No abstract

Workflow scheduling and resource provisioning are two of the most critical issues in cloud computing. Developing an optimal workflow scheduling strategy in the heterogeneous cloud environment is extremely difficult due to its NP-complete nature. Various optimization algorithms have been used to schedule the workflow so that users can receive Quality of Service (QoS) from cloud service providers as well as service providers can achieve maximum gain but there is no such model that can simultaneously minimize execution time and cost while balancing the load among virtual machines in a heterogeneous environment using JAYA approach. In this article, we employed the hybrid JAYA algorithm to minimize the computation cost and completion time during workflow scheduling. We considered the heterogeneous cloud computing environment and made an effort to evenly distribute the load among the virtual machines. To achieve our goals, we used the Task Duplication Heterogeneous Earliest Finish Time (HEFT-TD) and Predict Earliest Finish Time (PEFT). The makespan is greatly shortened by HEFT-TD which is based on the Optimistic Cost Table . We used a greedy technique to distribute the workload among Virtual Machines (VMs) in a heterogeneous environment. Greedy approach assigns the upcoming task to a VM which have lowest load. In addition, we also considered performance variation, termination delay, and booting time of virtual machines to achieve our objectives in our proposed model. We used Montage, LIGO, Cybershake, and Epigenomics datasets to experimentally analyze the suggested model in order to validate the concept. Our meticulous experiments show that our hybrid approach outperforms other recent algorithms in minimizing the execution cost and makespan, such as the Cost Effective Genetic Algorithm (CEGA), Cost-effective Load-balanced Genetic Algorithm (CLGA), Cost effective Hybrid Genetic Algorithm (CHGA), and Artificial Bee Colony Algorithm (ABC).

Cloud computing plays a significant role in everyone’s lifestyle by snugly linking communities, information, and trades across the globe. Due to its NP-hard nature, recognizing the optimal solution for workflow scheduling in the cloud is a challenging area. We proposed a hybrid meta-heuristic cost-effective load-balanced approach to schedule workflow in a heterogeneous environment. Our model is based on a genetic algorithm integrated with predict earliest finish time (PEFT) to minimize makespan. Instead of assigning the task randomly to a virtual machine, we apply a greedy strategy that assigns the task to the lowest-loaded virtual machine. After completing the mutation operation, we verify the dependency constraint instead of each crossover operation, which yields a better outcome. The proposed model incorporates the virtual machine’s performance variance as well as acquisition delay, which concedes the minimum makespan and computing cost. One of the most astounding aspects of our cost-effective hybrid genetic algorithm (CHGA) is its capacity to anticipate by creating an optimistic cost table (OCT) while maintaining quadratic time complexity. Based on the results of our meticulous experiments on some real-world workflow benchmarks and comprehensive analysis of some recently successful scheduling algorithms, we concluded that the performance of our CHGA is melodious. CHGA is 14.58188%, 11.40224%, 11.75306%, and 9.78841% cheaper than standard Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Cost Effective Genetic Algorithm(CEGA), and Cost-Effective Load-balanced Genetic Algorithm (CLGA), respectively.