Teaching learning‐based optimization (TLBO) was proposed by Rao to solve optimization problems. It is based on the theory of teaching‐learning mechanism. Although it performs well in unimodal problems yet its performance is not good in multimodal problems. To further improve this algorithm's performance and make it suitable for both unimodal problems and multimodal problems, we made some major changes in the theory and the algorithm's operators. The proposed algorithm is able to capture diverse optimal solutions in less number of iterations and is very good for solving multimodal problems. This newly created variant of TLBO is named generalized TLBO (GTLBO). The performance of GTLBO is tested on CEC−06, 2019 benchmark functions and other 15 classical benchmark functions, and it is found that the proposed algorithm is performing better comparatively. Then it is simulated for solving the workflow scheduling problem in CloudSim. Standard scientific workflow applications as Montage, Epigenomics, Sipht, and a sample workflow are used as dataset to test algorithms' performance in cloud environments. Our proposed approach, GTLBO, provides the proper distribution of workloads and offers minimal execution‐cost for the workflow applications. Results reflect the supremacy of the proposed algorithm GTLBO comparatively.
Workflow scheduling is an important way to manage the execution of a workflow. It introduces the concept of providing suitable resources to workflow tasks in order to finish workflow execution and meet the user's objectives. However, the problem becomes more complex when scheduling must balance two conflicting objectives, such as minimizing execution cost and maximizing load across all computing resources. A workflow has many interdependent tasks, and the cloud datacenter has many computing resources to execute the workflow. There can be an asymptotically infinite number of mappings of tasks-to-computing resources. Every mapping produces different execution costs with different workloads on computing resources. The main challenge for the researcher is to develop an intelligent scheduling algorithm to identify an optimal mapping that produces minimal execution cost with fair workload distribution on resources. We developed a novel meta-heuristic algorithm named Investment-Based Optimization (IBO) to identify an optimal mapping. The IBO algorithm was first tested on optimization benchmark functions and then simulated in CloudSim to see its performance for scheduling workflows. Finally, IBO was tested over Montage, Epigenomics, Sipht, and a sample workflow, and it was found that IBO reduces execution costs by 33%, 16%, 16.36%, and 20% with a fair workload distribution.
SummaryTeaching‐learning‐based optimization (TLBO) algorithm is a population‐based meta‐heuristic algorithm that was created to solve single‐objective optimization problems. The teaching‐learning mechanism of a classroom inspires it. TLBO suffers from weak exploration. As a result, its performance is not good for solving multimodal problems. To turn TLBO into a tool for solving multimodal problems and maintaining good diversity, we made significant modifications into the learning process of the fundamental TLBO. The proposed algorithm produces more diverse solutions and works better for solving multimodal problems. This newly created variant of TLBO is called “Intelligent‐Teaching‐Learning‐Based Optimization (I‐TLBO) algorithm.” I‐TLBO's performance is evaluated against the most recent standard benchmark function, CEC‐06, 2019, and it is discovered that I‐TLBO outperforms the other algorithms. After that, I‐TLBO was applied for flowtime‐aware‐cost minimization of the workflow executions in cloud datacenter. To solve these scheduling problems, I‐TLBO and other metaheuristic algorithms are simulated in CloudSim and tested over scientific workflows such as Inspiral, Montage, SIPHT, sample, Cybershake, and Epigenomics workflows. Finally, it is found that I‐TLBO reduces flowtime and cost both by 28.48%, 11.30%, 17.64%, 13.22%, 11.45%, and 14.71% in comparison to the second best performing algorithm while executing the standard workflow in cloud.
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