Performance Of Infrastructure Research Articles

기업이 인지한 기업지원이 위기 관리정도와 기업 성과에 미치는 영향

This study aimed to verify whether the need for business support to survive in a rapidly changing external environment affects corporate performance and whether the degree of crisis management mediates this relationship. Accordingly, a study was conducted on companies in Jeju, specifically in the intelligent tourism service and green energy solution industries, which are promoting the digital transformation of traditional sectors in response to the COVID-19 pandemic. Therefore, policy support for companies was categorized into four groups: Infrastructure, R&D, Technology support, and Business support. The impact of each support program on corporate performance was then examined through the degree of crisis management within the company. The following is a summary of the research results.
 First, the relationship between business support and crisis management was verified, showing that the impact varied depending on the characteristics of the business support program. In the short term, it was analyzed that companies require direct support programs to overcome crises. Second, the relationship between business support and corporate performance was found to be statistically insignificant. Third, in the relationship between crisis management and corporate performance, it was confirmed that managing financial and material resources in the short term can improve corporate performance. Finally, in the relationship between perceived corporate support and corporate performance, as well as the relationship between infrastructure, R&D, technology support, and corporate performance, it was found that resource management has a complete mediating effect.
 The implications of this study confirm that business support in response to uncertain business environments affects crisis management simultaneously but does not immediately impact corporate performance. As business support may have a negative effect on short-term corporate performance from the perspective of support agencies, it is necessary to establish a direction for support by dividing short-term and long-term outcomes derived through business support.

Community Participation in the Public Decision Process: Realizing Better Governance in Public Administration

Citizen participation in public decision-making processes has become a key element in efforts to achieve better governance in public administration. This journal article explains the importance of community participation in forming policies and managing public resources. By involving the public in the decision-making process, the government can produce more effective and sustainable policies. Community participation takes various forms, from public consultation to deeper collaboration in policy formation. In the context of public administration, public participation can increase government legitimacy and accountability. This article also describes the challenges and barriers that may arise in enabling effective community participation, including issues related to access, transparency, and lack of community skills or knowledge. In addition, this article highlights the concrete benefits of public participation, such as improving the quality of policies, reducing social conflict, and increasing the efficiency of using public resources. Through case study examples and empirical evidence, this article illustrates how public participation has succeeded in improving government performance in various sectors, including education, health, environment and infrastructure. In conclusion, public participation is an important element in improving governance in public administration. Effective participation allows the government to make policies that are wiser and adapt to the needs of society. By paying attention to existing challenges, the government can create an environment that supports broader public participation, which will ultimately result in better and more responsive government.

Evaluation of the contribution of the automated accounting information system to the managerial decision-making process in the commercial bank of Bangladesh

Accounting Information Systems (AIS) are becoming widespread among Bangladeshi banks. The purpose of this study is to assess how automated AIS affects Bangladeshi commercial banks. Purposeful sampling and a randomized block design were used to disseminate questionnaires to 400 respondents from Chattogram, Bangladesh, a commercial port city. Five-point Likert scale close-ended questions assess eight aspects of AIS usage and benefits: infrastructure, technological resources, understanding ability, operational comparability, relevance, reliability, decision-making, and organizational performance. Descriptive analysis, Kolmogorov–Smirnov test, Shapiro-Wilk test, factor analysis with Kaiser-Meyer-Olkin test, Cronbach's alpha, one-sample Wilcoxon Signed Rank Test, Kendall's tau-b Correlation Coefficient, Mann-Whitney test, Kruskal-WallisH test, and structural equation model were performed using MS Excel 2016, IBM SPSS Statistics 26, and IBM SPSS. The model was validated using convergent and discriminant validity tests. The study found that technological resources, operational comparability, relevance, and dependability influence AIS-based decision-making, while infrastructure and understandability do not. Private banks and AIS-trained personnel make more AIS-based decisions than public banks and non-AIS-trained workers. Regular and frequent decision-makers use AIS more than sporadic decision-makers. There is no effect of gender or job experience on AIS-based decision-making. Finally, AIS-based decision-making improved organizational performance. Therefore, the study results may assist Bangladeshi commercial banks in adopting AIS.

The estimation of infiltration and inflow in a sewage network by combining continuous monitoring of hydrological parameters, flow and temperature with stable isotopes of oxygen and hydrogen

The failure of sewage network systems can lead to the introduction of external water, impacting the capacity, performance, and environmental sustainability of urban infrastructures. This study examined methods for identifying and quantifying external water in a sewage system in cold climate conditions through the analysis of stable isotope of oxygen (δ18O) and hydrogen (δ2H) from samples, and continuous temperature monitoring, followed by the simulation of the network's hydraulics and temperature profile. The assessment was conducted during periods of low and high groundwater levels, specifically during dry weather flow. In comparison, the yearly trends of infiltration and inflow rates were assessed utilizing the moving minimum method. Using δ18O as a tracer, daily infiltration rates of 5.8 % and 35 % were estimated for periods of low and high groundwater levels, respectively. Using the outputs of the thermodynamic model, temperature was used as a tracer and the daily infiltration rates were found to be 1.5 % and 21.9 % for the same periods. The infiltration and inflow rate for the year in question was estimated to be 23 % using the moving minimum method. The findings of this study demonstrate the temporal variability of infiltration in networks and highlight the need for, as well as the potential of, a multi-faceted approach and continuous monitoring for the accurate estimation of external water before sewage network renovations are carried out.

The performance of renewable energy infrastructure

PurposeRenewable energy infrastructure is an important asset class in the context of reducing global carbon emissions going forward. This includes solar power, wind farms, hydro, battery storage and hydrogen. This paper examines the risk-adjusted performance and diversification benefits of listed renewable energy infrastructure globally over Q1:2009–Q4:2022 to examine the role of renewable energy infrastructure in a global infrastructure portfolio and in a global mixed-asset portfolio. The performance of renewable energy infrastructure is compared with the other major infrastructure sectors and other major asset classes. The strategic investment implications for institutional investors and renewable energy infrastructure in their portfolios going forward are also highlighted. This includes identifying effective pathways for renewable energy infrastructure exposure by institutional investors.Design/methodology/approachUsing quarterly total returns, the risk-adjusted performance and portfolio diversification benefits of global listed renewable energy infrastructure over Q1:2009–Q4:2022 is assessed. Asset allocation diagrams are used to assess the role of renewable energy infrastructure in a global infrastructure portfolio and in a global mixed-asset portfolio.FindingsListed renewable energy infrastructure was seen to underperform the other infrastructure sectors and other major asset classes over 2009–2022. While delivering portfolio diversification benefits, no renewable energy infrastructure was seen in the optimal infrastructure portfolio or mixed-asset portfolio. More impressive performance characteristics were seen by nonlisted infrastructure funds over this period. Practical reasons for these results are provided as well as effective pathways going forward are identified for the fuller inclusion of renewable energy infrastructure in institutional investor portfolios.Practical implicationsInstitutional investors have an important role in supporting reduced global carbon emissions via their investment mandates and asset allocations. Renewable energy infrastructure will be a key asset to assist in the delivery of this important agenda for a greener economy and addressing global warming. Based on this performance analysis, effective pathways are identified for institutional investors of different size assets under management (AUM) to access renewable energy infrastructure. This will see institutional investors embracing critical investment issues as well as environmental and social issues in their investment strategies going forward.Originality/valueThis paper is the first published empirical research analysis on the performance of renewable energy infrastructure at a global level. This research enables empirically validated, more informed and practical decision-making by institutional investors in the renewable energy infrastructure space. The ultimate aim of this paper is to articulate the potential strategic role of renewable energy infrastructure as an important infrastructure sector in the institutional real asset investment space and to identify effective pathways to achieve this renewable energy infrastructure exposure, as institutional investors focus on the strategic issues in reducing global carbon emissions in the context of increased global warming.

An Exploratory Study on Water Scarcity and Coping Mechanisms Among Women Households with Special Reference to Chidothe Village in Zomba, Malawi

Accessing clean and pure water is a crisis for women and families all over the world. Without the proper resources to receive water can lead to the fatality of women and their families. This paper explores water scarcity and household coping mechanisms to water scarcity with special reference to women households in Chidothe village. It identifies the sources of water in Chidothe village, understands the challenges faced by women in fetching water and explores household coping mechanisms to water scarcity with the purpose of raising awareness to the community’s situation. Although the water supply system was expanded in 2001, many areas including Chidothe village are still experiencing water problems. In the past years’ researchers and policy makers have focused on improving the performance of water utility infrastructure in order to eliminate this threat. However, little efforts have been made to understand social issues to water shortage and how people respond to them. Data gathering methods were individual interviews and focus group discussions. All interviews were audio recorded. The data was processed manually and analyzed thematically. The results were analyzed through insights and arguments from Feminist Political Ecology (FPE). The study reveals that women and girls in Chidothe Village have a greater responsibility to fetch water, are facing challenges to access portable water such as lack of money to connect to tap water, the absence of water kiosks in the village further worsens the problem and circumstances force them to draw water from unsafe sources, hence, exposing themselves to diseases. The results imply that there is an urgent need to address water supply systems in order to prevent people from water borne diseases. The study concludes that there is need to incorporate women in decision making to articulate their concerns and interests at local level and also water aid stakeholders should use gender sensitive approaches when planning, designing and implementing water projects.

Experimental investigation on damage of concrete beam embedded with sensor using acoustic emission and digital image correlation

With the construction and development of intelligent roads, an increasing number of sensors are being embedded into the road structures. Portland cement concrete (PCC) pavement is one of the main forms of road pavement, but the mechanical behaviors of the PCC pavement with sensor embedding are less studied. In this study, the damage mechanism of PCC pavement is investigated by using the three-point beam bending test, considering the sensor embedding and the coating of the sensor surface. The effect of sensor embedding on the flexural mechanical properties and the damage failure modes of concrete beams are evaluated by using the acoustic emission (AE) technique and the digital image correlation (DIC) technique. Moreover, the concept of degree of coupling (DC) is introduced to reveal the collaborative bearing mechanism between sensor and concrete, and is characterized by an evaluation model considering ultimate bearing capacity, critical instability point, collaborative bearing capacity, AE ring counts, and AE energy. The findings can provide valuable insights in the packaging design of sensor to guarantee the coupling between the sensor and the surrounding concrete for accurately monitoring the long-term performance of road infrastructure.

Predicting structure performance of urban critical infrastructure: an augmented attention-based LSTM model

PurposeAccurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.Design/methodology/approachThrough formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.FindingsThe empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.Originality/valueThis study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

Emerging Trend and Causes of Discrepancy between Proposed and Actual Flows of Foreign Capital into Nepalese Energy Sector

This paper aims to investigate the trend and causes of vast gap between actual and proposed FDI flows into energy sector by employing principal component analysis and one sample t test. Foreign direct investment is a main source of capital for the development of energy products in Nepal. Therefore, government of Nepal has emphasized to rise inflows of foreign capital to bridge the gap between demand and supply of capital in energy area. However, Nepal has received very small amount of actual FDI from the proposed FDI. It is found the increasing trend of FDI flows into energy sector after 2014/15 and risk factors of the country, market size, administrators’ quality, availability of physical infrastructure and public servant performance are the major factors causing the all proposed foreign capital do not actually flow into the energy sector. To minimize this divide, it is imperative to enhance country-risk factors. To narrow this gap, it is imperative to enhance country-risk factors and enhance the performance of bureaucrats in their specific domains.

Knowledge management capability of National Commission on Muslim Filipinos (NCMF) Region IX-B vis-à-vis knowledge management process, knowledge management infrastructure, intellectual capital, and organizational performance

Knowledge management capability of National Commission on Muslim Filipinos (NCMF) Region IX-B vis-à-vis knowledge management process, knowledge management infrastructure, intellectual capital, and organizational performance

Neural network inspired efficient scalable task scheduling for cloud infrastructure

The rapid development of Cloud Computing in the 21st Century is landmark occasion, not only in the field of technology, but also in the field of engineering and services. The development in cloud architecture and services has enabled fast and easy transfer of data from one unit of a network to other. Cloud services support the latest transport services like smart cars, smart aviation services and many others. In the current trend, smart transport services depend on the performance of cloud Infrastructure and its services. Smart cloud services derive real time computing and allows it to make smart decision. For further improvement in cloud services, cloud resource optimization is a vital cog that defines the performance of cloud. Cloud services have certainly aimed to make the optimum use of all available resources to the become as cost efficient and time efficient as possible. One of the issues that still occur in multiple Cloud Environments is a failure in task execution. While there exist multiple methods to tackle this problem in task scheduling, in the recent times, the use of smart scheduling techniques has come to prominence. In this work, we aim to use the Harmony Search Algorithm and neural networks to create a fault aware system for optimal usage of cloud resources. Cloud environments are in general expected to be free of any errors or faults but with time and experience, we know that no system can be faultless. With our approach, we are looking to create the best possible time-efficient system for faulty environments, Where the result shows that the proposed harmony search-inspired ANN model provides least execution time, number of task failures, power consumption and high resource utilization as compared to recent Red fox and Crow search inspired models.

Study of the influence of raw water infrastructure performance on drinking water

Water utilities are assessed on financial, customer service, operational, and HR performance, according to Government Regulation 122/2015. Environmental, operational, and physical factors impact cooling system performance. Local governments and water utilities assess water supply efficiency using distribution networks. In West Nusa Tenggara, researchers explored Lombok and Sumbawa. Water is abundant in Lombok but insufficient in Sumbawa. Despite the continual requirement for drinking water, this study concentrates on these two criteria since regional climates fluctuate. The former has superior local government water supplies, which affect Lombok and Sumbawa citizens’ health. This research outlines a single drinking water system’s two most important performance criteria. Researchers employ fuzzy c-means clustering, statistical regressive analysis, and questionnaire combinations for indeterminate parameters. The survey concluded that a water distribution authority has a good performance value if its intake and distribution network works effectively and the negative balance decreases. PDAM control will be less effective and the body less healthy if one portion fails, especially if fluctuations of water discharge. This research may result from an integrated drinking water collection and distribution performance model. The study found that water treatment plant performance, especially water discharge, increases water usage.

THE EFFECTS OF ENVIRONMENTAL PECULIARITIES ON TRANSPORTATION INFRASTRUCTURE PERFORMANCE IN LAGOS METROPOLIS, NIGERIA: RESIDENTS' EXPERIENCES

Efficient transportation infrastructure is pivotal for the seamless functioning of global cities, with coastal cities facing unique challenges due to environmental peculiarities. This study delves into how Lagos' environmental peculiarities impact transportation infrastructure performance, shedding light on both positive and negative implications. A comprehensive survey involving 1284 residents within Lagos Metropolis was conducted using multistage sampling techniques. A combination of descriptive (percentage and mean-weighted analysis) and inferential (Fisher’s exact test and Phi Cramer's V Test) statistics was adopted for data analysis. Major findings revealed that the environmental peculiarities in Lagos foster the use of multimodal infrastructure options and concurrently exert adverse effects on various facets of transportation infrastructure performance, including travel cost, commuting time, fare charges, wear and tear of infrastructure, and the overall cost of maintenance and repairs. The results of the Fisher’s exact test underscore the undeniable impact of Lagos' environmental peculiarities on transportation infrastructure performance (p = 0.000 < 0.05). This study concludes that high water tables, as a representative environmental peculiarity, substantially influence transportation infrastructure performance, shaping the mobility needs of residents. In light of these findings, the study advocates for the full implementation of sustainable infrastructure solutions. Recommendations include establishing robust drainage systems, incorporating corrosion-resistant materials, and adopting innovative technologies to mitigate the repercussions of rising sea levels on critical transportation infrastructure, enhancing its resilience in the face of environmental challenges.

Analysis of Plant Productivity, Farmer Income and Availability of Supporting Infrastructure on Soybean Agribusiness Institutional Performance in East Java

This research investigates the dynamics of soybean agribusiness in East Java through the lens of crop productivity, farmer income, supporting infrastructure, and institutional performance. A sample of 150 participants representing diverse scales of farming and geographic regions was surveyed to gather insights. Structural Equation Modeling with Partial Least Squares (SEM-PLS) was employed to analyze the relationships among variables. The study confirmed positive and significant associations between crop productivity and farmer income, supporting infrastructure and crop productivity, and supporting infrastructure and institutional performance. Discriminant validity was established, and model fit indices indicated the reliability of the proposed structural model. Hypothesis testing further validated the impact of the availability of supporting infrastructure, farmer income, and plant productivity on institutional performance. The findings offer practical insights for policymakers, farmers, and stakeholders to optimize soybean agribusiness in East Java.

Exploring the potential of AI-driven optimization in enhancing network performance and efficiency

The exponential growth of network complexity and data volume in modern digital ecosystems has underscored the need for innovative approaches to optimize network performance and efficiency. This paper delves into the potential of AI-driven optimization techniques in addressing this imperative. Leveraging artificial intelligence (AI) algorithms such as machine learning and deep learning, the study investigates how AI can revolutionize network management and operation to achieve higher levels of performance and reliability. Through a comprehensive review of existing literature and case studies, this paper elucidates the fundamental principles, methodologies, and applications of AI-driven optimization in diverse network environments. It examines how AI algorithms can analyze vast amounts of network data, identify patterns, and make data-driven decisions to optimize network configurations, routing protocols, and resource allocation strategies. Moreover, the study explores how AI-driven optimization can enhance network security, fault tolerance, and scalability by autonomously detecting and mitigating potential threats and vulnerabilities. The Review succinctly encapsulates the main findings and insights derived from the analysis, emphasizing the transformative potential of AI-driven optimization for network performance and efficiency enhancement. It underscores the benefits of AI-driven approaches in automating complex optimization tasks, reducing operational overhead, and adapting dynamically to changing network conditions and user demands. Additionally, the Review discusses the challenges and considerations associated with the implementation of AI-driven optimization techniques, including algorithmic bias, data privacy concerns, and ethical implications. In conclusion, the Review underscores the critical role of AI-driven optimization in addressing the evolving challenges of network management and operation. It advocates for continued research and development efforts aimed at harnessing the full potential of AI-driven optimization to unlock new levels of performance and efficiency in network infrastructures. By embracing AI-driven approaches, organizations can streamline network operations, improve user experience, and drive innovation in the digital era.

Reviewing the impact of AI on renewable energy efficiency and management

In recent years, the intersection of artificial intelligence (AI) and renewable energy has emerged as a pivotal domain with transformative potential. This review delves into a comprehensive review of the impact that AI technologies have had on enhancing efficiency and management in the renewable energy sector. The integration of AI into renewable energy systems has ushered in a new era of optimization, addressing challenges and unlocking opportunities for sustainable energy production. AI's role in renewable energy begins with its ability to analyze vast datasets generated by energy systems, weather patterns, and consumption trends. Machine learning algorithms have been employed to predict energy demand, optimize grid operations, and enhance forecasting accuracy, thereby contributing to the increased efficiency of renewable energy sources. This application is crucial for overcoming the intermittent nature of renewable sources such as solar and wind power. Moreover, AI-driven technologies facilitate the intelligent management of energy storage systems, enabling better utilization of excess energy during peak production periods. Advanced control mechanisms, empowered by AI, have significantly improved the coordination of diverse energy sources, ensuring a seamless integration of renewable energy into existing power grids. Additionally, AI-driven predictive maintenance has proven instrumental in reducing downtime and optimizing the performance of renewable energy infrastructure. The review also explores the potential environmental and economic benefits of AI in renewable energy, emphasizing the role of smart grids, demand response systems, and decentralized energy production. As the world strives towards a sustainable energy future, this review offers valuable insights into the ongoing evolution of AI technologies and their impact on revolutionizing the efficiency and management of renewable energy systems. The findings presented here contribute to the growing body of knowledge aimed at accelerating the global transition to cleaner and more sustainable energy solutions.

Green finance, new infrastructure, and carbon emission performance in Chinese cities

Green finance (GF), new infrastructure (NI), and low-carbon green development play important roles in promoting high-quality economic development in China. To date, few studies have considered the relationships among GF, NI, and carbon emission performance (CEP) simultaneously. Using the panel data of 285 Chinese cities from 2011 to 2021, an improved vertical and horizontal pull-out gearing method was used to measure the GF and NI composite indices, and the CEP was measured using the slacks-based measure (SBM) non-expected output model. Using the panel data fixed-effects model and interactive effect model, it was found that both GF and NI can effectively enhance urban CEP and produce a superposition effect, promoting each other to deepen their ability to enhance the CEP. Heterogeneity analysis showed that the effects of GF and NI are more notable in eastern China, with a higher degree of marketization, policy support for the development of the digital economy, and resource-based cities. Green technological innovation, industrial structure upgrading, and resource allocation optimization are pathways for GF and NI to jointly enhance the CEP. Further analysis of the spatial system of the equation model using the generalized three-stage least squares (GS3SLS) methodology found that there were spatial interaction spillover effects among GF, NI, and CEP and that these effects were better for a local area than for the surrounding neighboring cities.

The Effect of School Infrastructure Quality and Economic Factors on Academic Achievement of High School Students in East Java

This research investigates the impact of school infrastructure quality and economic factors on the academic achievement of high school students in Central Java through a survey-based quantitative analysis. A diverse sample of 200 participants, including students, teachers, and parents/guardians, provided insights into demographic characteristics, perceptions of school infrastructure, economic conditions, and academic performance. Structural Equation Modeling-Partial Least Squares (SEM-PLS) was employed to analyze the data. Results indicate positive perceptions of classrooms and laboratories, moderate economic conditions, and varied academic achievements within the sample. The measurement model confirmed the validity and reliability of constructs, while the structural model revealed significant positive relationships between school infrastructure quality, economic factors, and academic achievement. The findings have implications for educational policies, emphasizing the importance of targeted interventions to enhance the overall educational experience in East Java.

The effects of contractual and relational governance on public‐private partnership sustainability

AbstractThe study investigates how contractual and relational governance influence partner behaviors and promote sustainability performance of infrastructure public‐private partnerships (PPPs). Survey data from 217 Chinese PPP projects were analyzed using partial least squares structural equation modeling. This study found that both contractual and relational governance have positive effects on PPP sustainability, with contractual governance having a stronger impact. Contractual governance complements relational governance in promoting cooperative behavior, but relational governance does not amplify the effect of contractual governance on opportunistic behavior. The findings highlight the role of contracts in curbing opportunism and promoting collaboration via multiple functions of control, coordination, and adaption, while also revealing the limitations of relational approaches in the context of government‐business relationships and performance ambiguity. This study contributes to the PPP governance literature by uncovering the individual and interactive effects of contractual and relational governance on the economic, social, and environmental aspects of PPP sustainability.

Optimizing Performance of Cloud Infrastructure Through Effective Resource Scheduling

Cloud computing has emerged as a very promising technology that has garnered significant interest from both industry professionals and academic researchers. Cloud computing service models refer to the various types of services that are provided, including hardware and software infrastructure, platforms for application development, testing, and deployment, as well as enterprise software that is readily available for usage through subscription. Public cloud computing involves the delegation of IT infrastructure, storage, or applications to an external service provider. The presence of a cloud infrastructure also signifies the existence of geographically distributed computing resources. The utilisation of resources in conjunction with cloud computing is not exclusive to large-scale organisations, as it may be employed by entities of any size. Numerous services are offered based on a fee-for-use model, rendering them cost-effective for organisations of various sizes. Cloud service providers are obligated to provide consumers with cloud services on demand, as there is a growing need for such services. This requirement stems from the necessity to decrease the size of large data volumes, which in turn leads to cost savings in maintaining extensive storage systems. The overall effectiveness of cloud computing environments is directly related to the operational performance of cloud infrastructure. This phenomenon holds substantial significance in the realm of optimization, since it enhances the overall efficiency of the underlying cloud architecture. The proposed technique exhibits a significant effectiveness in enhancing cloud performance, as it manifests improvements for both service providers and cloud customers.
 Key Words: Cloud Computing, Task Scheduling, Cloud Infrastructure, Resource Scheduling, Performance Analysis and Infrastructure as Service.