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An effective meta-heuristic based multi-objective hybrid optimization method for workflow scheduling in cloud computing environment

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Abstract

Cloud computing is an emerging distributed computing model that offers computational capability over internet. Cloud provides a huge level collection of powerful and scalable computational resources for computation and data-intensive large scale workflow deployment. For business as well as scientific applications, optimal scheduling of workflow is emerged as a major concern. Optimization of scheduling process leads to the reduction of execution time, cost, etc. So, this paper presents an enhanced recent ant-lion optimization (ALO) algorithm hybridized with popular particle swarm optimization (PSO) algorithm to optimize a workflow scheduling specifically for cloud. A security approach called Data Encryption Standard (DES) is used for encoding the cloud information while scheduling is carried out. The research aims to contribute an enhanced workflow scheduling more safely than the existing frameworks. Enhancement procedures are evaluated in terms of cost, load, and makespan. The simulation procedures are done by utilizing the CloudSim tool. The proposed hybrid optimization results contrasted with well-known existing approaches. The existing round-robin (RR), ALO and PSO methods are selected to compare and identify the potency of the proposed system. The outcomes indicated that the proposed technique minimizes the cost by 9.8% of GA-PSO, 10% of PSO, 20% of ALO, 30% of RR and 12% of GA. Load balancing and makespan of the proposed method reduces by 8% than GA-PSO, 10% than ALO, 20% than PSO, 35% than RR and 45% than GA. The energy consumption and reliability performance are also reasonably well.

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Abbreviations

IaaS:

Infrastructure as a Service

VM:

Virtual machine

CSP:

Cloud Service Provider

QoS:

Quality of Service

GSA:

Gravitational Search Algorithm

HEFT:

Heterogeneous Earliest Finish Time

PEFT:

Predict Earliest Finish Time algorithm

MOGA:

Multi-objective Genetic Algorithm

MOPSO:

Multi-objective Particle Swarm Optimization Algorithm

CPM:

Cost Prediction Matrix

SCPS:

Secured Cost Prediction based scheduling

DAG:

Direct Acyclic Graph

MOP:

Multi-objective Optimization Problem

DES:

Data Encryption Standard

PSO:

Particle Swarm Optimization

ALO:

Ant-lion Optimization

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Authors and Affiliations

  1. Cochin University of Science and Technology, Kochi, Kerala, India

    Jabir Kakkottakath Valappil Thekkepuryil, David Peter Suseelan & Preetha Mathew Keerikkattil

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  1. Jabir Kakkottakath Valappil Thekkepuryil
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  2. David Peter Suseelan
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  3. Preetha Mathew Keerikkattil
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Correspondence to Jabir Kakkottakath Valappil Thekkepuryil.

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Kakkottakath Valappil Thekkepuryil, J., Suseelan, D.P. & Keerikkattil, P.M. An effective meta-heuristic based multi-objective hybrid optimization method for workflow scheduling in cloud computing environment. Cluster Comput 24, 2367–2384 (2021). https://doi.org/10.1007/s10586-021-03269-5

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