Cost-based scheduling of scientific workflow applications on utility grids

By Jia Yu, Rajkumar Buyya, Chen-Khong Tham. IEEE International Conference on e-Science, 2005

511 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/18d70400cac53b215570c24506fb771b9155b51c

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

1. Rajkumar Buyya
2. Marty A. Humphrey
3. Ming Mao
4. Jie Li
5. Ewa Deelman
6. Jia Yu
7. Sarbjeet Singh
8. Mala Kalra

In Information Systems

1. Rajkumar Buyya
2. Marty A. Humphrey
3. Ming Mao
4. Jie Li
5. Sarbjeet Singh
6. Mala Kalra
7. Ewa Deelman
8. Jia Yu

In Information Systems and Management

1. Ming Mao
2. Marty A. Humphrey
3. Rajkumar Buyya
4. Jie Li
5. Jia Yu
6. Sarbjeet Singh
7. Mala Kalra
8. Ewa Deelman

Citations in the corpus

• An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements (2009, background): “Buyya and Tham [25] propose a workflow scheduling algorithm for economy-driven or market-driven grids to minimize cost under the deadline constraint.”
• The cost of doing science on the cloud: The Montage example (2008, background): “Cost-based scheduling of scientific applications has also been addressed in [43].”
• Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms (2006, methodology): “The deadline assignment is based on our previous work [31].”
• HCOC: a cost optimization algorithm for workflow scheduling in hybrid clouds (2011, unknown): “In another work [35], Yu et.”
• Cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds (2012, background): “However, our problem is different from [12] and [13] in that we consider ensembles of workflows in IaaS clouds, which allow one to provision resources on a perhour billing model, rather than utility grids, which allow one to choose from a pool of existing resources with a per-job billing model.”
• A review of metaheuristic scheduling techniques in cloud computing (2015, methodology): “The proposed algorithm manages to reduce the cost by 10–20% when compared with Deadline-MDP algorithm [17].”
• Cloud auto-scaling with deadline and budget constraints (2010, methodology): “Therefore, this dissertation decides to extend two existing approaches, Greedy [49] and GAIN [48], as the baseline.”