Prediction of job characteristics for intelligent resource allocation in HPC systems: a survey and future directions
Z Hou, H Shen, X Zhou, J Gu, Y Wang… - Frontiers of Computer …, 2022 - Springer
Nowadays, high-performance computing (HPC) clusters are increasingly popular. Large
volumes of job logs recording many years of operation traces have been accumulated. In the …
volumes of job logs recording many years of operation traces have been accumulated. In the …
Search-based methods for multi-cloud configuration
Multi-cloud computing has become increasingly popular with enterprises looking to avoid
vendor lock-in. While most cloud providers offer similar functionality, they may differ …
vendor lock-in. While most cloud providers offer similar functionality, they may differ …
Selecting efficient cloud resources for hpc workloads
JR Brunetta, E Borin - Proceedings of the 12th IEEE/ACM International …, 2019 - dl.acm.org
Constant advances in CPU, storage, and network virtualization are enabling high-
performance computing (HPC) applications to be efficiently executed on cloud computing …
performance computing (HPC) applications to be efficiently executed on cloud computing …
Performance analysis of parallel composite service-based applications in clouds
X Li, L Pan, W Song, S Liu, X Meng - Future Generation Computer Systems, 2024 - Elsevier
When processing composite service application jobs containing parallel tasks, service
providers can optimize their quality of services (QoS) based on refined parallelism settings …
providers can optimize their quality of services (QoS) based on refined parallelism settings …
Towards a novel framework for automatic big data detection
Big data is a” relative” concept. It is the combination of data, application, and platform
properties. Recently, big data specific technologies have emerged, including software …
properties. Recently, big data specific technologies have emerged, including software …
[PDF][PDF] On the promise and challenges of foundation models for learning-based cloud systems management
Foundation models (FMs) are machine learning models that are trained broadly on large-
scale data and can be adapted to a set of downstream tasks via fine-tuning, few-shot …
scale data and can be adapted to a set of downstream tasks via fine-tuning, few-shot …
Performance prediction from simulation systems to physical systems using machine learning with transfer learning and scaling
A Mankodi, A Bhatt, B Chaudhury - … and Computation: Practice …, 2023 - Wiley Online Library
Selection from several computer systems with different hardware features resulting in
different software performance is a critical problem to solve. The problem becomes even …
different software performance is a critical problem to solve. The problem becomes even …
Self-tuning serverless task farming using proactive elasticity control
The cloud evolved into an attractive execution environment for parallel applications, which
make use of compute resources to speed up the computation of large problems in science …
make use of compute resources to speed up the computation of large problems in science …
Evaluation of neural network models for performance prediction of scientific applications
A Mankodi, A Bhatt, B Chaudhury - 2020 IEEE REGION 10 …, 2020 - ieeexplore.ieee.org
Performance prediction is an important and active research area. In particular, several
research efforts have built empirical models using machine learning algorithms for …
research efforts have built empirical models using machine learning algorithms for …
Containerised Application Profiling and Classification Using Benchmarks
Along with the rise of cloud and edge computing has come a plethora of solutions that
regard the deployment and operation of different types of applications in such environments …
regard the deployment and operation of different types of applications in such environments …