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

Gokuldhev, M.; Singaravel, G.

doi:10.1093/comjnl/bxaa053

Cited by 11 publications

(5 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

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show abstract

“…In yet another promising work, the WPCO algorithm resulted in efficient task-resource pairs [64]. In [7], authors enhanced the moth search algorithm by incorporating the local pollination strategy of the flower pollination algorithm to schedule tasks in such as way that minimizes makespan and energy consumption. The resultant multi-objective scheduling algorithm produced better performance than the baseline algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the heterogeneity of cloud resources, the rising scale and size of the cloud, and the dynamic demand of cloud users, the complexity of the TS problem increases. Hence, cloud TS falls into the category of NP-hard problems [6,7]. Ideally, there are R T ways of allocating T tasks over R computing resources, and the problem of obtaining an optimal feasible schedule fulfilling physical and timing constraints is known to be NP-hard [8].…”

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confidence: 99%

A novel hybrid Artificial Gorilla Troops Optimizer with Honey Badger Algorithm for solving cloud scheduling problem

Hussien,

Chhabra,

Hashim

et al. 2024

Cluster Comput

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Cloud computing has revolutionized the way a variety of ubiquitous computing resources are provided to users with ease and on a pay-per-usage basis. Task scheduling problem is an important challenge, which involves assigning resources to users’ Bag-of-Tasks applications in a way that maximizes either system provider or user performance or both. With the increase in system size and the number of applications, the Bag-of-Tasks scheduling (BoTS) problem becomes more complex due to the expansion of search space. Such a problem falls in the category of NP-hard optimization challenges, which are often effectively tackled by metaheuristics. However, standalone metaheuristics generally suffer from certain deficiencies which affect their searching efficiency resulting in deteriorated final performance. This paper aims to introduce an optimal hybrid metaheuristic algorithm by leveraging the strengths of both the Artificial Gorilla Troops Optimizer (GTO) and the Honey Badger Algorithm (HBA) to find an approximate scheduling solution for the BoTS problem. While the original GTO has demonstrated effectiveness since its inception, it possesses limitations, particularly in addressing composite and high-dimensional problems. To address these limitations, this paper proposes a novel approach by introducing a new updating equation inspired by the HBA, specifically designed to enhance the exploitation phase of the algorithm. Through this integration, the goal is to overcome the drawbacks of the GTO and improve its performance in solving complex optimization problems. The initial performance of the GTOHBA algorithm tested on standard CEC2017 and CEC2022 benchmarks shows significant performance improvement over the baseline metaheuristics. Later on, we applied the proposed GTOHBA on the BoTS problem using standard parallel workloads (CEA-Curie and HPC2N) to optimize makespan and energy objectives. The obtained outcomes of the proposed GTOHBA are compared to the scheduling techniques based on well-known metaheuristics under the same experimental conditions using standard statistical measures and box plots. In the case of CEA-Curie workloads, the GTOHBA produced makespan and energy consumption reduction in the range of 8.12–22.76% and 6.2–18.00%, respectively over the compared metaheuristics. Whereas for the HPC2N workloads, GTOHBA achieved 8.46–30.97% makespan reduction and 8.51–33.41% energy consumption reduction against the tested metaheuristics. In conclusion, the proposed hybrid metaheuristic algorithm provides a promising solution to the BoTS problem, that can enhance the performance and efficiency of cloud computing systems.

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

Cited by 11 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 novel hybrid Artificial Gorilla Troops Optimizer with Honey Badger Algorithm for solving cloud scheduling problem

A cloud database route scheduling method using a hybrid optimization algorithm

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