StackInsights: Cognitive Learning for Hybrid Cloud Readiness

Cloud Computing IT infrastructure has the potential to be particularly suitable for collaborative commerce (c-commerce) applications; because it generally requires less efforts and interferences for development, customization, integration, operation, and maintenance than other traditional IT infrastructures (e.g., on-premises and data centers). However, upgrading c-commerce applications running on traditional IT infrastructures, to run efficiently on cloud computing infrastructure, faces a number of challenges, mainly, lack of effective and reliable architectural model. This paper presents a description of a new architectural model for developing cloud computing based c-commerce applications; which is denoted as cc-commerce model. The model is an basically based on the standard cloud computing model, and it consists of six main components; these are: client, provider, auditor, broker, security and privacy, and communications network. The new model is implemented in a simple and flexible Web-based test tool, namely, the cc-commerce test (3CT) tool, which is used to evaluate the performance of the model through measuring the response times for four different configurations. The analysis of the obtained results demonstrates that the cc-commerce model can provide better response time than equivalent c-commerce models.

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.

As businesses navigate the complexities of modern IT infrastructure, hybrid cloud solutions have emerged as a compelling choice, blending the strengths of both public and private cloud environments. The increased adoption of hybrid cloud is driven by the need for flexibility, scalability, and enhanced security, allowing organizations to optimize their operations and adapt to changing demands. By leveraging the best of both worlds, companies can maintain sensitive data on private clouds while taking advantage of the expansive resources and cost efficiencies offered by public clouds. This balanced approach not only ensures business continuity and disaster recovery but also supports the seamless integration of legacy systems with cutting-edge cloud technologies. Moreover, the hybrid cloud model offers organizations the agility to innovate rapidly, respond to market changes, and meet regulatory requirements without compromising performance or security. As more businesses recognize the strategic benefits of hybrid cloud, its adoption is expected to accelerate, making it a cornerstone of digital transformation initiatives. This article explores the rising trend of hybrid cloud adoption, highlighting its key advantages, challenges, and the role it plays in shaping the future of enterprise IT.

Cloud computing is one of the emerging technologies in last decade; this technology provides a service model to organizations and public users. Organization users and developers, they start and maintain their organization without any hardware and software infrastructure they can develop their company with the help of cloud technology. Cloud deployment is categorized into three types, private, public and hybrid cloud environment and here public and private cloud more secure comparatively hybrid cloud because when data is moving from private to public cloud security problem is occur. Data communication is most important task in a network environment so the proposed model is focus on data security in hybrid cloud environment. In a hybrid cloud the participation of private cloud and its nodes very important after that the data transmission is depends on active node, In this node selection is based on genetic algorithm. The result of proposed model is going to compare with private and public cloud security parameters.

Over the last few years, the context of big data has gained a significant traction due to many factors. While the public cloud model had been deeply studied to face the increasing demand for large-scale data processing capabilities, many organizations are now focusing on the hybrid cloud model, where the classic scenario is enriched with a private (company owned) cloud -- e.g., for the management of sensible data. In this work, we propose HyMR, a policy to enable autonomic cloud bursting for clusters of virtual machines operating MapReduce jobs over a hybrid cloud. This policy -- together with an infrastructure level system for resource provisioning in hybrid clouds -- can be used to face the temporary (or permanent) lack of computational resources on the private cloud, allowing cloud bursting in the context of big data applications. By means of an empirical evaluation of the system scale-up/-down performance, we show that HyMR policy allows the user to significantly reduce the data-processing time.

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.

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.

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.

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.

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”.

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.

Deadline-constrained cost-aware workflow scheduling in hybrid cloud