A Cost-Effective Deadline-Constrained Dynamic Scheduling Algorithm for Scientific Workflows in a Cloud Environment
Abstract
Execution of a
A Cost-Effective Deadline-Constrained Dynamic Scheduling Algorithm for Scientific Workflows in a Cloud Environment makes appropriate provisioning and scheduling decisions so that the overall cost of execution is minimized while meeting a user-defined deadline. To minimize the overall cost of execution dynamically. To schedule scientific workflows with time constraints. Cloud computing, a distributed computing paradigm, enables the delivery of IT resources. Workflow scheduling is one of cloud computing’s most challenging problems. Although workflow scheduling on distributed systems such as grids and clusters has been extensively studied, these solutions are not viable for a cloud environment. It’s because, in two major ways, a cloud environment differs from other distributed environment: on-demand resource provisioning and pay-as-you-go pricing model. Thus, it is necessary to develop the true benefits of workflow orchestration on new approaches to cloud resources that can capitalize on the advantages and address the specific challenges of a cloud environment.
System Configuration
H/W System Configuration
Speed : 1.1 GHz
RAM : 256 MB(min)
Hard Disk : 20 GB
Floppy Drive : 1.44 MB
Key Board : Standard Windows Keyboard
Mouse : Two or Three Button Mouse
Monitor : SVGA
S/W System Configuration
Platform : cloud computing
Operating system : Windows Xp,7,
Server : WAMP/Apache
Working on : Browser Like Firefox, IE
Conclusion
Cloud computing environment offers tremendous opportunities and alternatives to execute large-scale scientific workflows. Executing A Cost-Effective Deadline-Constrained Dynamic Scheduling Algorithm for Scientific Workflows in a Cloud Environment scientific applications in the cloud involves making appropriate provisioning and scheduling decisions so that the overall cost of execution is minimized while meeting a user-defined deadline. The simulation experiments conducted on four well-known workflows show that, compared to the other state of the art heuristics, IC-PCP, RCT and RTC, the proposed algorithm displays the highest hit rate in meeting the deadline. It also exploits the slack time available with relaxed deadlines to produce cheaper schedules with lower execution costs. The proposed algorithm JIT-C generates schedules with an average of 34 percent lower costs compared to the best performing baseline algorithm RTC for the similar purpose.
