Multi-Objective Scheduling for Scientific Workflow in Multicloud Environment
Abstract
It is becoming an increasingly promising paradigm to provide resources and services from multiple clouds. However, workflow scheduling reliability is also a critical concern and even the most important QoS metric (service quality). The proposed Multi-Objective Scheduling for Scientific Workflow in Multicloud Environment MOS algorithm is based on particle swarm optimization (PSO) technology and the corresponding coding strategy takes into account both the location of execution tasks and the order of data transmission tasks.
Multi-Objective Scheduling for Scientific Workflow in Multicloud Environment Extensive simulation experiments demonstrate the significant multi-objective performance improvement of the MOS algorithm over the CMOHEFT algorithm and the RANDOM algorithm based on real-world scientific workflow models.
System Configuration
Platform : cloud computing
Conclusion
We had seen on the basis of beyond study that algorithms were established according to user priority parameters. Parameters that enhance security issue, total cost issue, energy consumption issue, performance issue, multi-objective workflow QoS issue. But multi-cloud computing is still not widely used due to lack of service response time, total cloud resources cost, reduced energy consumption, reliability, availability and fault tolerance, etc. due to third party and server locations.
