English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Infrastructure as a Service model of cloud computing is a desirable platformfor the execution of cost and deadline constrained workflow applications as theelasticity of cloud computing allows large-scale complex scientific workflowapplications to scale dynamically according to their deadline requirements.However, scheduling of these multitask workflow jobs in a distributed computingenvironment is a computationally hard multi-objective combinatorialoptimization problem. The critical challenge is to schedule the workflow taskswhilst meeting user quality of service (QoS) requirements and the application'sdeadline. The existing research work not only fails to address this challengebut also do not incorporate the basic principles of elasticity andheterogeneity of computing resources in cloud environment. In this paper, wepropose a resource provisioning and scheduling algorithm to schedule theworkflow applications on IaaS clouds to meet application deadline constraintswhile optimizing the execution cost. The proposed algorithm is based on thenature-inspired population based Intelligent Water Drop (IWD) optimizationalgorithm. The experimental results in the simulated environment of CloudSimwith four real-world workflow applications demonstrates that IWD algorithmschedules workflow tasks with optimized cost within the specified deadlines.Moreover, the IWD algorithm converges fast to near optimal solution.

Resource Provisioning and Scheduling Algorithm for Meeting Cost and Deadline-Constraints of Scientific Workflows in IaaS Clouds