1,838 research outputs found
Resource Management Techniques in Cloud-Fog for IoT and Mobile Crowdsensing Environments
The unpredictable and huge data generation nowadays by smart devices from IoT and mobile Crowd Sensing applications like (Sensors, smartphones, Wi-Fi routers) need processing power and storage. Cloud provides these capabilities to serve organizations and customers, but when using cloud appear some limitations, the most important of these limitations are Resource Allocation and Task Scheduling. The resource allocation process is a mechanism that ensures allocation virtual machine when there are multiple applications that require various resources such as CPU and I/O memory. Whereas scheduling is the process of determining the sequence in which these tasks come and depart the resources in order to maximize efficiency. In this paper we tried to highlight the most relevant difficulties that cloud computing is now facing. We presented a comprehensive review of resource allocation and scheduling techniques to overcome these limitations. Finally, the previous techniques and strategies for allocation and scheduling have been compared in a table with their drawbacks
Smart Energy Management for Smart Grids
This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book
Influence of the Motor Transport on Sustainable Development of Smart Cities
The transport system is one of the fundamental intelligent systems in the Smart City, and one of the main directions to ensure sustainability and safety of the city transport system is the concept of smart vehicles. Herewith, all processes at all stages of the life cycle should be intellectualized. Since the production stage of the life cycle is one of the most important, the introduction of smart technologies (Industry 4.0) in automotive industry will allow not only to optimize the processes and improve product quality but also to establish favorable conditions for the subsequent intellectualization of the automotive service. The benefits of using smart transport in all fields of activities as well as intellectualization of the decision-making process by the example of the automotive industry enterprises are presented in this chapter
FedZero: Leveraging Renewable Excess Energy in Federated Learning
Federated Learning (FL) is an emerging machine learning technique that
enables distributed model training across data silos or edge devices without
data sharing. Yet, FL inevitably introduces inefficiencies compared to
centralized model training, which will further increase the already high energy
usage and associated carbon emissions of machine learning in the future.
Although the scheduling of workloads based on the availability of low-carbon
energy has received considerable attention in recent years, it has not yet been
investigated in the context of FL. However, FL is a highly promising use case
for carbon-aware computing, as training jobs constitute of energy-intensive
batch processes scheduled in geo-distributed environments.
We propose FedZero, a FL system that operates exclusively on renewable excess
energy and spare capacity of compute infrastructure to effectively reduce the
training's operational carbon emissions to zero. Based on energy and load
forecasts, FedZero leverages the spatio-temporal availability of excess energy
by cherry-picking clients for fast convergence and fair participation. Our
evaluation, based on real solar and load traces, shows that FedZero converges
considerably faster under the mentioned constraints than state-of-the-art
approaches, is highly scalable, and is robust against forecasting errors
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