Multi Objective Task Scheduling in Cloud Computing Using Cat Swarm Optimization Algorithm

Mangalampalli, Sudheer; Swain, S. K.; Mangalampalli, Vamsi Krishna

doi:10.1007/s13369-021-06076-7

Cited by 35 publications

(14 citation statements)

References 15 publications

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“…From the results, it was proved that PSO-RADL outperforms over the existing baseline algorithms for the parameters, i.e., the makespan, resource utilization, task rejection, penalty, and total cost. In [ 9 ], the authors focused on formulating a task scheduling approach using CSO and based on the behavior of cats, it was proposed and aimed at addressing the parameters, i.e., the makespan, total power cost at datacenters, migration time, and energy consumption. The scheduling model implemented on Cloudsim and two types of workloads are used for checking the efficacy of the algorithm, i.e., the random workload and real time worklogs.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization

Mangalampalli

Karri

Elngar

2023

Sensors

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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.

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Section: Related Workmentioning

confidence: 99%

An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization

Mangalampalli

Karri

Elngar

2023

Sensors

Self Cite

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“…Mangalampalli et al [34] introduced the cat swarm optimization algorithm (CSOA), which addresses the parameters makespan, migration time, energy consumption, and total power cost at data centers. Te implementation was performed using the CloudSim simulator, and the input to the algorithm was randomly generated from CloudSim for the total energy cost; the parallel workloads of HPC2N and NASA were used as the input of the algorithm.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Gravitational Emulation Local Search-Based Algorithm for Task Scheduling in Cloud Computing

Praveen¹,

Ghasempoor

Shahabi

et al. 2023

Mathematical Problems in Engineering

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The flexibility of cloud computing to provide a dynamic and adaptable infrastructure in the context of information technology and service quality has made it one of the most challenging issues in the computer industry. Task scheduling is a major challenge in cloud computing. Scheduling tasks so that they may be processed by the most effective cloud network resources has been identified as a critical challenge for maximizing cloud computing’s performance. Due to the complexity of the issue and the size of the search space, random search techniques are often used to find a solution. Several algorithms have been offered as possible solutions to this issue. In this study, we employ a combination of the genetic algorithm (GA) and the gravitational emulation local search (GELS) algorithm to overcome the task scheduling issue in cloud computing. GA and the particle swarm optimization (PSO) algorithms are compared to the suggested algorithm to demonstrate its efficacy. The suggested algorithm outperforms the GA and PSO, as shown by the experiments.

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“…Mangalampalli et al [10] had suggested that the virtual resources present in the cloud needs to be intelligently provisioned for optimizing the entire scheduling activity. This could be realized by minimizing the makespan and maximizing the utilization of resources present in the cloud.…”

Section: Related Workmentioning

confidence: 99%

“…The projection components x and y shown in Eqs. (10) and ( 11) respectively get reduced continuously, with the altitude component z, together assist the hunting policy. The radius r in every spiral's motion makes the exploitation phase crispier.…”

Section: Hybrid Soa-csmentioning

confidence: 99%

Improvised Seagull Optimization Algorithm for Scheduling Tasks in Heterogeneous Cloud Environment

Pradeep¹,

Poornachary²,

Krishnamoorthy³

et al. 2023

Computers, Materials &Amp; Continua

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Well organized datacentres with interconnected servers constitute the cloud computing infrastructure. User requests are submitted through an interface to these servers that provide service to them in an on-demand basis. The scientific applications that get executed at cloud by making use of the heterogeneous resources being allocated to them in a dynamic manner are grouped under NP hard problem category. Task scheduling in cloud poses numerous challenges impacting the cloud performance. If not handled properly, user satisfaction becomes questionable. More recently researchers had come up with meta-heuristic type of solutions for enriching the task scheduling activity in the cloud environment. The prime aim of task scheduling is to utilize the resources available in an optimal manner and reduce the time span of task execution. An improvised seagull optimization algorithm which combines the features of the Cuckoo search (CS) and seagull optimization algorithm (SOA) had been proposed in this work to enhance the performance of the scheduling activity inside the cloud computing environment. The proposed algorithm aims to minimize the cost and time parameters that are spent during task scheduling in the heterogeneous cloud environment. Performance evaluation of the proposed algorithm had been performed using the Cloudsim 3.0 toolkit by comparing it with Multi objective-Ant Colony Optimization (MO-ACO), ACO and Min-Min algorithms. The proposed SOA-CS technique had produced an improvement of 1.06%, 4.2%, and 2.4% for makespan and had reduced the overall cost to the extent of 1.74%, 3.93% and 2.77% when compared with PSO, ACO, IDEA algorithms respectively when 300 vms are considered. The comparative simulation results obtained had shown that the proposed improvised seagull optimization algorithm fares better than other contemporaries.

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Multi Objective Task Scheduling in Cloud Computing Using Cat Swarm Optimization Algorithm

Cited by 35 publications

References 15 publications

An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization

An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization

A Hybrid Gravitational Emulation Local Search-Based Algorithm for Task Scheduling in Cloud Computing

Improvised Seagull Optimization Algorithm for Scheduling Tasks in Heterogeneous Cloud Environment

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