Machine learning methods for reliable resource provisioning in edge-cloud computing: A survey
Large-scale software systems are currently designed as distributed entities and deployed in
cloud data centers. To overcome the limitations inherent to this type of deployment …
cloud data centers. To overcome the limitations inherent to this type of deployment …
BHyPreC: a novel Bi-LSTM based hybrid recurrent neural network model to predict the CPU workload of cloud virtual machine
With the advancement of cloud computing technologies, there is an ever-increasing demand
for the maximum utilization of cloud resources. It increases the computing power …
for the maximum utilization of cloud resources. It increases the computing power …
Characterizing temperature, power, and soft-error behaviors in data center systems: Insights, challenges, and opportunities
GPUs have become part of the mainstream high performance computing facilities that
increasingly require more computational power to simulate physical phenomena quickly and …
increasingly require more computational power to simulate physical phenomena quickly and …
Smartharvest: Harvesting idle cpus safely and efficiently in the cloud
We can increase the efficiency of public cloud datacenters by harvesting allocated but
temporarily idling CPU cores from customer virtual machines (VMs) to run batch or analytics …
temporarily idling CPU cores from customer virtual machines (VMs) to run batch or analytics …
SLA-aware and energy-efficient VM consolidation in cloud data centers using robust linear regression prediction model
L Li, J Dong, D Zuo, J Wu - IEEE Access, 2019 - ieeexplore.ieee.org
Virtual machine (VM) consolidation provides a promising approach to save energy and to
improve resource utilization in data centers. However, the aggressive consolidation of virtual …
improve resource utilization in data centers. However, the aggressive consolidation of virtual …
A large-scale study of soft-errors on GPUs in the field
Parallelism provided by the GPU architecture has enabled domain scientists to simulate
physical phenomena at a much faster rate and finer granularity than what was previously …
physical phenomena at a much faster rate and finer granularity than what was previously …
Machine learning models for GPU error prediction in a large scale HPC system
GPUs are widely deployed on large-scale HPC systems to provide powerful computational
capability for scientific applications from various domains. As those applications are …
capability for scientific applications from various domains. As those applications are …
Characterizing co-located workloads in alibaba cloud datacenters
Workload characteristics are vital for both data center operation and job scheduling in co-
located data centers, where online services and batch jobs are deployed on the same …
located data centers, where online services and batch jobs are deployed on the same …
Performance modeling and predictive scheduling for distributed stream data processing
In a distributed stream data processing system, an application is usually modeled using a
directed graph, in which each vertex corresponds to a data source or a processing unit, and …
directed graph, in which each vertex corresponds to a data source or a processing unit, and …
A joint model for IT operation series prediction and anomaly detection
Status prediction and anomaly detection are two fundamental tasks in automatic IT systems
monitoring. In this paper, a joint model Predictor & Anomaly Detector (PAD) is proposed to …
monitoring. In this paper, a joint model Predictor & Anomaly Detector (PAD) is proposed to …