Cloud computing and its impact on service level: a multi-agent simulation model

As a new computational paradigm, cloud computing is attracting a lot of interest from researchers in the field of the business community and information technology sector and it can integrate with several heterogeneous resources makes it distinguished and unique to fulfill the demands of different types of users. The rapid increase in data volume and fixed access to online resources, which is related to all departments need to mine data for the discovery of knowledge. Its principal peculiarities incorporate a versatile asset design and along this line a suitable system for tending to be comprehended in an ideal mode. From the specific situations where cloud computing can be integrated, its use in business information and intelligence also conveys the highest aspirations from data mining to updates. This study gives an outline of the recent condition of the arrangement of Cloud Computing and elaborates, its implications in Business Intelligence and Data Mining. This study defines multiple layers that are expected to create such a framework in distinctive levels of deliberation, from the fundamental equipment stages to the product assets accessible to actualize the applications. At the end of this study, a few cases related to Data mining methodologies have been relocated to the Cloud Computing paradigm.

Cloud business intelligence can solve numerous management issues that are faced by many businesses. If it is used in a correct manner, it can substitute seamless utilization of crucial information in the growth of business. In the self-hosted environment, business intelligence will face resource crisis situation on the never-ending expansion of warehouses and OLAP's demands on the primary network. Today, cloud computing has instigated optimism for the prospects of future business intelligence. But thing to focus here is, how will business intelligence be implemented on cloud platform, and further, how will the traffic be managed and what will the demand profile look like? Moreover, in today's world, data generated on a daily basis from many different sources are numerous and valuable information for making effective decisions. This chapter focuses and tries to attempt these questions related to taking business intelligence to the cloud.

With the rapid development of national economy, our demand for power resources has also increased rapidly. The traditional power grid can not be achieved operational status of equipment for effective monitoring and control, we will not be able to ensure the quality and quality of electricity service. Cloudbased build smart grid information platform enables gathering the parameter of grid operation state, greatly increasing the economic efficiency of power enterprises. Firstly, cloud computing and smart grid briefly described, and then pointed out that cloud computing applications in the smart grid information platform, and specialized of cloud-based smart grid information platform, in order for the power grid enterprises to enhance their level of service and provide effective reference. KEYWORD: cloud computing; smart power grids; information platform; Application 4th International Conference on Mechanical Materials and Manufacturing Engineering (MMME 2016) © 2016. The authors Published by Atlantis Press 202 3 THE MAIN PROBLEM IS STILL THE SMART GRID INFORMATION PLATFORM FACING The transition to a cloud computing data center. Our data center because of the different regions and the degree of information presents different characteristics, some data centers already have a basic cloud computing capabilities, but most data centers still remain in the initial level, to ensure the normal operation of the existing data center power under the premise, gradual transition to cloud computing data center in order to meet future requirements of the development of smart grid. Restricting data transmission problem. State Grid Corporation between provincial branches and there are a lot of data needs to be transmitted, will bring massive data transmission efficiency decreases, taking into account the load balancing device between the cloud node, thus building a wide area network channels to resolve data transmission problem. Security of data transmission. Data Center for Cloud Computing power companies to build internal private cloud, capable of self-maintenance and management, system wide area network data transmission services, according to the third security zone defined security partition, and install proprietary equipment production area and external public network isolation . 4 CLOUD COMPUTING APPLICATIONS IN THE SMART GRID INFORMATION POLICY PLATFORM Integrate and optimize the management of heterogeneous resources. Among the smart grid systems, data resources, information systems between various platforms and mutually dispersed, severe heterogeneity, thus leading to the integration of the quality of information sharing to increase difficulty, unable to effectively improve network management efficiency for the power distribution system electrical work quality management and marketing operations system maintenance upgrade will not be able to effectively meet demand (Jie Yang, 2014). Cloud computing technology enables the power system to effectively integrate the various business data and computing resources on the basis of information for business collaboration platform built on effective, able to satisfy the huge demand for smart grid information resource sharing. Cloud computing network virtualization and application virtualization technology, the server virtualization technology and other resources to achieve each service abstraction, and then realize the difference between the system and the software effectively eliminated by the appropriate method to achieve the effective integration of information resources, and then to optimize the management, greatly improving the efficiency of heterogeneous resources management applications. Distributed storage and management of large volume of data. After application of smart grid information platform construction, customer service requests varied, huge data volume information brought it to the storage and management of data provides a great challenge. Cloud computing technology in general face amount of data each smart grid system and unit collected using forms distributed storage of data to achieve the effective storage, greatly to ensure the security and reliability of data storage. GFS effective use of technology with each node, the master server is responsible for the effective management of the whole file system, the file system and complete the work of the metadata stored in the data block is the main server for data storage block sliced, and by saving three or more redundant backup storage. At the same time, client access and complete access to the main block server information. Fast Power System Parallel Computation and Treatment. Smart grids and information platform for the large volume of data to provide stable operation of the power grid data base, but more critical is the analysis and design of each system operating data, planning and decision-making, and even failures and transient stability, state estimation and intelligent a variety of computing and content analysis and decision-making process, its huge computing needs, general computing system can not reach its goal (Wang, Mengxue, 2015). Cloud computing technology through parallel computing technology has greatly enhanced parallel processing capability of the power system, which provides parallel programming model makes more simple and efficient parallel algorithms. MapReduce as Google made an important parallel programming model, and its role in large data sets in parallel computing plays a very prominent. Parallel processing job results by the client to achieve effective user submitted, via decomposition automated master role, the corresponding task scheduling to working nodes and nodes work completed by the specific calculations and perform the appropriate task. 5 SMART GRID INFORMATION PLATFORM CLOUD COMPUTING ANALYSIS Cloud Computing Smart Grid Information Platform Architecture. Smart grid technology information platform architecture cloud computing include the following several levels and aspects (Yan Cui, 2015): First, that the platform infrastructure layer is the integration of the various hardware resources and related management capabilities of virtualized using virtual technology to achieve the corresponding hardware devices such as computers and network storage devices abstraction, and then complete the automation and optimization of resource data and load management; secondly, the main platform for the integration of the various layers of software

Business intelligence system (BIS) has gained a high momentum and played a significant role in enhancing the business environment especially with the help of cloud. The BIS with cloud computing is the solution to overcome complex problems where large amounts of data (market analysis, customer’s view, feedback and organization response time) are required to be processed. The objective of this paper is to show the realistic perspective of the possibilities for maximizing benefits and minimizing risks in Indian business. The authors have highlighted the importance of cloud computing in business and tried to develop a framework for integrating BIS with cloud computing, and an attempt has also been made to analyse the efficiency of the proposed model which improves performance over the cloud. Some of the challenging issues as well as the future research directions have also been discussed.

Current market is dynamic. Customer needs change, too. Global competition is also developing and market subjects must be able to interpret information correctly and make decisions instantly. Business Intelligence (BI) tools are based on the ultimate exploitation of data ownership and their transformation into information and knowledge. The increasing amount of data makes it necessary to develop linking business intelligence and cloud computing. This phenomenon brings about new opportunities as well as risks. This contribution is to analyse suitability of linking BI and cloud computing with respect to risks and benefits of cloud technologies. It should result in a group of criteria enabling to provide relevant information about suitability of this technology for organizations. An analysis of accessible studies on decision-making models and cloud computing and an analysis of companies’ attitudes to cloud computing and information technologies both served as starting points of the proposal.

The integration of Artificial Intelligence (AI) and cloud computing has revolutionized enterprise systems, particularly in predictive marketing. AI-powered enterprise solutions enable businesses to analyze vast amounts of data in real-time, enhancing decision-making, customer engagement, and operational efficiency. Predictive analytics allows companies to anticipate consumer behavior, refine marketing strategies, and optimize customer interactions. Cloud computing further supports AI-driven predictive marketing by providing scalable and cost-effective solutions that enhance data processing capabilities and business intelligence. AI-integrated enterprise resource planning (ERP) and customer relationship management (CRM) systems facilitate automated decision-making, improving supply chain management and personalized marketing campaigns. Despite its advantages, AI adoption in enterprise systems and predictive marketing presents challenges such as data privacy concerns, cybersecurity risks, and ethical considerations. The complexity of AI integration requires substantial investment in infrastructure and regulatory compliance to mitigate biases and ensure transparency in AI-driven decisions. Explainable AI (XAI) is increasingly necessary to build trust and accountability in enterprise applications. Future advancements in AI, including blockchain, augmented reality (AR), and quantum computing, will enhance predictive analytics and business intelligence, further transforming marketing automation and decision-making processes. The convergence of AI and blockchain is particularly promising in securing digital transactions and improving data transparency in enterprise operations. As AI continues to reshape enterprise systems and predictive marketing, businesses must adopt responsible AI practices, strengthen cybersecurity measures, and comply with global regulations to maximize its benefits. Companies that leverage AI-driven insights will gain a competitive edge by improving customer engagement, optimizing marketing strategies, and driving sustainable growth in the evolving digital economy.

Cloud computing and big data: Technologies and applications

The traditional way to manage Information Technologies (IT) in the companies is having a data center, and licensing monolithic applications based on the number of CPUs, allowed connections, etc. This also holds for Business Intelligence environments. Nevertheless, technologies have evolved and today other approaches are possible. Specifically, the service paradigm allows to outsource hardware as well as software in a pay-as-you-go model. In this work, we will introduce the concepts related to this paradigm and analyze how they affect Business Intelligence (BI). We will analyze the specificity of services and present specific techniques to engineering service systems (e.g., Cloud Computing, Service-Oriented Architectures -SOA- and Business Process Modeling -BPM-). Then, we will also analyze to which extent it is possible to consider Business Intelligence just a service and use these same techniques on it. Finally, we store the other way round. Since service companies represent around 70% of the Gross Domestic Product (GDP) in the world, special attention must be paid to their characteristics and how to adapt BI techniques to enhance services.

This study aimed to access the current razor clam population dynamics of Don Hoi Lord, to identify fisherman behavior and interactions, and to construct a multi-agent simulation model for collective discussion of razor clam management and conservation. Companion modeling approach, specifically Multi-agent system model (MAS) and Role-Playing game (RPG) was used in this study. Monthly filed data collection was conducted for one year. Line transects and quadrat sampling method were used for collecting razor clam population data on the biggest sand dune in Don Hoi Lord. In addition, socio-economic surveys of local fisherman were conducted monthly to understand their harvesting behavior. A Multi-agent simulation model was constructed under Cormas platform based on both of razor clam population data and local fisherman harvesting behavioral patterns. Then, two rounds of RPG were organized in March and July 2005, to initiate collective discussion among stakeholders. Mean razor clam density was 5.71+-2.49 individual/m[superscript 2], mean razor clam length was 4.55+-0.90 cm and dominant size class was 3-5 cm. At the moment, razor clam population has started to recover from the past exploitation caused by razor clam harvesting policy of provincial government. Besides, both rounds of RPG were an efficient tool to initiate collective learning and discussion among stakeholders. In each round, 4 scenarios were played. Scenario II (closed zone rotation for 3 month/each) in first RPG and scenario IV (quota system) in second RPG have been agreed upon by stakeholders and possible implemented in the future. The result of the multi-agent simulation model based on both agreed scenarios indicated that the razor clam population has responded positively due to consistent dynamics of razor clam population. Finally, a policy based on both agreed scenarios should be an appropriate management for razor clam conservation

The paper aims to identify how Industry 4.0 technologies affect the quality and speed of the managers’ decision-making process across the different stages of the value chain, based on the example of the manufacturing sector. The paper adopts qualitative research, based on nine in-depth interviews with key informants, to capture senior executives’ experiences with implementing Industry 4.0 technologies in their organisations. The research is focused on three manufacturing industries: the automotive, food and furniture industries. The research shows that depending on the stage of the value chain, different Industry 4.0 technologies are more suitable for the support of managers’ decisions. Various Industry 4.0 technologies support decision-making at different stages of the manufacturing value chain. In the Design stage, 3D printing and scanning technologies play a crucial role. In the case of Inbound Logistics, robotisation, automation, Big Data analysis, and Business Intelligence are most useful. During the Manufacturing stage, robotisation, automation, 3D printing, scanning, Business Intelligence, cloud computing, and machine-to-machine (M2M) integration enable quick decision-making and speed up production. Sensors and the Internet of Things (IoT) optimise distribution in the Outbound Logistics stage. And finally, Business Intelligence supports decisions within the Sales and Marketing stage. It is also the most versatile technology among all particular stages. The paper provides empirical evidence on the Industry 4.0 technology support in decision-making at different stages of the manufacturing value chain, which leads to more effective value chain management, ensuring faster and more accurate decisions at each value-chain stage. When using properly selected Industry 4.0 technologies, managers can optimise their production processes, reduce costs, avoid errors and improve customer satisfaction. Simultaneously, Industry 4.0 technologies facilitate predictive analytics to forecast and anticipate future demand, quality issues, and potential risks. This knowledge allows organisations to make better decisions and take proactive actions to prevent problems.

Today, more than ever, organizations face daily environmental changes. The source of change may be a new regulatory law, competitor, business model, hype in technology, new service trend, or the lack of skilled human resources. In order to understand the factors of change and their impacts on business strategies, organizations need to have in place a set of tools, methods and procedures to allow an instantaneous reaction, and foresee proactively the highest number of variables that will have impact in the organization’s competitive advantages. One of the central tools that managers have at their disposal to face these challenges come from the use of Information and Communication Technology (ICT), which are entering into a new stage. The maturity of some Internet technologies, business intelligence systems and outsourcing business models allows the availability of new services, based on cloud computing. The rise of new service trends are taking place, and if an organization is able of combining the advantages of those trends, it might gain competitive advantage, throughout Business Intelligence as a Service (BIaaS), and what began as a way to achieve operating savings, will eminently reveal its true disruptive potential throughout the Internet.

Information share and collaborative decision are important strategies for supply chain partners to realize their common profit. In order to contrast the decision process and decision effect of these strategies, information sharing mode, collaborative forecasting mode and collaborative planning, forecasting and replenishment mode, etc., several kinds of supply chain collaborative decision modes were analyzed and simulated employing procedure modeling technique. They were classified according to the collaborative degree. The fine differences in their information sharing and collaborative decision process of different supply chain collaborative decision modes were indicated and the influence to specific decision variables was quantified then. Finally, simulation experiments results illuminated decision effect of these supply chain collaborative decision modes. The ordering stability and the service level increase with the collaborative degree, and the dissatisfaction of the service level is from imprecise forecasting, or inconsistent ordering, or both.

In the recent era of data science, AI, and machine learning the amount of data has increased exponentially, and future growth is predicted to be even greater. For the last 4 decades RDBMS is way for data storage, but with the rise of cloud computing online applications have taken place of the stand-alone applications, while distributed servers have taken the place of traditional dedicated servers. Cloud computing has become very efficient because of the lower cost, ability to scale up and down, flexibility, and pay-as-you-go model. This is the major change after the birth of the Internet. Cloud computing has revolutionized the way data is stored and accessed. Cloud databases are an integral part of the cloud computing paradigm, offering scalability, flexibility, and cost-efficiency for managing large volumes of data. Cloud databases such as Amazon RDS, IBM DB2 and MongoDB are becoming popular. Cloud databases find the restrictions of older relational databases. Data storage, retrieval, modification, and analysis are essentially the functions of cloud databases. Cloud databases are mostly used by data-intensive applications including data warehousing, data mining, and business intelligence. Applications for supply chain management, online e-commerce, and transactional data management, including banking and airline reservations, also benefit from cloud storage. This research paper reviews the concept of cloud databases, exploring their features, it examines the architecture of cloud databases, the comparison between traditional Relational Database Management Systems (RDBMS) and NoSQL databases, and popular implementations in the cloud environment. Additionally, the challenges faced in their development and deployment.

Until today, Cloud providers only offer a limited set of non-negotiable service levels to their customers. Most often these service levels are expressed as guarantees for availability together with the offer to have access to a virtualised environment with a certain performance the customer may select from a number of predefined configurations. This simplifies the life of the provider, e.g., in terms of effort to maintain an adequate infrastructure, or regarding the effort for reducing the risk violating Service Level Agreements (SLAs) with its customer. In consequence, the current practice is slanted towards the benefit of the provider and ignores more specific requirements of its customers, e.g. regarding data protection and related guarantees. An analysis of the underlying problems shows two major fields to be worked on for solving the problem: Firstly, each provider uses its own proprietary technology for managing SLAs throughout their life-cycles. However, first standards are available and could be employed allowing the customer to use a single standard interface to negotiate with several providers. Secondly, there is neither a common set of terms to describe Cloud customers requirements regarding the Cloud services requested, nor, the back side of the medal, there is a common set of terms to describe the Quality of Service (QoS) of the Cloud providers' offerings. The focus of the presented work is (i) on the standard technology for negotiating and creating SLAs and (ii) the common terms and metrics describing providers' offerings and customers' requirements. Without these terms mapping the customers' requirements to cloud providers' offerings is a tedious manual and error-prone process and resulting SLAs will remain rudimentary. Additionally, both providers and their customers would benefit from more sophisticated and negotiable Service Level Agreements using existing standards. These SLAs are both (i) binding and monitorable agreements between the customer and the provider covering the customers' requirements and (ii) the basis for a QoS-aware Cloud resource management on the side of the provider including provisioning of physical machines and optimised allocation of virtual machines. Besides more traditional QoS aspects, terms related to Cloud Federation, Data Protection or Security Level Agreements are covered. Customers may use standards compliant agreement templates with the providers' offerings to select suitable providers for starting the negotiation to the extent the provider allows in the template. During the negotiation the provider may take into account the actual degree of capacity utilisation of its infrastructure and active SLAs, and may use the SLA resulting from a successful negotiation to further optimise its infrastructure through application and VM consolidation. The work presented in this thesis covers about a decade of research starting in the environment of Grid computing and ending with today's Cloud computing.

This paper proposes an operational business intelligence system for call centers. Using data collected from a large U.S. insurance company, the authors demonstrate a decision tree based solution to help the company achieve excellence through improved service levels. The initial results from this study provide insight into the factors affecting this firm’s call center service levels, and the solution developed in this paper provides two distinct advantages to managers. First, it enables them to identify key factors and the role they play in determining service levels. Second, a sliding window approach is proposed which allows managers to see the effects of resource reallocation on service levels on an on-going basis.