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Cloud computing offers Information and Communication Technologies (ICT) user's new aspects of ease of services through the Internet. Cloud is a gradual evolution of distributed, cluster, grid and utility computing. Cloud environment attracts the myriads of service providers for development and deployment of their application because it provides ubiquitous and on-demand access to shared resources. The majority of cloud applications like scientific applications are represented as workflow. Workflow is related to the automation of a process that represented by task and data/control information is passed between dependent tasks based on defined set of principles, execution of these tasks require different resources with different Quality of Service parameters. Workflow scheduling is most important concerned in a cloud computing environment because it depends on optimization objectives (like makespan, cost, reliability, security, energy consumption and energy cost, load balancing etc.) of scientific applications. In this review paper, we describe various scientific applications, taxonomy and comprehensive survey of existing workflow scheduling algorithms. We also, classify these algorithms based on nature of the algorithm, optimization model used, optimization objectives and tool used.

Recent years, Cloud Computing attracts the service provider for running applications on large data centers due to their highly available hardware, on-demand provision, and pay-as-you-go concepts. It provides the huge amount of computing power by leverages the virtualization. A virtualization technology is a promising approach to consolidating multiple Virtual machines (VM) onto a minimum number of servers. Dynamic VM provisioning, VM consolidation, and switching servers on and off as required, through all these techniques data centers can sustain the required Quality-of-Service (QoS) while accomplishing higher server utilization and energy efficiency. In our proposed work we can handle the inter-relationship between energy consumption, the number of VM migrations, SLA violation, and performance of the application. The proposed approaches handle the over-utilized servers by migrating the most appropriate VM to the suitable destination server. For this, we propose the VM selection and VM placement approaches. For overload detection, we used the exponential smoothing technique. To implement these approaches, we used cloudsim simulator. Our results show that in energy consumption can be reduced up to 42.3% and the number of VM migration is reduced as well as performances are improved in all approaches in different cases w.r.t. MAD_MU.