Architecting Resilient SAP Hana Systems: A Framework for Implementation, Performance Optimization, and Lifecycle Maintenance

Purpose – The purpose of this paper is to elicit the rich implications of IT business value for small- and medium-sized enterprises (SMEs) from the perspective of IT leaders. Design/methodology/approach – This paper uses case study and the repertory grid technique to explore cognition of IT business value of the IT leader in Guangdong CHJ Industry Co., Ltd (CHJ), a fast-growing jewelry company in China. The paper further examines this cognitive structure of IT business value by using cluster analysis technique. Findings – This paper develops a six-category framework of IT business value from IT leaders' perspective, which provides insights for future research of IT applications in SMEs. Originality/value – This study contributes to IT business value research by providing empirically grounded insight from perspectives of IT leaders in SMEs. The identified IT business value framework can also serve as a practical tool to help managers comprehensively assess and evaluate IT applications in SMEs.

SAP systems, particularly those running on SAP HANA, face unique challenges in performance optimization due to their real-time, in-memory processing nature. The predictive performance modeling framework incorporates both SAP job history and SAP HANA database metrics to forecast system performance and preemptively address potential issues. By leveraging machine learning algorithms and analyzing historical performance trends, the model predicts bottlenecks, database slowdowns, and job failures, enabling proactive capacity planning and tuning. Real-world implementations demonstrate how predictive analytics optimizes performance across SAP applications and the HANA database, highlighting improvements in both database and application layer performance. Additionally, integration with cloud-native SAP environments enables dynamic resource scaling based on predictions to minimize operational disruptions and ensure high availability.

Cloud Computing is arguably one of the most discussed information technologies today. It presents many promising technological and economical opportunities. However, many customers remain reluctant to move their business IT infrastructure completely to the cloud. One of their main concerns is Cloud Security and the threat of the unknown. Cloud Service Providers (CSP) encourage this perception by not letting their customers see what is behind their virtual curtain. A seldomly discussed, but in this regard highly relevant open issue is the ability to perform digital investigations. This continues to fuel insecurity on the sides of both providers and customers. Cloud Forensics constitutes a new and disruptive challenge for investigators. Due to the decentralized nature of data processing in the cloud, traditional approaches to evidence collection and recovery are no longer practical. This paper focuses on the technical aspects of digital forensics in distributed cloud environments. We contribute by assessing whether it is possible for the customer of cloud computing services to perform a traditional digital investigation from a technical point of view. Furthermore we discuss possible solutions and possible new methodologies helping customers to perform such investigations.

Over the past years, Machine Learning has been applied to an increasing number of problems across numerous industries. However, the steady rise in the application of Machine Learning has not come without challenges since companies often lack the expertise or infrastructure to build their own Machine Learning systems. These challenges led to the emergence of a new paradigm, called Machine Learning as a Service. Scientific literature has mainly analyzed this topic in the context of platform solutions that provide ready-to-use environments for companies. We recently have developed a platform-independent approach and labeled it Machine Learning Services. The aim of the present study is to identify and evaluate challenges and opportunities in the application of Machine Learning Services. To do so, we conducted a Delphi Study with a panel of machine learning experts. The study consisted of three rounds and was structured according to the five steps of the Data Science Lifecycle. A variety of challenges from the areas “Communication”, “Environment”, “Approach”, “Data”, “Retraining, Testing, Monitoring and Updating”, “Model Training and Evaluation” were identified. Subsequently, the challenges revealed by the Delphi Study were compared with previous work on Machine Learning as a Service, which resulted from a structured literature review. The identified areas serve as possible future research fields and give further implications for practice. Alleviating communication issues and assessing the business IT infrastructure prior to the machine learning project are among the key findings of our study.

As part of the ESA/ESOC study “Linux & Multi Core Processor Technology for Simulators” [7], several concepts concerning performance optimization of SIMULUS based operational simulators were introduced. Starting from the ossibility to explore parallelism and distributed execution of computational

Rapid growth of population and non-availability of land space in metropolitan cities of India has led to the unprecedented amount of construction of tall buildings. However, the design of tall buildings is still mainly revolving around the wind loading based on the Indian wind loading standard. Though wind tunnel tests are recently being carried out for many tall buildings, the industry/design practice is still not fully developed to make informed decisions with regard to aerodynamic design, better structural systems, need for detailed study etc. Through this paper, we are exposing the current Indian scenario with regard to the design of tall buildings subjected to wind forces. The paper addresses the most important two performance optimization approaches such as aerodynamic treatment and structural modifications.

Cloud computing has been envisioned as next-generation architecture of IT enterprise.Cloud computing is the internet based model which delivers computing services on demand.Cloud storage moves the users data to large data centers, which are remotely located and on which user does not have any control.Apart having more benefits, this unique feature of the cloud also poses many new security challenges which need to be clearly understood and resolved.The important concerns that need to be addressed in cloud computing is to assure the integrity of data i.e. correctness of his data in the cloud.In this paper we provide a scheme which gives a proof of data integrity in the cloud which the customer can employ to check the correctness of his data in the cloud.This proof can be done by both the cloud and the customer and can be incorporated in the Service level agreement (SLA).Here we are encrypting the hash values and providing the more security.This scheme ensures that the storage at the client side is minimal which will be beneficial for thin clients.Here the computational time is minimized by using integrity and encryption methodology on the data in cloud .It should be noted that this scheme applies only to archive storage data.

Setting up High Availability and Disaster Recovery for SAP HANA presents critical strategies for ensuring business continuity in enterprise environments. The implementation incorporates sophisticated architectural components for maintaining system operations through multi-layered redundancy and protection mechanisms. SAP HANA deployments leverage scale-out systems with coordinator and worker nodes, while system replication provides robust disaster recovery capabilities. Pacemaker cluster management enables automated failover and resource orchestration, with comprehensive testing frameworks ensuring system reliability. The architecture supports advanced features including active/active replication and tenant-level management, allowing organizations to achieve optimal resource utilization and granular control over high availability settings. These implementations demonstrate significant improvements in system performance, data consistency, and recovery capabilities, resulting in substantial reductions in downtime costs and data loss risks. The combination of strategic redundancy, proactive monitoring, and sophisticated replication mechanisms enables organizations to maintain continuous operations while meeting stringent service level agreements in mission-critical environments.

The exponential growth of data-intensive applications and heterogeneous network architectures has increased the demand for intelligent systems capable of predicting network performance and optimizing data flow in real time. Traditional static models are inadequate for addressing dynamic network conditions, latency variations, and bandwidth fluctuations. This paper presents a comprehensive review of existing methodologies and proposes a conceptual machine learning framework for predictive network performance and data flow optimization. The framework integrates supervised and reinforcement learning models with deep neural architectures to forecast traffic patterns, congestion probabilities, and throughput variations. Furthermore, the study explores hybrid approaches that combine network telemetry data, software-defined networking (SDN), and edge intelligence for adaptive traffic routing and self-healing network behaviors. Comparative analyses of feature selection techniques, model training strategies, and optimization algorithms are also discussed to highlight trade-offs in scalability, accuracy, and computational efficiency. The paper concludes by identifying emerging trends such as federated learning, graph neural networks, and explainable AI in predictive network management. By synthesizing insights from current literature and proposing a unified framework, this study contributes to the advancement of intelligent network operations, enabling proactive maintenance, reduced latency, and improved resource utilization across next-generation communication systems.

High enterprise IT service availability is a key success factor throughout many industries. While understanding of the economic importance of availability management is becoming more widespread, the implications for management of Service Level Agreements (SLAs) and thinking about availability risk management are just beginning to unfold. This paper offers a framework within which to think about availability management, highlighting the importance of variance of outage costs. The importance of variance is demonstrated using simulations on existing data sets of revenue data. An important implication is that when outage costs are proportional to outage duration, more but shorter outages should be preferred to fewer but longer, in order to minimize variance. Furthermore, two archetypal cases where the cost of an outage depends non-linearly on its duration are considered. An optimal outage length is derived, and some guidance is also given for its application when the variance of hourly downtime costs is considered. The paper is concluded with a discussion about the feasibility of the method, its practitioner relevance and its implications for SLA management.

To meet service level agreement (SLA) requirements, the majority of enterprise IT infrastructure is typically overprovisioned, underutilized, non-compliant and lacking in required agility resulting in significant inefficiencies. As enterprises introduce and migrate to next-generation applications designed to be horizontally scalable, they require infrastructure that can manage the duality of legacy and next generation application requirements. To address this, composable data center infrastructure disaggregates and refactors compute, storage, network and other infrastructure resources in to shared resources pools that can be “composed” and allocated on-demand. In this paper, we model a theorical problem of resource allocation in a composable data center infrastructure as a bounded multidimensional knapsack and then apply multi-objective optimization algorithms, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Generalized Differential Evolution (GDE3), to allocate resources efficiently. The main goal is to maximize resource availability for the application owner, while meeting minimum requirements (in terms of CPU, memory, network, and storage) within budget constraints. We consider two different scenarios to analyze heterogeneity and variability aspects when allocating resources on composable data center infrastructure.

This study focuses on investigating energy-aware resources and designing an energy-efficient cloud architecture. It aims to optimize the decentralization model and establish migration behavior to improve IT business operations using cloud computing. Several models were evaluated for performance metrics such as physical host use, power utilization, and service level agreement (SLA) misuse. These models include smart cloud optimization for resource configuration handling (SCORCH), V-MAN, decentralized energy-aware collaborative model (DEACM), efficient energy-aware resource management model (EEARMM), and energy and power-aware dynamic migration (EPADM). To handle resource configuration efficiently, the study employed V-MAN, DEACM, EEARMM, and EPADM within the framework of smart cloud optimization. Among these models, EPADM proved to be the most effective for resource utilization through dynamic VM migration. By leveraging EPADM, the cloud system can dynamically migrate virtual machines (VMs) to optimize resource allocation and energy consumption.

BYOD: The Next Wave of Consumerization of IT The Impact of BYOD on the Enterprise IT Landscape

The digital transformation influences business models, processes, and enterprise IT landscape as a whole. Therefore, business-IT alignment is becoming more important than ever before. Enterprise architecture management (EAM) is designed to support and improve this business-IT alignment. The success of EAM crucially depends on the information available about a company's enterprise architecture, such as infrastructure components, applications, and business processes. This paper discusses the results of a qualitative expert survey with 26 experts in the field of EAM. The goal of this survey was to highlight current practices in the information collection for EAM and identify relevant information from enterprise-external data sources. The results provide a comprehensive overview of collected and utilized information in the industry, including an assessment of the relevance of such information. Furthermore, the results highlight challenges in practice and point out investments that organizations plan in the field of EAM.

The TransLeader project (2023-2-PL01-KA220-HED-000179445) introduces a methodology for fostering IT leadership in the rapidly evolving fields of Artificial Intelligence (AI) and the Internet of Things (IoT).By integrating technical expertise with leadership competencies, the project equips future IT business leaders to address the challenges of an increasingly technology-driven global economy.As part of activity A2.5 "Project deliverables," the project partners developed and presented learning requirements tailored to the needs of higher education institutions.This paper expands upon these foundations by introducing a comprehensive model of collective knowledge transfer in IT education applicable world-wide.The model emphasizes the integration of shared knowledge practices, digital transformation strategies, and collaborative learning frameworks, all of which enhance institutional capacity, innovation potential, and sustainable development.In a global context marked by rapid technological evolution, the adoption of collective knowledge mechanisms strengthens IT education systems and supports the development of resilient, future-ready graduates.