Workflow scheduling in cloud environment using a novel metaheuristic optimization algorithm

Abstract

Summary Workflow scheduling is the most focused research issue in the on‐demand clouds where the user satisfaction like cost and bandwidth is more difficult. Several research works have been conducted earlier towards performing reliable workflow scheduling with the aim of reducing cost or execution time. However, those works lack to produce better result by compromising any attributes for attaining the goal. The existing work lacks from the security where the tasks might get corrupted during execution. To resolve this problem, an Enhanced Artificial Fish Swarm Algorithm (EAFSA)‐based IaaS Cloud Partial Critical Path (IC‐PCP) Replication and Hyper Elliptic Curve Cryptography (EAFSAIPR with HECC) is proposed. The main goal is to perform better workflow scheduling which can complete the task execution before deadline given by the users. This is done by predicting the early start time and latest finish time using EAFSA algorithm, so that task replication can be made to meet the soft deadline constraint. The task authentication is done efficiently using HEEC algorithm, so that corruption from malicious users can be avoided. The task replication is done securely using the cryptographic algorithm. The proposed EAFSAIPR with HECC algorithm uses idle time of provisioned resources to replicate workflow tasks optimally. The proposed EAFSAIPR algorithm scheduler focused to ensure the lowest cost while serving a deadline set by the user. The experimental results show that the scheduler can find good schedules of deadlines being met and reduces the total execution time of applications as the budget available for replication increases.

Transforming data‐intensive workflows: A cutting‐edge multi‐layer security and quality aware security framework

“…where C e and G a defines capacitance load and active gate percentage, respectively. Equation ( 11) is a constraint with Equations ( 12)- (15). The total computational capability of VMs with dynamic varying incoming workflow task load M l is expressed through the following equation…”

Redefining the learning mechanism in teaching‐learning‐based optimization and its applications for flowtime‐aware‐cost minimizing of the workflow in cloud

“…Authors in References 33‐42 used cost minimization as scheduling criteria to schedule the workflow with no constraints. Authors in References 43‐47 considered the makespan as scheduling criteria for the workflow execution. Many authors 48‐56 have employed a linear mix of makespan and workflow cost without addressing budget or schedule constraints.…”

A Unified Mechanism for Cloud Scheduling of Scientific Workflows

“…Compute intensive applications such as scientific workflows believe in dynamic scheduling as their compute requirements do change with time. This is witnessed by many techniques and studies proposed in recent years [23][24][25][26][27][28]. Scheduling algorithms could use heuristic and metaheuristic approaches and, therefore, two well-known categories of the heuristic and meta-heuristic algorithms do exist in the literature.…”

“…According to Singh et al [40], they include ant colony optimization, particle swarm optimization, genetic algorithm, cat swarm optimization and artificial bee colony. Ramathilagam and Kandasamy [25] and Toussi and Mahmoud [24] presented novel optimization techniques for workflow scheduling in clouds. The latter technique (presented in [24]) used divide and conquer approach to achieve deadline constrained cost optimization.…”