504 research outputs found
Future Trends and Directions for Secure Infrastructure Architecture in the Education Sector: A Systematic Review of Recent Evidence
The most efficient approach to giving large numbers of students’ access to computational resources is through a data center. A contemporary method for building the data center\u27s computer infrastructure is the software-defined model, which enables user tasks to be processed in a reasonable amount of time and at a reasonable cost. The researcher examines potential directions and trends for a secured infrastructure design in this article. Additionally, interoperable, highly reusable modules that can include the newest trends in the education industry are made possible by cloud-based educational software. The Reference Architecture for University Education System Using AWS Services is presented in the paper. In conclusion, automation boosts efficiency by 20% while decreasing researcher involvement in kinetics modeling using CHEMKIN by 10%. Future work will focus on integrating GPUs into open-source programs that will be automated and shared on CloudFlame as a service resource for cooperation in the educational sector
Modern computing: vision and challenges
Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress
Modern computing: Vision and challenges
Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress
Decentralized planning for self-adaptation in multi-cloud environment
The runtime management of Internet of Things (IoT) oriented applications deployed in multi-clouds is a complex issue due to the highly heterogeneous and dynamic execution environment. To effectively cope with such an environment, the cross-layer and multi-cloud effects should be taken into account and a decentralized self-adaptation is a promising solution to maintain and evolve the applications for quality assurance. An important issue to be tackled towards realizing this solution is the uncertainty effect of the adaptation, which may cause negative impact to the other layers or even clouds. In this paper, we tackle such an issue from the planning perspective, since an inappropriate planning strategy can fail the adaptation outcome. Therefore, we present an architectural model for decentralized self-adaptation to support the cross-layer and multi-cloud environment. We also propose a planning model and method to enable the decentralized decision making. The planning is formulated as a Reinforcement Learning problem and solved using the Q-learning algorithm. Through simulation experiments, we conduct a study to assess the effectiveness and sensitivity of the proposed planning approach. The results show that our approach can potentially reduce the negative impact on the cross-layer and multi-cloud environment
A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center
As cloud computing usage grows, cloud data centers play an increasingly
important role. To maximize resource utilization, ensure service quality, and
enhance system performance, it is crucial to allocate tasks and manage
performance effectively. The purpose of this study is to provide an extensive
analysis of task allocation and performance management techniques employed in
cloud data centers. The aim is to systematically categorize and organize
previous research by identifying the cloud computing methodologies, categories,
and gaps. A literature review was conducted, which included the analysis of 463
task allocations and 480 performance management papers. The review revealed
three task allocation research topics and seven performance management methods.
Task allocation research areas are resource allocation, load-Balancing, and
scheduling. Performance management includes monitoring and control, power and
energy management, resource utilization optimization, quality of service
management, fault management, virtual machine management, and network
management. The study proposes new techniques to enhance cloud computing work
allocation and performance management. Short-comings in each approach can guide
future research. The research's findings on cloud data center task allocation
and performance management can assist academics, practitioners, and cloud
service providers in optimizing their systems for dependability,
cost-effectiveness, and scalability. Innovative methodologies can steer future
research to fill gaps in the literature
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