Client Requirements Research Articles

Enhancing Requirements Change Request Categorization and Prioritization in Agile Software Development Using Analytic Hierarchy Process (AHP)

Software development now relies heavily on agile methods, which call for the efficient administration and prioritization of change requests. In order to improve requirement prioritization using the Analytic Hierarchy Process (AHP) in Agile methods, this study article presents a new framework for classifying software requirements into Small Change Requests (SCRs) and Large Change Requests (LCRs). The paper examines the difficulties associated with requirement prioritization and categorization in Agile settings and offers a methodical system for dividing change requests into categories based on complexity, impact, and timeline. In order to provide a thorough grasp of the project scope and objectives, the framework considers both functional and non-functional needs. A case study containing several Agile software development projects is used to evaluate the performance of the suggested categorization and prioritization model. According to the findings, the combination of SCR and LCR categorization with AHP enables more effective teamwork and greater matching of development goals with partner objectives. The research also shows that the suggested framework's integration into the Agile development process results in a more efficient decision-making process, less time wasted on talks, and improved resource distribution. The model aids in risk mitigation by allowing a methodical and quantifiable approach to requirement prioritization. These risks are related to quick changes in project scope and changing client requirements. By presenting a fresh framework for requirement categorization and prioritization, this study adds to the current discussion on successful requirement management in Agile methods. Agile software development projects become more effective and adaptable overall thanks to the incorporation of AHP, which guarantees a more methodical and objective prioritization process. This study has the potential to greatly improve the administration of shifting needs and user expectations in Agile settings by offering a structured method to classify and rank change requests.

Developing Individual Flexibility Analysis Insistence Outlining

Manufacturing production refers to the methodology for most efficiently manufacturing products and goods for sale. To produce in the most efficient way possible, there are many variables that need to be considered. Many variables impact manufacturing production, such as the availability of raw materials, marketplace demand, labour costs, and inventory costs. Giving the clients the exact product with high quality is the biggest concern when it comes to the manufacturing process. There is a high probability of not meeting the expectations of clients after the production of the products. So before production, the client requirements need to be analysed and it should be checked whether they are possible or not, whether production can produce the products or not, and whether the budget to produce these products needs to be analysed too. This is why this project uses a machine-learning algorithm called logistic regression. Logistic regression estimates the probability of an event occurring, such as voting or not voting, based on a given dataset of independent variables. Since the outcome is a probability, the dependent variable is bounded between 0 and 1. This logistic regression algorithm helps in various ways in this project. After the production process, the products need to be checked for quality; if the quality meets the client's expectations, the products are sent to the client, or else the products are again sent to manufacturing to produce again, which also has to meet the client's expectations. Utilising the raw materials in an efficient way is a major key to efficient production. It should be done by assessing the client's requirements. This is basically done not to reject client requirements but to assess the impossible client requirements and convert them into a possible solution to the maximum extent possible, not to reject client requirements

Modeling of the Operational Parameters on the Performance of an Air Conditioning System

Heat and moisture are removed from occupied spaces by air conditioning systems. Air conditioners regulate moisture and temperature, and improve air quality. Human efficiency is dramatically boosted and worker's efficiency is affected by their work environment. Human being is more comfortable and productive with air conditioning. After a few years of excellent operation, the system began to fail, resulting in numerous resident complaints. The goal of this study is to establish operational characteristic models for an air conditioning system's performance in order to fulfill future client requirements. The inlet and output temperatures as a function of local time were measured for the LSBLG 1200/MCF model central water chiller air conditioning system. The COP and EER curves were examined. MiniTAB 16.0 was used to establish the characteristic curves and models, according to the findings. The regression models are statistically significant at 97.7 percent and 91.1 percent for p-value less than 0.05 and R2 , respectively. The COP and EER values were calculated, and it was determined that the best cooling occurred between 1 and 3 p.m. The chiller and cooling tower achieve maximum coefficients of performance (COP) of 52 and 20, respectively. The chiller and cooling tower have EER ratings of 177 and 68, respectively. Regression analysis is used to develop models that characterize the statistical relationship between operational parameters such as input and outlet temperatures as a function of local time and response variables such as COP and EER. The performance of the system can be monitored using existing models. Keywords: Modeling, operational parameters, performance, and air conditioning system.

Pre-design CSFs (critical success factors) of building projects for Dubai's development boom

PurposeThis paper aims to identify the pre-design critical success factors CSFs pertaining to different types of construction projects in the rapidly growing city of Dubai, by adopting one of the multi-criteria decision-making (MCDM) tools, analytic hierarchy process (AHP).Design/methodology/approachA mapping process was utilized to filter and categorize CSFs gathered from a literature review. A total of 5 main and 27 sub-main criteria were derived following RIBA's Plan of Work 2020 (Royal Institute of British Architects). A questionnaire was distributed among experts in the construction industry to assess the development in vicinity of the Sheikh Zayed Road (SZR). An analysis was conducted to prioritize the CSFs as per experts' judgment.FindingsAccording to the experts' combined judgment, CSFs linked to client requirements (CR) are the most significant at 22.9% priority weight. Overall project review (OPR), project team (PT), project preparation (PP), previous projects feedback and experience (PPF) success factors achieved 21.4, 19.5, 18.9 and 17.2%, respectively. Within CR, sub-CSFs of client consultation and acceptance (CCA), responsibly matrix for stakeholders (RM), client track record (CTR) and client level of service (CLS) were prioritized by the experts.Research limitations/implicationsThis work was carried out by respondents based in UAE only, and focused on one urban stretch of Dubai, this could be replicated by employing experts from other countries and other case study areas within Dubai.Practical implicationsThis study would significantly have an impact on the education of current and future project managers, planners, engineers, architects and, most importantly, decision-makers on helpful pre-design strategies for enhancing their project success. This research could assist, direct and educate management, as the management provides/manages the necessary resources for projects. Furthermore, management also has the authority to choose project leaders/consultants that will make the project successful.Social implicationsConsolidating Dubai's phenomenal growth requires prioritizing socio-economic strategies in future development projects at the pre-design and pre-construction stage, which will improve the city's business environment and the quality of life for Dubai's residents. This opens up fresh possibilities over the next few years for integrating robust procedures for successful execution, monitoring and evaluation of future construction developments from the outset.Originality/valueAlthough many studies have identified various factors as important for successful building projects, few appear to have done a complete analysis using pre-design criteria to systematically categorize professional views on the relative relevance of CSFs. There is a lack in research examining the factors that led to Dubai's successful construction projects. This study attempts to bridge this gap and provide a prioritized framework reference model to assist project/asset managers and decision-makers.

Trans Women’s Body Self-Image and Health: Meanings and Impacts of Sex Work

This paper aims to present and discuss the results of a qualitative study conducted in the city of Porto, Portugal, aiming to understand the self-determination process and the meanings and impacts of sex work on trans women’s body self-image and health. Between January and July 2019, six individual interviews were performed with participants aged between 23 and 57 and then analyzed using thematic analysis. The results suggest that the participants were subjected to several experiences of discrimination and violence since childhood, negatively impacting their health and social integration. In their narratives, a conflict between the idea of an idealized body, congruent with gender identity, and the idea of a profitable body, compatible with the requirements of sex work clients, emerged. As a consequence, several tensions are experienced, both in personal and professional domains. Additionally, the difficulties felt in accessing the National Health System, to receive specialized support and treatment, increase the risk of being exposed to clandestine procedures, which exacerbates their vulnerability. The findings point to the importance of comprehending trans women sex workers’ needs and reanalyzing how their health conditions might be improved.

Floor plan recommendation system using graph neural network with spatial relationship dataset

The purpose of this study was to develop a recommendation system that, in the pre-design phase, quickly and easily search adequate floor plans satisfying the client requirements about the spatial relationship type using artificial intelligence (AI) technology. In this study using a graph dataset representing the spatial relationship between entities, we propose a deep neural network approach using SimGNN and shallow networks with teacher–student learning to compute graph similarity, measured by graph edit distance, fast and accurately during the search operation in the recommendation system. The prediction errors between the GED score (ground truth) and the predicted score were small enough to employ the neural networks for the recommendation system instead of using GED, which takes a long calculation time. The proposed recommendation systems based deep networks also suggested floor plans satisfying given conditions on the spatial relationship with high accuracy.

Local Differential Privacy-Based Federated Learning under Personalized Settings

Federated learning is a distributed machine learning paradigm, which utilizes multiple clients’ data to train a model. Although federated learning does not require clients to disclose their original data, studies have shown that attackers can infer clients’ privacy by analyzing the local models shared by clients. Local differential privacy (LDP) can help to solve the above privacy issue. However, most of the existing federated learning studies based on LDP, rarely consider the diverse privacy requirements of clients. In this paper, we propose an LDP-based federated learning framework, that can meet the personalized privacy requirements of clients. We consider both independent identically distributed (IID) datasets and non-independent identically distributed (non-IID) datasets, and design model perturbation methods, respectively. Moreover, we propose two model aggregation methods, namely weighted average method and probability-based selection method. The main idea, is to weaken the impact of those privacy-conscious clients, who choose relatively small privacy budgets, on the federated model. Experiments on three commonly used datasets, namely MNIST, Fashion-MNIST, and forest cover-types, show that the proposed aggregation methods perform better than the classic arithmetic average method, in the personalized privacy preserving scenario.

Methods of Teaching Graphic Design in HEIs for Art

Under the conditions of digitalization in technology and art, there is a sense of significant changes in visual creativity, where design projects are given not only new artistic expressiveness, but everything becomes an immeasurable resource base and source of inspiration for future graphic designers. This new reality encourages the permanent updating of approaches and the use of innovative methods of teaching graphic design in HEIs for art in the process of preparing for the professional activity of a future graphic designer. Therefore, a modern graphic designer, being the creator of a demanded graphic product that meets the requirements of business and clients, must have a sense of style, and artistic taste, track new trends in graphic design, be versed in styles, be creative, able to create original innovative design projects, have technical knowledge and computer programs, etc. The research aims to establish a pattern of promoting the use of differentiated methods of teaching graphic design in higher education institutions for art. The goal can be achieved by surveying higher education students on the Internet to determine the ability of HEIs for art to apply effective methods of teaching graphic design. Research methods: comparative analysis; systematization; generalization; survey. Results. The survey among higher education students found that online graphic design platforms such as Canva (98.3%), Pixlr (94.1%), Design Wizard (93.8%), Visme (92.4%), Snappa (89.8%), BeFunky (89.2%), etc. contribute to the formation of professional skills in the field of graphic design. It has been found that the following graphic design programs on PCs, namely Adobe Photoshop (95.5%), Affinity Designer (95.3%), Gravit Designer (91.5%), Adobe InDesign (89.9%), Adobe Illustrator (89.5%), best contribute to the formation of professional skills in future graphic designers. It has been determined that 3D graphic design programs such as Paint3D (91.9%), Autodesk Maya (89%), Sumo3D (87.9%), Blender (85.7%), Autodesk 3ds Max (83.3%), ZBrush (81.5%), etc. are the most conducive to the formation of professional skills. The study has revealed which methods of teaching graphic design in HEIs for art are used by teachers. These include innovative methods (91.8%), the method of graphic modeling (89.8%), the method of observation and independent reproduction of certain artistic images through graphic design (87.3%), the method of graphic illustration (86.2%), the method of projective and graphic form finding (81%), etc. It has been established that the successful implementation of differentiated methods of teaching graphic design in HEIs for art contributes to the formation of skills in using modern computer graphics programs to create design objects (91.2%), the ability to use basic skills in project graphics (89.3%), the ability to know color science to create a coloristic solution for a future design object (89%), etc.

A Comprehensive Security Architecture for Information Management throughout the Lifecycle of IoT Products

The Internet of things (IoT) is expected to have an impact on business and the world at large in a way comparable to the Internet itself. An IoT product is a physical product with an associated virtual counterpart connected to the internet with computational as well as communication capabilities. The possibility to collect information from internet-connected products and sensors gives unprecedented possibilities to improve and optimize product use and maintenance. Virtual counterpart and digital twin (DT) concepts have been proposed as a solution for providing the necessary information management throughout the whole product lifecycle, which we here call product lifecycle information management (PLIM). Security in these systems is imperative due to the multiple ways in which opponents can attack the system during the whole lifecycle of an IoT product. To address this need, the current study proposes a security architecture for the IoT, taking into particular consideration the requirements of PLIM. The security architecture has been designed for the Open Messaging Interface (O-MI) and Open Data Format (O-DF) standards for the IoT and product lifecycle management (PLM) but it is also applicable to other IoT and PLIM architectures. The proposed security architecture is capable of hindering unauthorized access to information and restricts access levels based on user roles and permissions. Based on our findings, the proposed security architecture is the first security model for PLIM to integrate and coordinate the IoT ecosystem, by dividing the security approaches into two domains: user client and product domain. The security architecture has been deployed in smart city use cases in three different European cities, Helsinki, Lyon, and Brussels, to validate the security metrics in the proposed approach. Our analysis shows that the proposed security architecture can easily integrate the security requirements of both clients and products providing solutions for them as demonstrated in the implemented use cases.

Review: Application of Machine Learning to Investment Portfolios

This review focuses on the use of three machine learning methods in portfolios using reinforcement learning, recurrent neural networks and random forests. Machine learning algorithms create a model from training or sample data, train themselves by continuously learning from the data, and then adjust their actions based on the insights found to gain the ability to make better predictions and decisions. As today's big data continues to expand and grow, the market demand for machine learning is expected to increase significantly. This is because companies can use it to understand trends in customer behavior and business operation patterns to support the development of new products. Thus, the use of machine learning methods to build an optimal portfolio can help investors minimize risk and maximize returns. Asset management firms enable investors to invest in the best investment opportunities by developing investment plans based on specific client requirements and return expectations.

Blind matching algorithm based proxy distribution against internet censorship

AbstractProxy based censorship circumvention technique is treated as one of the most promising approaches to fighting against Internet censorship, and its practical applications like the onion router and Lantern have been widely utilized. However, the proxy distribution, as a basic and core issue, still remains unsolved due to (i) the finiteness and non‐renewable nature of proxy resources, (ii) the heterogeneity of clients and proxies requirements, and (iii) the existence of secondary censorship. In this paper, a win‐win driven proxy distribution mechanism for proxy based censorship circumvention systems is designed by leveraging the blind matching theory. Specifically, a combinational manner is developed to measure the quality of service (QoS) requirements of clients in terms of normalized bandwidth and reliability, and security requirements of proxies in terms of credits and suspicion. Then, by introducing the concept of virtual currency, the proxy distribution issue is formulated as two‐sided combinatorial problems from the perspective of clients and proxies, respectively. To solve the complex NP‐hard combinatorial problems in a win‐win manner without unilateral dominance, a two‐sided many‐to‐many context‐free matching game is constructed, in which the aspiration levels and agreement functions are defined to capture the security and QoS concerns of clients and proxies. Further, a modified notion of pairwise stability is exploited as the solution concept, and a client‐proxy blind matching algorithm with market and information decentralization is proposed to obtain the ε‐pairwise stable solution, where clients and proxies make encounters randomly and update their aspiration levels through a stochastic learning process. Finally, theoretical analysis and numerical results are carried out to validate the properties and performance of the proposed algorithm including stability, complexity, and convergence.

Critical audit indicators for assessing construction projects performance (CPP) at pre-contract stage in Tanzania

This study evaluates the current practices of auditing for CPP in Tanzania and identifies critical performance audit indicators at the PCS. A sequential exploratory mixed methods approach comprising of documentary review from four (4) main audit institutions in Tanzania, 9-semi-structured interviews, and 43 questionnaires from audit experts were employed. Qualitative content analysis, and descriptive and inferential statistics were used to analyse qualitative and quantitative data respectively. From the findings, practitioners variably audit the performance of projects, either by omitting some measures at specific stages, measuring certain items in isolation, or overlapping some measures across stages of a project, which influences the different interpretations of the audit results. Study findings further indicate that procurement plans, budget, and feasibility study are the top most significant indicators at a planning stage, while client requirements, contract documents, and specifications are the top significant indicators at the design stage. Appropriateness of the method of procurement, evaluation process, award of contract, and capacity and competence of the selected contractor regarding project size, and complexity, are considered the most significant at the procurement stage. This study provides useful insights into performance audit indicators for construction project auditors and a baseline for audit practice policy directions in developing countries.

Project Failure: A Bad Communication (Case Study)

The most common cause of project failure is miscommunication. It is most underrated and purposefully not deeply planned by most of the contractors, like Sujata, who become confused when changes and updates are not communicated correctly and on time. This ambiguity leads to errors, missed steps in the process, conflicts, and other problems. All these scenarios impacted the project, resulting in delays or budget overruns in the end. In this case, multiple stakeholder communications and changes in client requirements caused the project to fail and updates are not communicated correctly and on time. This ambiguity leads to errors, missed steps in the process, conflicts, and other issues. All these scenarios will result in delays or budget overruns in the end. In this case, multiple stakeholder communications and changes in client requirements caused the project to fail. As a result, internal conflicts will arise, with teams blaming each other for inefficiencies and poor deliverables. This case also entails the importance of stakeholder engagement at different levels of the project and the strategy to be implemented for stakeholder management. The goal of this case study is to draw attention to the difficulties that arise when small and medium-sized businesses fail to recognize the value of communication management in stakeholder engagement, which provided them success but were unable to account for the losses they suffered. This will enable them to account for the costs associated with change management involving stakeholder engagement.

Developing a weighted synergy network–based model for selecting prefabricated construction services

PurposeThe purpose of this paper is to develop a model considering synergy effect for prefabricated construction service combination selection.Design/methodology/approachThis research defines prefabricated construction service as a service-led construction method that meets the specific requirements of clients. Based on network theory, the multi-dimensional collaborative relationships of the prefabricated construction inter-services are formulated. The synergy effect is quantitatively calculated through the linear weighting of the strengths of collaborative relationships. Further, a weighted synergy network (WSN) is developed, from which a service composition selection model considering the synergy effect is established. Then, a genetic algorithm is employed to implement the model.FindingsThe results showed that (1) when the number of prefabricated construction services is increased, the synergy effect of combination options is enhanced; (2) The finer-grained prefabricated construction services, the stronger the synergy effect of service combination; (3) Clients have heterogeneous preferences for collaborative relationships, and there are differences in the synergy effect of service combination.Originality/valueThe contribution of this research includes proposed a method to quantify the synergy effect from the perspective of collaborative relationships, explored the specific procedure for the prefabricated construction service combination selection under the service-led construction, and provided a reference for promoting the development in construction. Besides, the model proposed could be applied to prefabricated construction service composition selection with diverse research boundaries or client preferences by executing the same procedure.

Implications of Onshore Development on Global Software Engineering

Recently software industry has paid significant attention to customizing software products across distributed boundaries. Communicating the requirements of multiple clients across distributed borders is a crucial challenge for the software customization process. Local decision-making and local development at the client site are considered methods for reducing difficulties in communicating the requirements of multiple clients across distributed boundaries. This paper introduces a new model called the onshore development model (ODM) for accomplishing the customization requests in the distributed development process of software. This model presents a scenario for enhancing the onsite development of specific requirements to reduce delays and misunderstandings between the clients and the team involved. This model depends on moving the development process to the client’s location. Three empirical studies were conducted to evaluate the proposed model to measure its productivity, time performance, and cost reduction. The proposed model has been compared with two other models: the basic model (BM), which allocates the decision-making process and the development process for teams at the vendor’s location, and the local decision-making model (LDec), which assigns the decision-making process for team at the client’s location. The results of the empirical studies showed significant outperforming of the proposed model over the basic model and local decision-making model in productivity, time performance, and cost reduction. The productivity of the proposed model improved by 39% and 10% more than the basic model and the local decision-making model, respectively. In addition, the time performance of the proposed model became faster by 49% and 20.8% than the basic model and the local decision-making model, respectively. Also, it reduced the total cost of the development process by 31% in terms of the salaries of all persons involved in requirements collecting, decision-making, and development.

Perspective of Construction Building Professionals on Low-Carbon Materials in Malaysia

Low-carbon (LC) materials and alternative technology must overcome various institutional policy and market constraints to gain greater acceptance in the construction sector. The primary impediments that must be addressed to build a viable and sustainable local construction sector are awareness and knowledge. To analyse the perspective on LC materials in the northern states of Malaysia, 93 companies were surveyed via their construction building professionals (CBPs) on awareness, usage frequency, user experience, drivers for material selection and barriers to LC material adoption in projects. To establish an understanding of CBPs and LC materials, survey data were analysed using semi-structured qualitative-quantitative approaches and the Statistical Package for the Social Sciences (SPSS), influenced by basic theory. 79.57% valid responses indicate that most participants had a low degree of awareness, with 95% confidence that there is no difference in the score levels of the overall viewpoints between architects, civil engineers and structural engineers. While unfired bricks (16.2%) are commonly and widely employed in construction projects, structural insulated panels (40.9%) are the most favourable LC materials for future projects. Client requirements, regulatory requirements and expectations of a shorter completion time are the most important factors driving CBPs to select LC materials, accounting for 44.6%, 37.8% and 37.8%, respectively. According to the CBPs, the main constraint factor to market acceptability is the “Lack of sustainable material information” (44.9%). The proposed recommendations include “Training on designing a building with LC material”, “Clear regulation on limiting carbon emission in a project” and “Increase demonstration of projects and case studies” at a rate ranging from 36.5% to 43.2% to improve client and CBPs acceptance of LC materials for a more sustainable building sector.

Task Based Assessment: An Innovative Methodology for Studying and Assessing High Level Programming-Oriented Courses

Many students are being struggled with high level programming related courses. It is an enthusiastic and dynamic approach to teach and assess such courses for the development of their programming skills. Also, traditional approach to evaluate such high-level programmingoriented courses is with list of programs repeatedly being followed in consecutive batches as well as theory exam is conducted that generally focuses on remembering/understanding oriented questions. During the study tenure, students get very less opportunities to write or solve technical problems or tasks as per the IT industry needs as well as they get struggle to be selected in a reputed IT company by clearing the technical examination through the campus drive. Also, IT companies do not follow only the mechanism i.e., “writing code from scratch for all projects”. In fact, many more situations arise where students must outperform similarly. Such circumstances can be discovered depending on the work culture, constraints, coding elegance, etc. within each industry. Proposed method is one of such mechanisms out of these i.e., each problem is being divided into different tasks. Each task may represent a requirement of client or a set of small requirements. Here, the proposed innovative methodology is “Task Based Assessment (TBA)” for high level programming-oriented courses. This would focus on outcome based learning and innovative assessment approaches. The results obtained with the conventional approach are compared to the results obtained with the TBA approach, and it is discovered that a greater number of students obtained more than B+ grade (>=70%) with the proposed TBA approach, as also statistically proven. This correlation indicates that students are more conscious and interested in the TBA approach as opposed to mugged up & writing hypothetical ideas into the examinations. Furthermore, student feedback has been found to be extremely positive. Also, this would helpstudents to improve their programming skills to cope with the IT industry challenges. Keywords: High Level Programming Languages, Programming Skills, Technical Skills, IT Industry Challenges, Task Based Assessment, Innovative Assessment

Proposal for Dynamic and Secure Authentication in IoT Architectures Based on SDN

The connectivity of private resources on public infrastructure, user mobility, and the advent of new technologies have added new client and server-side security requirements. Security is the major element of the Internet of Things (IoT) that will certainly reinforce an even greater acceptance of IoT by citizens and companies. Security is critical in this context given the underlying stakes. This paper aims to advance the thinking on authentication of connected objects by proposing an authentication mechanism that meets the needs of IoT systems in terms of security and performance. It is based on SDN (Software-Defined Networking), which refers to a set of advanced technologies that allow for centralized control of network resources. OTP (One-Time Password) is a type of authentication that could be useful in connected object environments and smart cities. This research work extends the principle of OTP and proposes a lightweight authentication method using a new approach to OTP generation that relies on two parameters (Two-Factor Authentication, 2FA) to ensure the security of underlying systems. Subsequently, we leverage the combination of SDN and the 2FA algorithm to propose an adaptive authentication and authorization solution in the IoT network.

Increasing the potential application of Microsoft project and Primavera P6 for project management: A comparative analysis of the residential project

Change during the management of construction projects has been observed due to raised complexities, changes in work orders, change in client requirements and much more sudden and natural reasons. A large number of paperwork is required for planning and scheduling work, which complicates project management. There are lots of software related to planning, scheduling, controlling, and project management in the market to make complicated manual work easier. The current study emphasizes on application of Primavera P6 and Microsoft Project (MSP) for project handling and management. Complexities during the life cycle of the project have been observed, starting from pre-construction planning to schedule the activity sequence, defining labour duties, selecting of construction methods, and assigning resources and responsibilities to the players involved in the work. In this research, A case study of a residential project has been chosen. The costing and scheduling of the project were calculated by using primavera P6 and Microsoft Project. After a detailed study and comparative analysis of scheduling and cost with Primavera P6 and Microsoft Project show very minor differences. The total Project duration in MSP is 93 days and in primavera 87 days. Similarly, after cost comparison analysis by using both techniques, the result shows that using MSP total project cost was 13,34,345 Rs. and in primavera 13,82,573 Rs. After observing the detailed tools of both the software it can be concluded that there is not much difference between both the software parameters. But the application of MSP is much user friendly compared to Primavera. And in countries like India still the application of software base project handling is done for larger construction projects only. For smaller works project managers still limit the use of various software. Both software has some different features for different types of project management-related tasks. This research aims to highlight the positive insights of both tools for the projects. The findings of this research also showcase that for small, medium, and large scale construction requirements of software tool changes and so its effectiveness also.

A Review on Load Balancing Algorithms in Cloud Computing

Cloud computing is a new standard for large-scale distributed and parallel computing that is still in its infancy. At a predetermined time, provides common resources, information, software packages, and other resources in accordance with client requirements. As cloud computing becomes more popular and more people become interested in assistive computing, better and quicker service is necessary. It is necessary to enhance the management of the good load balancing solutions that are already accessible. Recently, numerous clients from all over the world have been seeking various services at a quick rate, negatively impacting cloud computing performance. The various load balancing techniques used in cloud computing are effective in customizing demand by identifying the appropriate virtual machines. In this review, a comparative study will be done between many load balancing algorithms in different cloud computing, as many studies have been done on this important topic. Important research will also be analyzed and defined, and analyzes will be written for each research that talks about dynamic and non-dynamic load balancing techniques and algorithms. Many details will also be mentioned from the researcher's point of view as a summary of each research.