7 research outputs found
Edge Offloading in Smart Grid
The energy transition supports the shift towards more sustainable energy
alternatives, paving towards decentralized smart grids, where the energy is
generated closer to the point of use. The decentralized smart grids foresee
novel data-driven low latency applications for improving resilience and
responsiveness, such as peer-to-peer energy trading, microgrid control, fault
detection, or demand response. However, the traditional cloud-based smart grid
architectures are unable to meet the requirements of the new emerging
applications such as low latency and high-reliability thus alternative
architectures such as edge, fog, or hybrid need to be adopted. Moreover, edge
offloading can play a pivotal role for the next-generation smart grid AI
applications because it enables the efficient utilization of computing
resources and addresses the challenges of increasing data generated by IoT
devices, optimizing the response time, energy consumption, and network
performance. However, a comprehensive overview of the current state of research
is needed to support sound decisions regarding energy-related applications
offloading from cloud to fog or edge, focusing on smart grid open challenges
and potential impacts. In this paper, we delve into smart grid and
computational distribution architec-tures, including edge-fog-cloud models,
orchestration architecture, and serverless computing, and analyze the
decision-making variables and optimization algorithms to assess the efficiency
of edge offloading. Finally, the work contributes to a comprehensive
understanding of the edge offloading in smart grid, providing a SWOT analysis
to support decision making.Comment: to be submitted to journa
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