A Heuristics-Based Cost Model for Scientific Workflow Scheduling in Cloud

Scientific Workflow Applications (SWFAs) can deliver collaborative tools useful to researchers in executing large and complex scientific processes. Particularly, Scheduling (SWFS) accelerates the computational procedures between available resources dependent workflow jobs based on researchers’ requirements. However, cost optimization is one of SWFS challenges handling massive complicated tasks requires determining an approximate (near-optimal) solution within polynomial time. Motivated by this, current work proposes a novel model effective solving this challenge. The proposed contains three main stages: (i) application, (ii) targeted environment, (iii) criteria. has been used optimize completion time (makespan) overall cloud computing for all considered scenarios research context. This will ultimately reduce service consumers. At same time, reducing positive impact profitability providers towards utilizing achieve competitive advantage over other providers. To evaluate effectiveness model, empirical comparison was conducted employing core types heuristic approaches, including Single-based (i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Invasive Weed (IWO)), Hybrid-based Heuristics Algorithms (HIWO)), Hyper-based Dynamic Hyper-Heuristic (DHHA)). Additionally, simulation-based implementation SIPHT SWFA considering different sizes datasets. provides efficient platform optimally schedule data-intensiveness computational-intensiveness SWFAs. results reveal that attained optimal Job total small dataset. In contrast, hybrid hyper-based approaches consistently achieved better medium-sized