相关论文: Carbon-Aware Mapping and Scheduling for Deadline-C…
In this paper, we study the peak-aware energy scheduling problem using the competitive framework with machine learning prediction. With the uncertainty of energy demand as the fundamental challenge, the goal is to schedule the energy output…
The growing reliance on large-scale data centers to run resource-intensive workloads has significantly increased the global carbon footprint, underscoring the need for sustainable computing solutions. While container orchestration platforms…
Provisioning dynamic machine learning (ML) inference as a service for artificial intelligence (AI) applications of edge devices faces many challenges, including the trade-off among accuracy loss, carbon emission, and unknown future costs.…
An important goal of modern scheduling systems is to efficiently manage power usage. In energy-efficient scheduling, the operating system controls the speed at which a machine is processing jobs with the dual objective of minimizing energy…
Saving energy is an important issue for cloud providers to reduce energy cost in a data center. With the increasing popularity of cloud computing, it is time to examine various energy reduction methods for which energy consumption could be…
Information and communication technologies account for a growing portion of global environmental impacts. While emerging technologies, such as emerging non-volatile memories (eNVM), offer a promising solution to energy efficient computing,…
Given recent algorithm, software, and hardware innovation, computing has enabled a plethora of new applications. As computing becomes increasingly ubiquitous, however, so does its environmental impact. This paper brings the issue to the…
In this work, a heterogeneous set of wireless devices sharing a common access point collaborates to perform a set of tasks. Using the Map-Reduce distributed computing framework, the tasks are optimally distributed amongst the nodes with the…
The paper considers single-machine scheduling problems with a non-renewable resource. In this setting, we are given a set jobs, each of which is characterized by a processing time, a weight, and the job also has some resource requirement.…
The transformation of smart mobility is unprecedented--Autonomous, shared and electric connected vehicles, along with the urgent need to meet ambitious net-zero targets by shifting to low-carbon transport modalities result in new traffic…
Recurring industrial analytics and machine-learning workflows are becoming a major computational burden in modern engineering practice. Large parametric database generation, scheduled model retraining, repeated evaluation pipelines, and…
The exponential growth of AI has created unprecedented demand for computational resources, pushing chip designs to the limit while simultaneously escalating the environmental footprint of computing. As the industry transitions toward…
The exponential growth in demand for digital services drives massive datacenter energy consumption and negative environmental impacts. Promoting sustainable solutions to pressing energy and digital infrastructure challenges is crucial.…
Task graphs provide a simple way to describe scientific workflows (sets of tasks with dependencies) that can be executed on both HPC clusters and in the cloud. An important aspect of executing such graphs is the used scheduling algorithm.…
Containers are becoming a popular workload deployment mechanism in modern distributed systems. However, there are limited software-based methods (hardware-based methods are expensive requiring hardware level changes) for obtaining the power…
Today's cloud data centers are often distributed geographically to provide robust data services. But these geo-distributed data centers (GDDCs) have a significant associated environmental impact due to their increasing carbon emissions and…
Data center operators are typically faced with three significant problems when running their data centers, i.e., rising electricity bills, growing carbon footprints and unexpected power outages. To mitigate these issues, running data…
The growing demand for multi-DNN workloads with unpredictable task arrival times has highlighted the need for interruptible scheduling on edge accelerators. However, existing preemptive frameworks typically assume known task arrival times…
The rapid advancement of AI, particularly large language models (LLMs), has raised significant concerns about the energy use and carbon emissions associated with model training and inference. However, existing tools for measuring and…
The rapid advancement of Artificial Intelligence (AI) has created unprecedented demands for computational power, yet methods for evaluating the performance, efficiency, and environmental impact of deployed models remain fragmented. Current…