NDN) and edge cloud computing (ECC) are emerging technologies that are considered as the most representative technologies for the future Internet. Both technologies are the promising enabler for the future Internet such as fifth generation (5G) and beyond which requires fast information response time. We believe that clear benefits can be achieved from the interplay of NDN and ECC and enables future network technology to be much more flexible, secure and efficient. In this paper, therefore, we integrate NDN with ECC in order to achieve fast information response time. Our framework is based on N-Tier architecture and comprises of three main Tiers. The NDN is located at the Tier1 (Things/end devices) and comprises of all the basic functionalities that connect Internet of Things (IoT) devices with Tier 2 (Edge Computing), where we have deployed our Edge node application. The Tier 2 is then further connected with Tier 3 (Cloud Computing), where our Cloud node application is deployed at multiple hops on the Microsoft Azure Cloud machine located in Virginia, WA, USA. We implement an NDN-based ECC framework and the outcomes are evaluated through testbed and simulations in terms of interest aggregation, round trip time (RTT), service lookup time with single query lookup time and with various traffic loads (load-based lookup time) from the IoT devices. Our measurements show that enabling NDN with edge computing is a promising approach to reduce latency and the backbone network traffic and capable of processing large amounts of data quickly and delivering the results to the users in real time.

Infection prevention and control is essential for high-quality health care. Chlorine is a widely used disinfectant in health care facilities; however, inconsistent availability and quality pose challenges for health staff. On-site chlorine production offers a potential solution. This study evaluates the reliability, change in chlorine availability and quality, and total cost of ownership of the Aqua Research STREAM Disinfectant Generator in primary health care facilities in Ghana. Our cross-sectional study evaluated 18 STREAM devices across 12 primary health care facilities in Ahafo, Central, and Volta Regions. A mean time between failures approach was used to benchmark device reliability. Historical commercial chlorine stock records were compared with STREAM chlorine production volumes. Chlorine quality assessments compared free residual chlorine levels in commercial and STEAM chlorine samples. A 5-year cost model estimated the total cost of STREAM ownership and per-liter cost savings potential. STREAM units remained functional for 94.8% of the study; 67% (n = 12) functioned without failure. Six devices averaged 60.8 days before their first failure (range 16–96 days). No device recorded more than one component failure. STREAM devices eliminated chlorine stock outs, previously occurring 37 days per year on average. In all, 83% of health facilities were found to have degraded commercial chlorine, whereas 100% of STREAM samples met the device’s target concentration (0.5% ± 0.1% mg/L). The 5-year total cost of ownership ranged from $3,700 to $7,900. District hospitals saw a 17% reduction in annual chlorine supply costs with STREAM units, while health centers experienced an increase of 48%. The STREAM shows promise for improving infection prevention and control practices in Ghana’s health system. Study results informed the Ghana Health Service’s decision to purchase 400 STREAM devices (June 2024). Future research should explore operational models that expand chlorine availability and lower chlorine costs in health facilities.

Vehicle depreciation is a key factor in determining the total cost of vehicle ownership and consumer purchase behavior. This report examines how light-duty-vehicle residual values have evolved over time for conventional and advanced vehicle technologies, accounting for important factors such as market segment, size class, and country of assembly. Advancements in electric vehicle technology have led to plug-in vehicles exhibiting depreciation curves similar to those of conventional vehicles. This report compares two methods for determining depreciation trends (snapshot method and time-series method) in order to identify potential impacts on calculating vehicle total cost of ownership given differing data availability.

Enterprise ERP modernization through cloud migration is promoted as a route to higher performance and lower total cost of ownership (TCO), yet enterprises often experience mixed results and hidden cost leakage. To address this problem, the study measured post-migration performance and TCO and modeled drivers of both outcomes. A quantitative, cross-sectional, case-based design synthesized survey data from N = 210 stakeholders across six cloud-migration cases (SaaS 56.2%, hybrid 43.8; phased 67.1%, big-bang 32.9%). Independent variables were cloud migration readiness, implementation quality, governance and risk management maturity, change management effectiveness, and vendor or partner support; dependent variables were ERP performance outcomes and TCO outcomes, decomposed into visible/direct and hidden/indirect components. Analysis began with Cronbach’s alpha to confirm scale consistency (readiness α = 0.89; implementation quality α = 0.91; governance α = 0.87; change management α = 0.90; vendor support α = 0.88; performance α = 0.93; TCO α = 0.92), then applied descriptive statistics, Pearson correlations, and multiple regression, with a Cloud Migration Footprint Index (CMFI) and a Performance and TCO Tradeoff Map. Results showed performance improvement (M = 3.94, SD = 0.62) and TCO improvement (M = 3.62, SD = 0.66), with visible benefits (M = 3.76, SD = 0.71) exceeding hidden cost pressure (M = 3.41, SD = 0.78) by 0.35 (HCR = 0.91). Performance correlated most with implementation quality (r = 0.68) and change management (r = 0.63), and the performance regression explained 61% of variance, F(5,204) = 64.22, p < .001, led by implementation quality (β = 0.34) and change management (β = 0.26). For TCO, the regression explained 47% of variance, F(5,204) = 35.89, p < .001, with governance maturity strongest (β = 0.29) and change management not significant (p = .074). High-footprint cases (CMFI M = 0.67) reported higher performance (M = 4.08) but higher hidden-cost pressure (M = 3.52). Overall, 41.4% were modernization winners, while 22.9% showed performance-first cost leakage, implying that governance and lifecycle controls protect cloud ERP business cases while sustaining gains.

Introduction: This study assesses the economic feasibility of implementing electric buses in Juiz de Fora - MG, considering acquisition, operation, and maintenance costs compared to diesel models. The methodology is based on the Total Cost of Ownership (TCO) analysis, considering factors such as charging infrastructure, energy consumption, government incentives, environmental impact, and public policies related to electromobility.The results indicate that, although electric buses present 24% lower maintenance costs and up to 88.9% reduction in electricity expenses when combined with solar energy, the high initial acquisition cost and the need for structural investments compromise their economic feasibility in the short term. Additionally, implementing charging infrastructure represents a significant financial challenge for public transport operators. However, the analysis shows that alternative financing models, such as battery leasing and public-private partnerships (PPPs), can mitigate some of the financial challenges. Furthermore, more robust public policies, such as government subsidies, tax exemptions, and differentiated electricity tariffs, are essential to enable the energy transition in the public transport sector. Thus, this study reinforces the need for economic and regulatory strategies to facilitate fleet electrification, making urban transport more efficient, sustainable, and economically accessible in the long run. Objective: This article aims to analyze the economic feasibility of implementing electric buses in public transport in Juiz de Fora - MG, considering acquisition, operation, and maintenance costs compared to diesel buses. To achieve this, factors such as energy efficiency, charging infrastructure, government incentive policies, and available financing models will be evaluated. Additionally, the study seeks to identify the challenges and opportunities associated with transitioning to an electric fleet in the city, providing subsidies for decision-making by public managers and transport companies. Theoretical Framework: The study highlights the Total Cost of Ownership (TCO) analysis, financing models for electromobility, public policies for sustainable transport, and environmental impacts of fleet electrification, providing a solid foundation for understanding the research context. Method: The methodology adopted in this research follows a quantitative approach, based on Total Cost of Ownership (TCO) analysis and the economic feasibility assessment of fleet electrification. Data was collected from operational cost surveys, energy consumption analysis, and investments in charging infrastructure, supplemented by information from case studies and secondary sources, such as technical reports and industry regulations. Results and Discussion: The results revealed that, despite up to 24% lower maintenance costs and up to 88.9% electricity savings when using solar energy, the high initial cost of electric buses and charging infrastructure compromises their economic feasibility in the short term. In the discussion section, these results are contextualized within the theoretical framework, emphasizing the importance of government subsidies, tax incentives, and alternative financing models to enable fleet electrification. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the findings can influence sustainable mobility and electromobility practices. These implications include public policy planning for urban transport, the adoption of financing models for electric fleets, and the feasibility of integrating renewable energy into the sector. Additionally, the findings may contribute to strategies that reduce economic barriers and encourage the transition to a more efficient and sustainable public transport system. Originality/Value: This study contributes to the literature by analyzing the economic feasibility of fleet electrification in a medium-sized city in Brazil, a topic still underexplored in local contexts. The innovative approach includes the integration of solar energy as an alternative to reducing operational costs, expanding the debate on sustainability in public transport. The relevance and value of this research are highlighted by its potential impact on public policy formulation, urban planning, and the adoption of more accessible financial models for energy transition in the mobility sector.

The total cost of ownership of durable consumer goods: A conceptual model and an empirical application

The development of technologies and economies of scale has decreased battery cost for electric vehicle powertrains substantially in the last decade. However, electric vehicles are still pricier compared to vehicles with conventional powertrains. Incentives that promote vehicle electrification for cleaner and more sustainable urban mobility should rely on a holistic cost analysis that considers the life cycle costs of different vehicle technologies. This paper presents a method that estimates the direct Total Cost of Ownership (TCO) for different electric vehicle powertrains. The TCO, which include direct and indirect costs, is estimated for three representative urban light duty vehicles: Hybrid Electric Vehicle (HEV), Electric Vehicle (EV), and Plug-in Hybrid Electric Vehicle (PHEV). The TCO includes the present value of: 1) Vehicle retail cost including tax, 2) Lifetime operation cost from the time of vehicle purchase to the time of scrapping, including fueling, maintenance, taxes, insurance, registration and licensing costs, 3) Battery replacement cost, and 4) Time cost. Battery life expectancy is estimated by considering wear data. The results, which are based on vehicle technology characteristics and driver behavior, showed that the TCO for United States, showed that the TCO for EV and PHEV is higher by 24% and 36%, respectively, compared to the HEV. The method provides a better understanding of overall vehicle performance; it facilitates comparisons and supports policies for improved urban mobility.