Cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds
By Maciej Malawski, Gideon Juve, Ewa Deelman, J. Nabrzyski. International Conference for High Performance Computing, Networking, Storage and Analysis, 2012
536 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Information Systems, Information Systems and Management
View Paper: https://www.semanticscholar.org/paper/813e836347456fb2d61c8feb6a71b0a4165c5c6d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Marty A. Humphrey
- Ming Mao
- Jie Li
- Amir Salman Avestimehr
- Amirhossein Reisizadeh
- Maria Alejandra Rodriguez
- Ramtin Pedarsani
- Rajkumar Buyya
In Information Systems
- Marty A. Humphrey
- Ming Mao
- Maria Alejandra Rodriguez
- Jie Li
- Rajkumar Buyya
- Amirhossein Reisizadeh
- Ramtin Pedarsani
- Saurav Prakash
In Information Systems and Management
- Marty A. Humphrey
- Ming Mao
- Jie Li
- Rajkumar Buyya
- Maria Alejandra Rodriguez
- Patrick Valduriez
- Esther Pacitti
- Marta Mattoso
Citations in the corpus
- A Survey of Data-Intensive Scientific Workflow Management (2015, methodology): “Since we can create VMs dynamically in the cloud environment, some methods [33, 48, 76, 92, 97, 108, 109] are proposed to make a trade-off between parallelization and other constraints, e.”
- Deadline Based Resource Provisioningand Scheduling Algorithm for Scientific Workflows on Clouds (2014, background): “Another recent work on workflow ensemble developed for clouds is presented by Malawski et al. [13].”
- Cloud auto-scaling with deadline and budget constraints (2010, methodology): “It is a more reasonable assumption from the service provider perspective because they are targeting the overall application performance, instead of a single job or a set of jobs that is known in advance [21][54][55].”
- Coded computation over heterogeneous clusters (2017, background): “Different cloud computing platforms employ different pricing policies, and these need to be taken into account for developing efficient task allocation and execution algorithms [40]–[44].”
- Coded Computation Over Heterogeneous Clusters (2019, background): “Different cloud computing platforms employ different pricing policies, and these need to be taken into account for developing efficient task allocation and execution algorithms [40]–[44].”
- Evolutionary Multi-Objective Workflow Scheduling in Cloud (2016, unknown): “F”