Multi Scheduling Reactive Resource Sharing for Dynamic Dataflow in Cloud Environment

In recent years cloud parallel data processing has emerged to be one of the killer applications for Infrastructure-as-a-Service (IaaS) clouds. Major Cloud computing companies have started to integrate frameworks for parallel data processing in their product portfolio making it easy for customers to access these services and to deploy their programs. The growing computing demand from industry and academia has lead to excessive power consumption which not only impacting long term sustainability of Cloud like infrastructures in terms of energy cost but also from environmental perspective. The problem can be addressed by replacing with more energy efficient infrastructures, but the process of switching to new infrastructure is not only costly but also time consuming. Cloud being consist of several extended centers under different administrative domain, make problem more difficult. Thus, for reduction in energy consumption, the proposed work address the challenge by effectively distributing compute-intensive parallel applications on cloud. This proposes a Meta scheduling algorithm which exploits the heterogeneous nature of Cloud to achieve reduction in energy consumption.