Mobile edge computing with the near-data processing paradigm can support applications
requiring low latency and high computing capability, where energy cost is a significant part …

Recent rapid development in deep learning (DL) applications generates harsh requirements
for DL inference services provided by GPU servers. On one hand, a high volume of different …

This article introduces a probabilistic model for managing power dynamically (DPM) in a
data center. The model involves switching servers on and off, while considering both the …

Today's data centers often need to run various machine learning (ML) applications with
stringent SLO (Service-Level Objective) requirements, such as inference latency. To that …

In this paper we demonstrate that it is possible to obtain considerable improvement of
performance and energy aware metrics for training of deep neural networks using a modern …

Managing the performance of online web services in cloud data centers while optimizing
resource allocation and power consumption is a multifaceted challenge. Often, resource and …