Search Results (4)

Infrastructure as Code (IaC) underpins automated cloud provisioning in modern DevOps environments; however, controlled comparative evaluations of leading IaC tools under identical conditions remain limited. This study presents a controlled comparative evaluation of Terraform, Pulumi, and AWS CloudFormation within a standardized Amazon Web Services environment. An identical multi-tier architecture was implemented using each tool, and repeated deployment cycles were conducted to observe differences in provisioning duration, removal time, structural maintainability, and operational characteristics. Descriptive statistical analysis across 30 controlled repetitions indicates that Terraform and Pulumi achieve comparable deployment performance, whereas CloudFormation requires more than twice the average provisioning time under the conditions evaluated. Removal durations were similar across tools but remained longest for CloudFormation. Structural analysis reveals trade-offs between declarative modular design, programmatic flexibility, and native cloud integration. The study provides a controlled, comparative framework to support evidence-based selection of IaC tools in production-oriented cloud environments. Full article

The integration of edge-to-cloud infrastructures in smart grid (SG) data center networks requires scalable, efficient, and secure architecture. Traditional server-based SG data center architectures face high computational loads and delays. To address this problem, a lightweight data center network (DCN) with low-cost, and fast-converging optimization is required. This paper introduces a container-based time synchronization model (CTSM) within a spine–leaf virtual private cloud (SL-VPC), deployed via AWS CloudFormation stack as a practical use case. The CTSM optimizes resource utilization, security, and traffic management while reducing computational overhead. The model was benchmarked against five DCN topologies—DCell, Mesh, Skywalk, Dahu, and Ficonn—using Mininet simulations and a software-defined CloudFormation stack on an Amazon EC2 HPC testbed under realistic SG traffic patterns. The results show that CTSM achieved near-100% reliability, with the highest received energy data (29.87%), lowest packetization delay (13.11%), and highest traffic availability (70.85%). Stateless container engines improved resource allocation, reducing administrative overhead and enhancing grid stability. Software-defined Network (SDN)-driven adaptive routing and load balancing further optimized performance under dynamic demand conditions. These findings position CTSM-SL-VPC as a secure, scalable, and efficient solution for next-generation smart grid automation. Full article

From a sustainable development perspective, continuous industrial growth is an important issue. In recent years, it has become difficult for companies to survive in an increasingly competitive and rapidly changing business environment. To align with these changes, companies must not only rapidly transform their own organizations but also their current business domains. However, it is difficult for a leading company alone to transform existing business domains. While it is known that the transformation of a business area requires cooperation with partners, the mechanism of sustainable growth in existing business ecosystems is unclear. To solve this problem, this paper aims to unveil the role of transformation momentum in the business ecosystem in the IT (information technology) industry, which is rapidly changing from traditional IT services to cloud-based services. This study has selected Amazon Web Services (AWS) and Microsoft Azure as IoT case studies, as these cases have successfully transitioned from their original business domains to new ones. Based on these cases, we established a Sustainable Business Ecosystem Transformation (SBET) model for transforming industries using the business ecosystem’s dynamic capabilities. The SBET model demonstrates how transformation momentum can be created using business ecosystems in four phases (Exploration, Creation, Formation, and Mutation). The SBET model contributes to expanding the business ecosystem concept by adopting sustainable growth and accelerating transformations to enhance global IT business ecosystems. Using the model in this study, companies can achieve continuous growth not only in their own organizations but also in their partners in the wider business domain. Full article

Optical Character Recognition has made large strides in the field of recognizing printed and properly formatted text. However, the effort attributed to developing systems that are able to reliably apply OCR to both printed as well as handwritten text simultaneously, such as hand-filled forms, is lackadaisical. As Machine printed/typed text follows specific formats and fonts while handwritten texts are variable and non-uniform, it is very hard to classify and recognize using traditional OCR only. A pre-processing methodology employing semantic segmentation to identify, segment and crop boxes containing relevant text on a given image in order to improve the results of conventional online-available OCR engines is proposed here. In this paper, the authors have also provided a comparison of popular OCR engines like Microsoft Cognitive Services, Google Cloud Vision and AWS recognitions. We have proposed a pixel-wise classification technique to accurately identify the area of an image containing relevant text, to feed them to a conventional OCR engine in the hopes of improving the quality of the output. The proposed methodology also supports the digitization of mixed typed text documents with amended performance. The experimental study shows that the proposed pipeline architecture provides reliable and quality inputs through complex image preprocessing to Conventional OCR, which results in better accuracy and improved performance. Full article