Tails in the cloud: a survey and taxonomy of straggler management within large-scale cloud data centres
Cloud computing systems are splitting compute-and data-intensive jobs into smaller tasks to
execute them in a parallel manner using clusters to improve execution time. However, such …
execute them in a parallel manner using clusters to improve execution time. However, such …
Multi-source distributed system data for ai-powered analytics
S Nedelkoski, J Bogatinovski, AK Mandapati… - Service-Oriented and …, 2020 - Springer
The emerging field of Artificial Intelligence for IT Operations (AIOps) utilizes monitoring data,
big data platforms, and machine learning, to automate operations and maintenance (O&M) …
big data platforms, and machine learning, to automate operations and maintenance (O&M) …
Detecting straggler MapReduce tasks in big data processing infrastructure by neural network
Straggler task detection is one of the main challenges in applying MapReduce for
parallelizing and distributing large-scale data processing. It is defined as detecting running …
parallelizing and distributing large-scale data processing. It is defined as detecting running …
CERES: Container-based elastic resource management system for mixed workloads
It is common to deploy multiple workloads in one cluster to achieve high resource utilization,
which tends to bring more resource contentions and performance interferences. If the …
which tends to bring more resource contentions and performance interferences. If the …
Terms: Task management policies to achieve high performance for mixed workloads using surplus resources
Resource contentions and performance interferences can lead to workload performance
degradation in mixed-workload deployment clusters. Previous work guarantees the resource …
degradation in mixed-workload deployment clusters. Previous work guarantees the resource …
GPU cluster dynamics: insights from Alibaba's 2023 trace release
A Siavashi, M Momtazpour - Computing, 2025 - Springer
In this paper, we present a comprehensive analysis of GPU cluster traces from Alibaba,
released in 2023, focusing on understanding the detailed settings of nodes and pods and …
released in 2023, focusing on understanding the detailed settings of nodes and pods and …
Identifying Performance Bottleneck in Shared In-Network Aggregation during Distributed Training
As the emergence of recently popular large language model, distributed training (DT)
optimizes the performance via using different parallelization strategies, resource schedulers …
optimizes the performance via using different parallelization strategies, resource schedulers …
Hiperjobviz: Visualizing resource allocations in high-performance computing center via multivariate health-status data
Scheduling, visualizing, and balancing resource allocations in High-Performance
Computing Centers are complicated tasks due to a large amount of data and the dynamic …
Computing Centers are complicated tasks due to a large amount of data and the dynamic …
Detection of stragglers and optimal rescheduling of slow running tasks in big data environment using LFCSO-LVQ classifier and enhanced PSO algorithm
This paper plans to implement intelligent techniques in finding straggler tasks along with
speculating their way of execution. Here, the LFCSO-LVQ is proposed to effectively identify …
speculating their way of execution. Here, the LFCSO-LVQ is proposed to effectively identify …
Detecting last-level cache contention in workload colocation with meta learning
H Shen, C Li - 2019 IEEE International Symposium on …, 2019 - ieeexplore.ieee.org
While workload colocation improves cluster utilization in cloud environments, it introduces
performance-impacting contentions on unmanaged resources. We address the problem of …
performance-impacting contentions on unmanaged resources. We address the problem of …