Task scheduling in the cloud computing paradigm poses a challenge for researchers as the workloads that come onto cloud platforms are dynamic and heterogeneous. Therefore, scheduling these heterogeneous tasks to the appropriate virtual resources is a huge challenge. The inappropriate assignment of tasks to virtual resources leads to the degradation of the quality of services and thereby leads to a violation of the SLA metrics, ultimately leading to the degradation of trust in the cloud provider by the cloud user. Therefore, to preserve trust in the cloud provider and to improve the scheduling process in the cloud paradigm, we propose an efficient task scheduling algorithm that considers the priorities of tasks as well as virtual machines, thereby scheduling tasks accurately to appropriate VMs. This scheduling algorithm is modeled using firefly optimization. The workload for this approach is considered by using fabricated datasets with different distributions and the real-time worklogs of HPC2N and NASA were considered. This algorithm was implemented by using a Cloudsim simulation environment and, finally, our proposed approach is compared over the baseline approaches of ACO, PSO, and the GA. The simulation results revealed that our proposed approach has shown a significant impact over the baseline approaches by minimizing the makespan, availability, success rate, and turnaround efficiency.
Effective workflow scheduling in cloud computing is still a challenging problem as incoming workflows to cloud console having variable task processing capacities and dependencies as they will arise from various heterogeneous resources. Ineffective scheduling of workflows to virtual resources in cloud environment leads to violations in service level agreements and high energy consumption, which impacts the quality of service of cloud provider. Many existing authors developed workflow scheduling algorithms addressing operational costs and makespan, but still, there is a provision to improve the scheduling process in cloud paradigm as it is an nondeterministic polynomial-hard problem. Therefore, in this research, a task-prioritized multiobjective workflow scheduling algorithm was developed by using cuckoo search algorithm to precisely map incoming workflows onto corresponding virtual resources. Extensive simulations were carried out on workflowsim using randomly generated workflows from simulator. For evaluating the efficacy of our proposed approach, we compared our proposed scheduling algorithm with existing approaches, i.e., Max–Min, first come first serve, minimum completion time, Min–Min, resource allocation security with efficient task scheduling in cloud computing-hybrid machine learning, and Round Robin. Our proposed approach is outperformed by minimizing energy consumption by 15% and reducing service level agreement violations by 22%.
Rapid growth has been occurred in the IT industry with the emergence of Cloud computing in terms of the resources provisioned to the users in a seamless and flexible way. Task Scheduling is a prodigious challenge in the Cloud Computing. It is difficult to schedule the continuously varying
requests to schedule on continuously varying resources. The existing approaches haven’t considered all the metrics while considering only the metrics like makespan and waiting time. In this paper, our focus is to formulate a Multi objective approach which is used to optimally map and
load balance the tasks in the cloud by calculating the task priority and VM priority based on the electricity price per unit cost while minimizing the makespan, migration time and the power cost in the datacenters. The proposed algorithm is modeled using the hybridized approach by combining
PSO and Cuckoo search algorithms. It is simulated on cloudsim simulator and it is compared against the basic ACO, GA, PSO and CS algorithms and our algorithm is outperformed against these basic algorithms with concerned parameters such as makespan, Migration time and the Total Power cost in
the datacenters.
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