Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Kepler: An Extensible System for Design and Execution of Scientific Workflows
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
VisTrails: visualization meets data management
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
Provenance for Computational Tasks: A Survey
Computing in Science and Engineering
ESCIENCE '08 Proceedings of the 2008 Fourth IEEE International Conference on eScience
On the Use of Cloud Computing for Scientific Workflows
ESCIENCE '08 Proceedings of the 2008 Fourth IEEE International Conference on eScience
A break in the clouds: towards a cloud definition
ACM SIGCOMM Computer Communication Review
Workflows and e-Science: An overview of workflow system features and capabilities
Future Generation Computer Systems
Adaptive Dimensioning of Cloud Data Centers
DASC '09 Proceedings of the 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing
Scientific workflows and clouds
Crossroads - Plugging Into the Cloud
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
SciPhy: a cloud-based workflow for phylogenetic analysis of drug targets in protozoan genomes
BSB'11 Proceedings of the 6th Brazilian conference on Advances in bioinformatics and computational biology
A Performance Evaluation of X-Ray Crystallography Scientific Workflow Using SciCumulus
CLOUD '11 Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing
Towards a Cost Model for Scheduling Scientific Workflows Activities in Cloud Environments
SERVICES '11 Proceedings of the 2011 IEEE World Congress on Services
An Evaluation of the Cost and Performance of Scientific Workflows on Amazon EC2
Journal of Grid Computing
Swift: A language for distributed parallel scripting
Parallel Computing
Dynamic scaling of call-stateful SIP services in the cloud
IFIP'12 Proceedings of the 11th international IFIP TC 6 conference on Networking - Volume Part I
Dynamic virtual machine scheduling in clouds for architectural shared resources
HotCloud'12 Proceedings of the 4th USENIX conference on Hot Topics in Cloud Ccomputing
An adaptive parallel execution strategy for cloud-based scientific workflows
Concurrency and Computation: Practice & Experience
A Provenance-based Adaptive Scheduling Heuristic for Parallel Scientific Workflows in Clouds
Journal of Grid Computing
Cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Capturing and querying workflow runtime provenance with PROV: a practical approach
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Hi-index | 0.00 |
Cloud computing has established itself as a solid computational model that allows for scientists to use a series of distributed virtual resources to execute a wide range of scientific experiments. In several cases, there is a demand for high performance in executing these experiments since many activities are data and computing intensive. Parallelism techniques are a key issue in this experimentation process. There are approaches that provide parallelism capabilities for scientific workflows in clouds. However, most of them rely on the scientist to dimension the virtual cluster to be instantiated. Dimensioning the virtual cluster to execute the workflow in parallel may be a hard task to accomplish, i.e. it is hard to define and adapt the optimal number of virtual machines to be used. Most systems follow this manual configuration of the scientist for the whole workflow execution, using adaptive techniques only in the presence of failures. Due to the huge number of options (virtual machine types) to configure a cloud environment, the configuration task commonly becomes impractical to be performed manually, and if it is not adjusted adaptively during the execution, it can impact negatively on workflow performance, or it can produce excessive increase in financial cost. This paper proposes a service called SciDim which is based on the use of a multi-objective cost function allied to genetic algorithms and provenance data to help determining an "ideal" initial configuration for the virtual cluster, under budget and deadline constraints set by the scientist