Online learning and technology acceptance has become a highly significant subject in the field of information technology. The challenges of eLearning acceptance and adoption in higher education are complex and multifaceted: it is important to carefully consider the environmental, social, and economic implications of eLearning implementation and to work toward ensuring that eLearning programs are accessible, equitable, and sustainable over the long term. Many theories and models have been proposed over the years to explain individual usage and behavior and measure the degree of acceptance and satisfaction toward technology acceptance and online learning. This study reviews the challenges and limitations of online learning acceptance and adoption for the last ten years (2012–2022). Lack of technical support, awareness, institution readiness, quality online course content, and less information technology skill of faculty members in the early years present challenges. Further, self-efficacy, financial and technology factors, pedagogical learning, socio-economic evolution, digital competence and compatibility, and lack of technological infrastructure have significantly affected the adoption of eLearning in higher education institutions in recent years.

Knowledge Management (KM) is widely discussed by researcher and attracts many enterprisers to extract, dispense and use information in a systematic way under Knowledge Management System (KMS). New technology adoption within the knowledge management system is one of the core issue, identified by researcher and underlined as future research agenda. Cloud computing becomes the most adoptable choice for enterprisers to reduce infrastructure and maintenance cost by shifting business on the cloud. Higher Education Institutions (HEIs) are more enthusiastic about knowledge management due to its primary goal of knowledge creation and sharing. Cloud based knowledge management attract higher education institutions by changing the educational method and objectives due to innovative trends in technology. This exploratory research based on literature review for cloud-based knowledge management, targeting higher education institutions. This study highlights the benefits and challenges associated with cloud-based knowledge management system and its impact on knowledge.

Failure recovery is a nontrivial property for current distributed systems. An autonomous failure recovery in a distributed system is the ability of a system to execute self-corrective action when an instance or a subset of the system becomes faulty. However, autonomous failure recovery in current large distributed system is a very complicated procedure and often complicated to implement. In order to achieve a high level of reliability and availability in current distributed environment,This paper presents an autonomous, self-configured fail-stop failure recovery model. This model utilized the advantages of the distributed neighbor replica technique (NRT). In this paper, the algorithm along with theoretical framework for autonomous failure recovery are illustrated. This paper propose a resource manager for optimal resource selection. In the event of a resource failure, the resource manager autonomously decide on a resource among a faulty resource neighbors and auto-reconfigure the system. This selection is based on certain reliability parameters or criteria. This paper also illustrates a prototype model implementation. The model also demonstrate that this model is theoretically sound with the ability to perform autonomous recovery smoothly by quickly reconfiguring its services upon detection of failur