相关论文: Cloud Energy Micro-Moment Data Classification: A P…

When deploying machine learning (ML) applications, the automated allocation of computing resources-commonly referred to as autoscaling-is crucial for maintaining a consistent inference time under fluctuating workloads. The objective is to…

With the advantages that cloud computing offers in terms of platform as a service, software as a service, and infrastructure as a service, data engineers and data scientists are able to leverage cloud computing for their ETL/ELT (extract,…

Energy management, and in particular its optimization, is one of the hot trends in the current days, both at the enterprise level (optimization of whole corporate/government buildings) and single citizens' homes. The current trend is to…

Today, deep learning optimization is primarily driven by research focused on achieving high inference accuracy and reducing latency. However, the energy efficiency aspect is often overlooked, possibly due to a lack of sustainability mindset…

High-Performance Computing (HPC) centers and cloud providers support an increasingly diverse set of applications on heterogenous hardware. As Artificial Intelligence (AI) and Machine Learning (ML) workloads have become an increasingly…

This paper presents a methodology for allocating energy consumption to multiple users of shared data center machines, infrastructure, and software. Google uses this methodology to provide carbon reporting data for enterprise customers of…

Cloud elasticity - the ability to use as much resources as needed at any given time - and low cost - a user pays only for the resources it consumes - represent solid incentives for many organizations to migrate some of their computational…

Investigations have been performed into using clustering methods in data mining time-series data from smart meters. The problem is to identify patterns and trends in energy usage profiles of commercial and industrial customers over 24-hour…

Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of uncertainty for climate modeling. Many climate models do not…

Hundreds of millions of network cameras have been installed throughout the world. Each is capable of providing a vast amount of real-time data. Analyzing the massive data generated by these cameras requires significant computational…

This paper discusses how usage patterns and preferences of inhabitants can be learned efficiently to allow smart homes to autonomously achieve energy savings. We propose a frequent sequential pattern mining algorithm suitable for real-life…

To reduce negative environmental impacts, power stations and energy grids need to optimize the resources required for power production. Thus, predicting the energy consumption of clients is becoming an important part of every energy…

This paper explores an effective machine learning approach to predict cloud market performance for cloud consumers, providers and investors based on social media. We identified a set of comprehensive subjective metrics that may affect cloud…

These days more companies are shifting towards using cloud environments to provide their services to their client. While it is easy to set up a cloud environment, it is equally important to monitor the system's runtime behaviour and…

The increasing deployment of large language models (LLMs) in natural language processing (NLP) tasks raises concerns about energy efficiency and sustainability. While prior research has largely focused on energy consumption during model…

By providing unprecedented access to computational resources, cloud computing has enabled rapid growth in technologies such as machine learning, the computational demands of which incur a high energy cost and a commensurate carbon…

The energy demand of modern cloud services, particularly those related to generative AI, is increasing at an unprecedented pace. To date, carbon-aware computing strategies have primarily focused on batch process scheduling or…

Artificial intelligence (AI) and Machine learning (ML) workloads are an increasingly larger share of the compute workloads in traditional High-Performance Computing (HPC) centers and commercial cloud systems. This has led to changes in…

Automated document classification is a trending topic in Natural Language Processing (NLP) due to the extensive growth in digital databases. However, a model that fits well for a specific classification task might perform weakly for another…

This study delves into the shift from centralized to decentralized approaches in the electricity industry, with a particular focus on how machine learning (ML) advancements play a crucial role in empowering renewable energy sources and…