Dynamic workflow scheduling in cloud computing environment ased on optimization techniques

Abstract

In the recent years of information explosion across industry and academia, newlineresulted in challenges of receiving, storing, managing, scheduled processing, newlineanalyzing of the data and interpreting the information out of it. Latest newlinetechnological improvements like Cloud Computing, Distributed File System, newlineParallel Computing and In-Memory technologies address the challenges which big newlinedata has brought in. Based on the above mentioned technologies, this research newlinepresents workflow scheduling in cloud computing environment. The advanced newlinedevelopment in virtualization technologies and cloud computing serve the way for newlinedistributing computing resources for existing resource pools based on demand and newlinescientific computing. Cloud computing provides a pool of abstracted, virtualized newlineresources, including computing power, storage, platforms and software newlineapplications over the internet based on users demand. newlineDue to its many benefits such as elastically scalable resource provisioning newlineand cost-effectiveness, cloud computing is being accepted by more and more newlineusers, day by day. These days many scientists and researchers, are moving to newlineCloud computing for achieving High Performance Computing (HPC). Big Data newlinehas to be stored and processed efficiently to extract knowledge and information newlinefrom them. The data volume is scaling faster than computing resources. Hence newlinemanaging large datasets and processing information out of them is a challenging newlinetask. The larger the dataset longer is the time taken for computation. Further the newlineworkflow too has grown complex, having numerous subtasks, which needs to be newlineexecuted either in sequence or in parallel. Also the cloud computing environment newlinehas numerous combination of resources as resource pools. This further newlinecomplicates, assigning the workflow to the cloud resources and scheduling of the newlineassigned tasks with various consideration like minimum makespan, maximum newlineresource utilization and effective deadline hit along with other quality of service newlinerequirements defined by the customer