Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing

Abstract

Workflow scheduling is one of the significant issues for scientific applications among virtual machine migration, database management, security, performance, fault tolerance, server consolidation, etc. In this paper, existing time-based scheduling algorithms, such as first come first serve (FCFS), min–min, max–min, and minimum completion time (MCT), along with dependency-based scheduling algorithm MaxChild have been considered. These time-based scheduling algorithms only compare the burst time of tasks. Based on the burst time, these schedulers, schedule the sub-tasks of the application on suitable virtual machines according to the scheduling criteria. During this process, not much attention was given to the proper utilization of the resources. A novel dependency and time-based scheduling algorithm is proposed that considers the parent to child (P2C) node dependencies, child to parent node dependencies, and the time of different tasks in the workflows. The proposed P2C algorithm emphasizes proper utilization of the resources and overcomes the limitations of these time-based schedulers. The scientific applications, such as CyberShake, Montage, Epigenomics, Inspiral, and SIPHT, are represented in terms of the workflow. The tasks can be represented as the nodes, and relationships between the tasks can be represented as the dependencies in the workflows. All the results have been validated by using the simulation-based environment created with the help of the WorkflowSim simulator for the cloud environment. It has been observed that the proposed approach outperforms the mentioned time and dependency-based scheduling algorithms in terms of the total execution time by efficiently utilizing the resources.

Resource provisioning model for executing realistic workload in heterogenous internet of things environment

“…The results show that the proposed model attains better result in reducing the energy when compared with the other workload execution methods. Prakash et al [20], they have proposed a model called as parent to child (P2C) which executes the workload in the given deadline. This work considers the node dependency of the workload.…”

A new optimization algorithm based on average and subtraction of the best and worst members of the population for solving various optimization problems

“…Stochastic optimization algorithms have shown an acceptable ability to effectively solve optimization problems by providing appropriate solutions. These algorithms can be employed in various issues such as cloud computing ( Prakash & Bala, 2014a , 2014b ; Prakash, Bawa & Garg, 2021 ), cross-platform applications ( Vassallo et al, 2019 ), engineering ( Dehghani et al, 2020b ), energy commitment ( Dehghani, Montazeri & Malik, 2019 ) and other optimization challenges in science fields. Stochastic optimization algorithms can be divided into four types, namely evolutionary, swarm, physics and game-based optimization algorithms with respect to the central idea in their design.…”

Time Effective Cloud Resource Scheduling Method for Data-Intensive Smart Systems

“…They further reviewed the applications of reinforcement highlighting the major challenges in this scenario. The multidependency scenario of resource scheduling is assessed by Prakash et al (2021). They proposed a parent to child node dependency-based scheduling algorithm emphasizing proper resource scheduling overcoming the limitations posed by time-based schedulers.…”