Fuzzy based Workflow Scheduling on Cloud Systems to Meet The Deadline of Real Time Tasks

As there is fast change in the design of computing systems and networking technologies, distributed computing is gaining prominence. Cloud computing being the form of distributed computing provides numbers of services to the user or to the large scale applications. Cloud computing is characterized by providing on demand services, resource pooling, rapid elasticity etc. Workflow scheduling (WFS) on cloud systems mainly focuses on task allocation to achieve the desired workload balancing by providing optimal utilization of available resources so as to meet quality of service (QOS) parameters. In a heterogeneous environment like cloud handling real time tasks poses challenges of VM scheduling, VM migration and load balancing to meet the deadlines. Implementation of real-time tasks poses a lot of problems due to the unpredictability of the tasks involved and due to the lack of complete task knowledge prior to the execution process. Fuzzy logic can be used in dynamic scheduling of these tasks based on the load information at various VMs processing units. Fuzzy logic uses the system's deadline miss ratio (DMR) and system throughput to calculate a value which provides a system performance metric used to drive the system to the desired level of performance.