The Architecture and Analysis of a New Cloud Collaborative Commerce Model

Copyright: © 2013 Deka GC. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. As governments around the world continue to grapple with slow-moving economies, cloud computing is emerging as a viable and valuable answer to the persistent demands for reducing public sector spending. Recent attention has shifted to a new initiative called Cloud Government (G-Cloud) incorporating cloud computing into e-Government services, particularly aimed at communicating directly with citizens and implementing cloud computing services in order to cut costs and improve upon the IT services infrastructure. G-Cloud is an Internet based communications which enable public bodies to host, select and use computing services from secure, resilient and cost-effective IT service environment. The ambition of the journey from traditional IT infrastructures towards cloud computing and IaaS is a trusted Cloud with the network as the logical platform to bring the existing assets in the Datacenter and new Cloud computing approaches with Virtualization, Governance, Security, Information and Application. The G-Cloud program’s core benefits are cost reduction, improved services, faster delivery and progressive sustainability agenda. The citizen-centric data, services and applications will be stored, maintained and delivered from G-Cloud and a comprehensive government portal will be a part and parcel of this cloud-based service delivery. Government applications store will provide a “portal” to purchase G-Cloud services. The latest trend on G-Cloud is following the agency-by-agency implementation scenario. Various Ministries/Departments may use cloud services provided by various Cloud Service Provider (CSP) based on the availability in a particular locality as well as the specific requirement of the particular Department/Ministry. It is a fact that all the CSPs are not equally competent in all types of services like accounting, ERP, CRM etc. But for the smooth function of the Government machinery these clouds must be interoperable. Current cloud frameworks are not designed to interact with each other, which make it more difficult for organizations to consolidate their applications and fully realize the savings and productivity benefits of cloud solutions. The dynamic location of service facilities, Virtualization of hardware and software elements is stressing the communication network & protocols, when datacenters are interconnected through the Internet. Although the “Computing” aspects of Cloud have been largely investigated, lower attention has been devoted to the “Networking of Clouds”.

Cloud is a large pool of easily usable and accessible virtualized resources (such as hardware, development platforms and/or services). It provides an on-demand, pay-as-you-go computing resources and had become an alternative to traditional IT Infrastructure. As more and more consumers delegate their task to cloud providers, Service Level Agreement (SLA) between consumer and provider becomes an important aspect. Due to the dynamic nature of cloud the matching of SLA templates need to be dynamic and continuous monitoring of Quality of Service (QoS) is necessary to enforce SLAs. SLA template contains many parameters like cloud's resources (physical memory, main memory, processor speed etc.) and properties (availability, response time etc.). This work addresses the issue of matching SLA parameters to find suitable cloud provider for particular application.

The trend of merging telecommunication infrastructures with traditional IT infrastructures is becoming increasingly more common. The driver behind this development is partly the strong need for enhanced services from the telecom operator?s side aiming for value added services provision to the customer. The effect of this new trend is that existing value chains in the Telco infrastructure will disappear and the merge between the Telco infrastructure and the IT infrastructure can bring added value to the Telco?s network, which is needed in order to increase the willingness to pay for the services. Thus, Telecom operators are very keen on participating in the overall service delivery chain being placed on top of their transport infrastructure. This paper proposes a method, to help to propel the previously mentioned trend by enabling the user to query for aggregated composed services directly from the network edge. A network selection is therefore achived based on the services provided by the access network and their capabilities and costs, parallel to other traditional pure network-related parameters such as signal strength and network QoS.

Data analytics is the science of extracting patterns, trends, and actionable information from large sets of data. The growing nature of data in from different servers with in consistent data formats like structured, semi-structured and unstructured data. Traditional IT infrastructure is simply not able to meet the demands of this new “Data Analytics” landscape. The secondary problem for data analytics is security, the rapid increase in usage of Internet, and consequent applications that allow users to create contents freely, amplify the already huge web volume. In today's data world, there are some vital things to keep in mind while beginning big data and analytics innovation projects. Hence the need of secured data analytics tool is mandatory.

Hybrid cloud is an integrated cloud computing environment utilizing a mix of public cloud, private cloud, and on-premise traditional IT infrastructures. Workload awareness, defined as a detailed full range understanding of each individual workload, is essential in implementing the hybrid cloud. While it is critical to perform an accurate analysis to determine which workloads are appropriate for on-premise deployment versus which workloads can be migrated to a cloud off-premise, the assessment is mainly performed by rule or policy based approaches. In this paper, we introduce StackInsights, a novel cognitive system to automatically analyze and predict the cloud readiness of workloads for an enterprise. Our system harnesses the critical metrics across the entire stack: 1) infrastructure metrics, 2) data relevance metrics, and 3) application taxonomy, to identify workloads that have characteristics of a) low sensitivity with respect to business security, criticality and compliance, and b) low response time requirements and access patterns. Since the capture of the data relevance metrics involves an intrusive and in-depth scanning of the content of storage objects, a machine learning model is applied to perform the business relevance classification by learning from the meta level metrics harnessed across stack. In contrast to traditional methods, StackInsights significantly reduces the total time for hybrid cloud readiness assessment by orders of magnitude.

The increasing proliferation of independent application service providers is making traditional IT infrastructure insufficient when dealing with new issues that appear in a dynamic outsourcing business model. Quality of service (QoS) gradually becomes an essential benchmark to differentiate diverse service providers during service selection process. In this paper, we argue service selection can be deemed as a decision making process -to decide which services providers should be selected within the specified service provision context during a definitive timeslot. Thus, existing decision support approaches can be leveraged if applicable. Hence, we propose a service selection solution which utilizes the decision support systems module (DSS Module) to select the most appropriate service. In DSS module we introduce AHP model to carry out the service QoS measurement based on the Context-specific Quality Aspects. The contributions of this paper are two folds. Firstly, we provide a novel and feasible solution for QoS-based service selection and secondly, we apply DSS module into web services, thus opening a new, fertile ground for DSS research in service ecosystem literature.

Advanced manufacturing such as aerospace, semi-conductor, and flat display device often involves complex production processes, and generates large volume of production data. In general, the production data comes from products with different levels of quality, assembly line with complex flows and equipments, and processing craft with massive controlling parameters. The scale and complexity of data is beyond the analytic power of traditional IT infrastructures. To achieve better manufacturing performance, it is imperative to explore the underlying dependencies of the production data and exploit analytic insights to improve the production process. However, few research and industrial efforts have been reported on providing manufacturers with integrated data analytical solutions to reveal potentials and optimize the production process from data-driven perspectives.

Recent years have shown an increase in the number and complexity of cyberattacks not only on traditional IT infrastructures, but also on smart energy supply networks. Cybercriminals and cyberspies are becoming more sophisticated and persistent in their actions, which requires increased cyber security measures and the development of new technologies to protect infrastructure. In this work we present a framework for detection of Man-in-the-Middle cyberattacks in smart grids based on the application of the ensemble process for feature selection. The workflow of the framework consists of two main stages: model creation and performance evaluation. To create the model, a study was carried out, which involved the selection of the optimal set of features by applying various methods of feature selection. In addition to the sets of features that were formed by feature selection methods, another set of features was formed, which is the result of the process of assembling features that were formed by feature selection methods. A feature score was proposed, which allows weighting of each feature depending on its frequency of selection in all selection methods and the relative number of selected features in the entire set. To check the effectiveness of detecting MitM cyberattacks, SVM and Decision Tree classifiers were used, the test data for which were generated according to the obtained sets of features.

The rapid adoption of cloud computing technologies has transformed the way organizations design, develop, and manage complex projects. Cloud-based environments, characterized by their distributed architectures, dynamic scaling, and multi-tenancy features, offer enhanced flexibility and scalability. However, these benefits also introduce new layers of complexity and vulnerability, making risk assessment and mitigation more challenging than in traditional IT infrastructures. This explores the latest advances in risk assessment and mitigation strategies specifically tailored for complex cloud-based project environments. Modern risk landscapes in the cloud are shaped by evolving cybersecurity threats, compliance demands, integration challenges, and operational uncertainties. Traditional risk assessment models often fall short in capturing the dynamic and interconnected nature of cloud ecosystems. In response, recent developments have introduced advanced techniques such as AI-driven threat detection, real-time risk monitoring, and scenario-based probabilistic modeling. These innovations enable proactive identification and prioritization of risks, allowing for more agile and adaptive mitigation responses. Mitigation strategies have also evolved significantly, with the rise of cloud-native security controls, DevSecOps practices, and resilience engineering methods like chaos testing. Organizations are increasingly adopting Zero Trust architectures and automated incident response frameworks to enhance their defensive posture. Additionally, there is a growing focus on vendor risk management and compliance automation to address regulatory requirements across jurisdictions. This highlights several case studies that demonstrate the practical application of these advanced methodologies in various sectors, including healthcare, finance, and enterprise IT. Despite these advancements, challenges such as data sensitivity, high implementation costs, and skill gaps persist. This concludes by outlining future directions for research and practice, emphasizing the need for continuous innovation, cross-disciplinary collaboration, and the integration of emerging technologies like quantum-safe cryptography to ensure robust risk management in cloud environments. These efforts are essential to sustaining the security, resilience, and trustworthiness of modern cloud-based project infrastructures.

Traditional IT infrastructure monitoring systems do not account for contextual relationships between events in log files, which leads to a high rate of false positives (up to 60–80%). This work proposes an innovative hybrid architecture that combines semantic understanding of event sequences (LSTM) with the classification accuracy of tabular models (XGBoost). The main idea is to create a “semantic fingerprint” of the event history for each service. The expected experimental results are anticipated to demonstrate an improvement in the F1 score by 15–25% while maintaining a low latency of less than 50 ms when running exclusively on CPU.

Cloud computing has transformed traditional IT infrastructure, offering dynamic, configurable resources on demand.On the other hand, it brings unique cybersecurity challenges, particularly in protecting sensitive data from unauthorized access.Traditional single factor authentication does not address these risks.By implementing multi-factor authentication (MFA), which combines several factors for verification including password, tokens, and biometrics, this cloud security method has become increasingly popular.Existing authentication methods fall short in the cloud environment, so this paper presents a novel multi-factor, multi-layered cloud access authentication framework.The methodology for MFA implementation uses risk-adaptive authentication based on real-time risk assessment, adaptive policy for MFA selection, and seamless verification workflows for end-users.Moreover, the implementation of the advanced technologies that cover all aspects such as encryption, SSO, and adaptive authentication further safeguards it against evolving threats.Through experimental analyses, we show that the framework is effective to substantially reduce the vulnerabilities without compromising usability and scalability.This will help cloud service providers and organizations alike better equip themselves with an effective and agile security environment through the adoption of next generation MFA strategies.

Recently, deep neural networks (DNNs) have been widely studied and achieved tremendous success in a variety of real-world applications, such as computer vision, natural language processing, medical diagnosis, and machine translation. Deep reinforcement learning (DRL), as an emerging powerful deep learning technique, combines DNNs with reinforcement learning into an interactive system that enables the agent to learn the best policy from the environment to optimize their rewards. DRL opens up many new applications in domains such as healthcare, robotics, and smart grids. With the rapid evolution of IT infrastructures, cloud computing has been witnessed as the most common computing paradigm. The underlying infrastructure of cloud computing relies on a large amount of data centers. Nowadays, with the fast development of communication technologies, such as 5G, cloud computing is now evolving into yet another new paradigm called edge computing, providing for emerging applications, especially in the Internet-of-Things. The energy efficiency issue from both "cloud" and "edge" are becoming more crucial and calls for more attention. To solve the real-world energy efficiency problems, we take advantage of the deep learning and deep reinforcement learning techniques for efficient modeling of both "edge" and "cloud" applications. On the "edge" side, wearable technologies and the Internet of Things (IoT) are becoming increasingly popular in both personal and commercial applications with the employment of the embedded system technologies. Non-Volatile Processors (NVPs) have been proposed for the intermittent power supply problem, to ensure the instant on/off for embedded systems under the unstable power supply. We proposed neural network-based prediction techniques to derive the best power extraction policy and converter parameters optimization technique for NVPs. In cloud computing, we formulated a novel optimal power management problem of data centers to minimize the total cost by proposing the long short term memory (LSTM) neural network-based method for the prediction of future data center power consumption, and further we presented a novel DRL-based hierarchical framework for solving the overall resource allocation and power management problem in cloud computing systems. Furthermore, we explored the effectiveness of the DRL-based framework for high dimensional action and state spaces problem-dynamic treatment regimes, to model real-life complexity in heterogeneous disease progression and treatment choices, to provide doctors and patients the data-driven personalized decision recommendations. Last but not least, we explored the efficiency of deep neural networks. Structured weight pruning is a representative model compression technique of DNNs to reduce both the storage and computation requirements and meanwhile accelerate inference. An automatic hyperparameter determination process is necessary due to a large number of flexible hyperparameters. We propose an automatic structured pruning framework to compress the DNNs. Our framework outperforms the prior works on automatic model compression by up to 33× in pruning rate (120× reduction in the actual parameter count) under the same accuracy. Significant inference speedup has been observed from the proposed framework on actual measurements on the smartphone.

E-Government is recognized globally as an enabler toward transformation of public sector from a bureaucratic agency centric organizational model to a SMART (simple, moral, accountable, responsive, and transparent) citizen-centric government model. Successful planning and implementation of e-Government requires abundant technical and financial resources for capital investment as well as for systems maintenance on a sustained basis, which inhibits the development of e-government in most developing countries. Public Private Partnership (PPP) is being increasingly recognized as a valuable source of new technology, management expertise, and investment capital. Traditional public sector IT environment is characterised by low asset utilization, duplicative systems, and long procurement cycle time. Cloud computing has made it possible to consolidate IT infrastructure for the entire government relieving individual agencies of the burden of investing and managing IT resources. This paper identifies and examines the e-government challenges faced by developing countries in general and Pakistan in particular proposes a solution that combines the benefits of PPPs and the leapfrogging potential of cloud computing technology.

The modern data centers (DCs) are essential to fulfilling ever-evolving computational demands around cloud computing, big data, and IT infrastructure. These DCs are facilities (Figure 1) that house computer systems and associated components such as networking and storage systems. To operate a DC, power supplies, network connections, environmental controls (e.g., air conditioning, humidity), and security infrastructure are needed. Technology and business challenges such as virtualization, load consolidation, real-time troubleshooting, and service-level guarantees require a robust and adaptive server management plan for enterprise. The majority of DC issues are related to overutilization of resources, application failures, data security, power usage effectiveness (PUE), and infrastructure costs. This requires proactive solutions that are business intelligent and built over a network of sense points that are guaranteed to deliver reliable trends and measurements in a reliable and timely fashion. Since it is expensive to build new DCs, the best option is to improve usage of an existing facility through lower infrastructure overhead to deliver better resource management. An optimal sensor network would perform real-time sensor-data collection and deliver a) improved server rack utilization, b) improved DC cooling, and c) improved loadbalancing through dynamic capping of thermally constrained systems.

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