Local Pollination-Based Moth Search Algorithm for Task-Scheduling Heterogeneous Cloud Environment

Gokuldhev, M.; Singaravel, G.

doi:10.1093/comjnl/bxaa053

Cited by 9 publications

(3 citation statements)

References 22 publications

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“…Movement and pollination, which have the highest exploitation-exploration tenacity, can be improved by modifying the sun flower optimisation algorithm [19]. For better cloud performance and urgent concerns, local pollination-based moth search algorithm (LPMSA) optimizes the cloud-based assignment of tasks using flower pollination and moth search algorithms [20]. Multi-object searching approach spacing multi-objective antlion algorithm (S-MOAL) decreases VM makespan and consumption cost [21].…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid technique for optimal task scheduling in cloud computing environments

Sabat,

Sahoo,

Pradhan

et al. 2024

TELKOMNIKA

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Since cloud computing has an abundance of users, the system has to execute a wide range of tasks. Task scheduler methods that are both robust and efficient while delivering the best outcomes are required. The task volume and runtime in the cloud vary rapidly, making task assessment and resource mapping difficult. Security issues, communication delays, and data loss are substantial barriers to scheduling. Furthermore, optimization techniques can be utilized to reduce load and assign tasks so that the user can finish tasks faster. This paper offers a hybrid job scheduling technique for cloud computing using adaptive particle swarm optimization and ant colony optimization particle swarm optimization-ant colony optimization (adaptive PSO-ACO). After rapidly finding the initial solution via particle swarm optimization, the ant colony optimization approach establishes its first pheromone distribution. The suggested hybrid algorithm is compared to standalone PSO and ACO algorithms. Compared to ACO, the percentage decrease is 7.9%. Hybrid method has the lowest total cost, 55% less compared to PSO. Tasks vary when virtual machines (VMs) are constant and VMs vary when tasks are constant. Parameters like final cost, makespan, fitness value, computation time and weighted time are assessed to evaluate the performance of the hybrid task scheduling algorithm.

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Section: Literature Surveymentioning

confidence: 99%

Hybrid technique for optimal task scheduling in cloud computing environments

Sabat,

Sahoo,

Pradhan

et al. 2024

TELKOMNIKA

View full text Add to dashboard Cite

show abstract

“…Finally, Gokuldhev and Singaravel 30 suggested an optimal task‐scheduling algorithm of the Local Pollination‐based Moth Search Algorithm (LPMSA), which is the hybridization of the Moth Search Algorithm (MSA) and Flower Pollination Algorithm (FPA). The LPMSA selected an optimal solution for suitable task scheduling in the cloud.…”

Section: Related Workmentioning

confidence: 99%

A cloud database route scheduling method using a hybrid optimization algorithm

Baghi

Navimipour

2023

Int J Communication

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Cloud computing has appeared as a technology allowing a company to employ computing resources such as applications, software, and hardware to calculate over the Internet. Scholars have paid great attention to cloud computing because of its cutting-edge availability, cost decrement, and boundless applications. A cloud database is a data storage site on the web where the optimal path is spotted to access the needed database. So, placing the ideal path to a database is crucial. The cloud database defined the scheduling problem to choose the perfect route. Cloud database path scheduling is a multifaceted procedure consisting of congestion control, routing list, and network flow distribution. It has a postponement in searching for the needed source route from the cloud database. Offering numerous infinite resources with the growing database workload is an NP-Hard optimization problem where the query request needs optimal schedules to respond to the required services. So, we have used a hybrid cuckoo search (CS) and genetic algorithm (GA), motivated by a social bird's phenomenon, to solve this problem. Integrating genetic operators has dramatically enhanced the balance between the capability of searching and utilization.

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“…e Wilcoxon test compares makespan minima and energy usage. is work's limitations are the necessity to test with real-time applications and add more parameters to the algorithm [27]. e local pollination-based gray wolf optimizer (LPGWO) method was used by Gokuldhev et al to efficiently schedule jobs.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control System

et al. 2022

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Integrating cloud computing with wireless sensor networks creates a sensor cloud (WSN). Some real-time applications, such as agricultural irrigation control systems, use a sensor cloud. The sensor battery life in sensor clouds is constrained. The data center’s computers consume a lot of energy to offer storage in the cloud. The emerging sensor cloud technology-enabled virtualization. Using a virtual environment has many advantages. However, different resource requirements and task execution cause substantial performance and parameter optimization issues in cloud computing. In this study, we proposed the hybrid electro search with ant colony optimization (HES-ACO) technique to enhance the behavior of task scheduling, for those considering parameters such as total execution time, cost of the execution, makespan time, the cloud data center energy consumption like throughput, response time, resource utilization task rejection ratio, and deadline constraint of the multicloud. Electro search and the ant colony optimization algorithm are combined in the proposed method. Compared to HESGA, HPSOGA, AC-PSO, and PSO-COGENT algorithms, the created HES-ACO algorithm was simulated at CloudSim and found to optimize all parameters.

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Local Pollination-Based Moth Search Algorithm for Task-Scheduling Heterogeneous Cloud Environment

Cited by 9 publications

References 22 publications

Hybrid technique for optimal task scheduling in cloud computing environments

Hybrid technique for optimal task scheduling in cloud computing environments

A cloud database route scheduling method using a hybrid optimization algorithm

Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control System

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