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Biomedical workflow applications' necessities have increased with the progress of the biotechnology industry and it involves large volumes of data which require large-scale computation. Due to the upcoming data deluge of biomedical data, processing and scheduling of the same in computing resources have presented many challenges. A biomedical workflow application requires heterogeneous resources for execution. Thus, it utilizes the advantage of cloud computing, as a cloud provides scalable high-performance computing resources. Scheduling of these workflows in the cloud is a crucial problem for which the proposed solution contributes greatly towards execution improvement. The proposed algorithm provides optimal and effective utilization of computational resources by novel scheduling of algorithms. Least Execution Time Cloud Workflow Scheduling (LETCWS) and Revised Heterogeneous Earliest Finish Time (RHEFT) algorithms are proposed and implemented in two distinct levels to schedule the biomedical workflow applications in such a way that it reduces the execution time while reducing the cost. The proposed algorithms are evaluated using the Workflowsim tool with real-world biomedical workflow applications. Experimental results illustrate the performance of scheduling workflow applications over the other existing approaches. The result shows that the proposed algorithm achieves better performance in terms of execution time and cost. [ABSTRACT FROM AUTHOR]

Cloud computing offers utility-based IT services on-demand to the users on a pay-per-use-basis. The cloud centers consist of physical machines (PMs) with virtual machines (VMs). These data centers consume a large amount of energy due to the improper resource utilization and lack of efficient scheduling algorithms to perform the task-resource mapping. These issues lead to huge energy consumption along with high maintenance costs and carbon emissions. In this paper, a Power Efficient Scheduling and VM Consolidation (PESVMC) algorithm is proposed to address these issues and the associated challenges. The numerous existing research works concentrated on the application of energy management techniques to hardware level support for the reduction of energy consumption. The proposed algorithm emphasizes on the software level by taking the flexibility of the virtualization technology and it consists of two phases, VM Scheduling phase, and VM Consolidation phase. In the Scheduling phase, the tasks with maximum runtime are allocated to VM, which is expected to consume minimal energy. In the VM Consolidation phase, overloaded and underloaded hosts are determined based on the double-threshold scheme. Further, Live Migration technique is applied for migrating the VMs from over-utilized or underutilized hosts to other hosts with the normal utilization. A power efficient utilization factor is introduced to determine the underloaded hosts. This utilization factor is proven to reduce the number of migrations, which can cause additional energy consumption. Energy efficient Scheduling combined with VM Consolidation is successful in maximizing the resource utilization and minimizing the energy consumption. The experimental evaluation is performed using WorkflowSim and the proposed algorithm achieves significant energy conservation and resource utilization. [ABSTRACT FROM AUTHOR]