Hybrid electro search with genetic algorithm for task scheduling in cloud computing

Velliangiri, S.; Karthikeyan, P.; Xavier, V. M. Arul; Baswaraj, D.

doi:10.1016/j.asej.2020.07.003

Cited by 98 publications

(77 citation statements)

References 19 publications

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“…The algorithm hybrid electro search with a genetic algorithm (HESGA) was proposed by Velliangiri et al [36] to optimize the results in terms of makespan, execution time, and cost. The proposed algorithm takes advantage of both the involved algorithms.…”

Section: Hybrid Using Gamentioning

confidence: 99%

Hybrid scheduling algorithms in cloud computing: a review

Arora

Banyal

2022

IJECE

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<p><span>Cloud computing is one of the emerging fields in computer science due to its several advancements like on-demand processing, resource sharing, and pay per use. There are several cloud computing issues like security, quality of service (QoS) management, data center energy consumption, and scaling. Scheduling is one of the several challenging problems in cloud computing, where several tasks need to be assigned to resources to optimize the quality of service parameters. Scheduling is a well-known NP-hard problem in cloud computing. This will require a suitable scheduling algorithm. Several heuristics and meta-heuristics algorithms were proposed for scheduling the user's task to the resources available in cloud computing in an optimal way. Hybrid scheduling algorithms have become popular in cloud computing. In this paper, we reviewed the hybrid algorithms, which are the combinations of two or more algorithms, used for scheduling in cloud computing. The basic idea behind the hybridization of the algorithm is to take useful features of the used algorithms. This article also classifies the hybrid algorithms and analyzes their objectives, quality of service (QoS) parameters, and future directions for hybrid scheduling algorithms.</span></p>

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Section: Hybrid Using Gamentioning

confidence: 99%

Hybrid scheduling algorithms in cloud computing: a review

Arora

Banyal

2022

IJECE

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“…Accordingly, we evaluate the effect of the topology of LSTM and CNN models selected by different bio-inspired optimization algorithms on the error reduction and predictive performance of the model. The compared state-of-the-art algorithms include the GA [35], PSO [33], DE [30], GWO [26], ABC [29], WO [31], CS algorithm [28], and BAT algorithm [34]. By comparing the prediction ability of the proposed model to these obtained by the other nine models established using the compared bio-inspired algorithms shown in Table 11, we can realize that across the three datasets, the proposed model achieved the highest precision, recall, accuracy, and F-score, with limited error rates and low SD values.…”

Section: Comparative Analysismentioning

confidence: 99%

“…Therefore, it is useful to employ the swarm intelligence optimization techniques for enabling the networks to automatically tune their hyperparameters besides the layer connections and make the optimal utilization of the redundant computing resources. Grey wolf optimizer (GWO) [26], antlion optimization (ALO) [27], crow search (CS) algorithm [28], artificial bee colony (ABC) [29], differential evolution (DE) algorithm [30], whale optimization (WO) algorithm [31], Salp swarm algorithm [32], PSO [33], bat optimization (BAT) algorithm [34], and genetic algorithm (GA) [35] are some biologically-inspired algorithms investigated in optimization purposes.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Evolutionary Deep Learning Model for Ovarian Cancer Diagnosis

et al. 2021

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Ovarian cancer (OC) is a common reason for mortality among women. Deep learning has recently proven better performance in predicting OC stages and subtypes. However, most of the state-of-the-art deep learning models employ single modality data, which may afford low-level performance due to insufficient representation of important OC characteristics. Furthermore, these deep learning models still lack to the optimization of the model construction, which requires high computational cost to train and deploy them. In this work, a hybrid evolutionary deep learning model, using multi-modal data, is proposed. The established multi-modal fusion framework amalgamates gene modality alongside with histopathological image modality. Based on the different states and forms of each modality, we set up deep feature extraction network, respectively. This includes a predictive antlion-optimized long-short-term-memory model to process gene longitudinal data. Another predictive antlion-optimized convolutional neural network model is included to process histopathology images. The topology of each customized feature network is automatically set by the antlion optimization algorithm to make it realize better performance. After that the output from the two improved networks is fused based upon weighted linear aggregation. The deep fused features are finally used to predict OC stage. A number of assessment indicators was used to compare the proposed model to other nine multi-modal fusion models constructed using distinct evolutionary algorithms. This was conducted using a benchmark for OC and two benchmarks for breast and lung cancers. The results reveal that the proposed model is more precise and accurate in diagnosing OC and the other cancers.

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“…In [13], the authors had integrated the genetic algorithm and the electro search algorithm and proposed a Hybrid Electro Search with a genetic algorithm (HESGA) for improving the task scheduling process in the cloud. The authors had considered the QoS parameters namely makespan, load balancing, resource utilization and cost for fine tuning the task scheduling activity.…”

Section: Related Workmentioning

confidence: 99%

CCSA: Hybrid Cuckoo Crow Search Algorithm for Task Scheduling in Cloud Computing

Pradeep¹

2021

IJIES

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Services are delivered promptly and efficiently managed over the Internet using cloud computing. Data and storage services are offered to users through cloud computing without regard to the actual physical location of the users. The cloud environment includes huge number of tasks and computing resources. Identification of appropriate virtual machine (VM) for allocation of resources to complete any submitted task is done through the task scheduling algorithms, which play a significant role in the cloud computing process. Task scheduling techniques improvise the makespan of the schedule and account for considerable reduction of cost expended. In this paper, an efficient hybridized scheduling algorithm that replicates the parasitic behaviour of the cuckoo and food gathering habit of the crow bird, named Cuckoo Crow Search Algorithm (CCSA) had been presented for improvising the task scheduling process. The crow bird always stares at its neighbours looking for a better food source than the one it currently possesses. At some circumstances the crow even goes a step further and steals its neighbour's food. The CCSA had been inspired from such characteristics of these birds and had been designed to be applied in the cloud environment for identifying a suitable VM for carrying out the task scheduling process. The proposed CCSA accounts for reduction in makespan and cost and its performance had been experimentally verified by comparing it with other existing algorithms like Multi objective -Ant Colony Optimization (MO-ACO), ACO, Min-Min and the results had been presented. The proposed CCSA technique had produced an improvement of 3.14%, 10.70%, and 21.60% for makespan and had reduced the overall cost to the tune of 4.56%, 11.19% and 19.35% when compared with MO-ACO, ACO, Min-Min algorithms respectively when used with 10 VMs. The detailed description of results obtained had been described in result section.

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Hybrid electro search with genetic algorithm for task scheduling in cloud computing

Cited by 98 publications

References 19 publications

Hybrid scheduling algorithms in cloud computing: a review

Hybrid scheduling algorithms in cloud computing: a review

Multi-Modal Evolutionary Deep Learning Model for Ovarian Cancer Diagnosis

CCSA: Hybrid Cuckoo Crow Search Algorithm for Task Scheduling in Cloud Computing

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