Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization
MZ Hasan, H Al‐Rizzo - Concurrency and Computation …, 2020 - Wiley Online Library
Summary Internet of Things (IoT) is steadily growing in support of current and projected real‐
time distributed Internet applications in civilian and military applications, while Cloud …
time distributed Internet applications in civilian and military applications, while Cloud …
Reducing energy footprint in cloud computing: a study on the impact of clustering techniques and scheduling algorithms for scientific workflows
The concept of scientific workflow makes it possible to link and control different tasks to carry
out a complex treatment. The complicated workflow is generated by scientific distributed …
out a complex treatment. The complicated workflow is generated by scientific distributed …
A data-aware scheduling strategy for executing large-scale distributed workflows
Task scheduling is a crucial key component for the efficient execution of data-intensive
applications on distributed environments, by which many machines must be coordinated to …
applications on distributed environments, by which many machines must be coordinated to …
Block size estimation for data partitioning in HPC applications using machine learning techniques
The extensive use of HPC infrastructures and frameworks for running data-intensive
applications has led to a growing interest in data partitioning techniques and strategies. In …
applications has led to a growing interest in data partitioning techniques and strategies. In …
Convergence of HPC and Big Data in extreme-scale data analysis through the DCEx programming model
High-level programming models can help application developers to access and use
resources without the need to manage low-level architectural entities, as a parallel …
resources without the need to manage low-level architectural entities, as a parallel …
Exploiting machine learning for improving in-memory execution of data-intensive workflows on parallel machines
Workflows are largely used to orchestrate complex sets of operations required to handle and
process huge amounts of data. Parallel processing is often vital to reduce execution time …
process huge amounts of data. Parallel processing is often vital to reduce execution time …
DynDL: Scheduling data-locality-aware tasks with dynamic data transfer cost for multicore-server-based big data clusters
Featured Application This work is applicable to most state-of-the-art data-parallel
frameworks, such as Hadoop, Spark, Pregel, and Tensorflow, to improve task-scheduling …
frameworks, such as Hadoop, Spark, Pregel, and Tensorflow, to improve task-scheduling …
Network-aware task selection to reduce multi-application makespan in cloud
One new metric that plays a vital role in evaluating the cloud service is the multi-application
makespan. There are usually multiple applications without a deadline in the cloud, while the …
makespan. There are usually multiple applications without a deadline in the cloud, while the …
High-performance framework to analyze microarray data
F Marozzo, L Belcastro - Microarray Data Analysis, 2022 - Springer
Pharmacogenomics is an important research field that studies the impact of genetic variation
of patients on drug responses, looking for correlations between single nucleotide …
of patients on drug responses, looking for correlations between single nucleotide …
Malleability Techniques for HPC Systems
J Carretero, D Exposito, A Cascajo… - … Conference on Parallel …, 2022 - Springer
Abstract The current static usage model of HPC systems is becoming increasingly inefficient
due to the continuously growing complexity of system architectures, combined with the …
due to the continuously growing complexity of system architectures, combined with the …