A Cuckoo Search Algorithm-Based Task Scheduling in Cloud Computing

Agarwal, Mohit; Srivastava, Gur Mauj Saran

doi:10.1007/978-981-10-3773-3_29

Cited by 44 publications

(23 citation statements)

References 9 publications

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“…The BA algorithm has also been adapted for cloud computing challenges such as scheduling workflow applications [60,61] and cloud service composition [62]. There are also some implementations of the CS algorithm for the cloud computing domain [63,64].…”

Section: Swarm Intelligence Overview and Cloud Computing Applicationsmentioning

confidence: 99%

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

et al. 2019

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The cloud computing paradigm, as a novel computing resources delivery platform, has significantly impacted society with the concept of on-demand resource utilization through virtualization technology. Virtualization enables the usage of available physical resources in a way that multiple end-users can share the same underlying hardware infrastructure. In cloud computing, due to the expectations of clients, as well as on the providers side, many challenges exist. One of the most important nondeterministic polynomial time (NP) hard challenges in cloud computing is resource scheduling, due to its critical impact on the cloud system performance. Previously conducted research from this domain has shown that metaheuristics can substantially improve cloud system performance if they are used as scheduling algorithms. This paper introduces a hybridized whale optimization algorithm, that falls into the category of swarm intelligence metaheuristics, adapted for tackling the resource scheduling problem in cloud environments. To more precisely evaluate performance of the proposed approach, original whale optimization was also adapted for resource scheduling. Considering the two most important mechanisms of any swarm intelligence algorithm (exploitation and exploration), where the efficiency of a swarm algorithm depends heavily on their adjusted balance, the original whale optimization algorithm was enhanced by addressing its weaknesses of inappropriate exploitation-exploration trade-off adjustments and the premature convergence. The proposed hybrid algorithm was first tested on a standard set of bound-constrained benchmarks with the goal to more accurately evaluate its performance. After, simulations were performed using two different resource scheduling models in cloud computing with real, as well as with artificial data sets. Simulations were performed on the robust CloudSim platform. A hybrid whale optimization algorithm was compared with other state-of-the-art metaheurisitcs and heuristics, as well as with the original whale optimization for all conducted experiments. Achieved results in all simulations indicate that the proposed hybrid whale optimization algorithm, on average, outperforms the original version, as well as other heuristics and metaheuristics. By using the proposed algorithm, improvements in tackling the resource scheduling issue in cloud computing have been established, as well enhancements to the original whale optimization implementation.

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Section: Swarm Intelligence Overview and Cloud Computing Applicationsmentioning

confidence: 99%

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

et al. 2019

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“…Both metaheuristics have various applications for practical NP-hard challenges, RFID network planning problem [54], training feed-forward neural networks [55] and constrained portfolio optimization [56], and were also tested for benchmark functions [57,58]. Some of the BA and CS implementations for cloud computing challenges include scheduling workflow applications [59,60], cloud service composition [61], task scheduling [62] and load balancing [63].…”

Section: Review Of Swarm Intelligence Metaheuristics and Its Applicatmentioning

confidence: 99%

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

Strumberger

Tuba

Bačanin

et al. 2019

JSAN

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Cloud computing technology enables efficient utilization of available physical resources through the virtualization where different clients share the same underlying physical hardware infrastructure. By utilizing the cloud computing concept, distributed, scalable and elastic computing resources are provided to the end-users over high speed computer networks (the Internet). Cloudlet scheduling that has a significant impact on the overall cloud system performance represents one of the most important challenges in this domain. In this paper, we introduce implementations of the original and hybridized monarch butterfly optimization algorithm that belongs to the category of swarm intelligence metaheuristics, adapted for tackling the cloudlet scheduling problem. The hybridized monarch butterfly optimization approach, as well as adaptations of any monarch butterfly optimization version for the cloudlet scheduling problem, could not be found in the literature survey. Both algorithms were implemented within the environment of the CloudSim platform. The proposed hybridized version of the monarch butterfly optimization algorithm was first tested on standard benchmark functions and, after that, the simulations for the cloudlet scheduling problem were performed using artificial and real data sets. Based on the obtained simulation results and the comparative analysis with six other state-of-the-art metaheuristics and heuristics, under the same experimental conditions and tested on the same problem instances, a hybridized version of the monarch butterfly optimization algorithm proved its potential for tackling the cloudlet scheduling problem. It has been established that the proposed hybridized implementation is superior to the original one, and also that the task scheduling problem in cloud environments can be more efficiently solved by using such an algorithm with positive implications to the cloud management.

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“…As the above features of cloud model enable the user to access and elastically scaled in and scaled out the resources dynamically on their demand basis [41][42]. The measured service characteristic of cloud model is responsible for the metering out of the resources i.e.…”

Section: Measured Servicesmentioning

confidence: 99%

“…Service level agreement may be defined as a formal document of negotiation between the CSP and users of the cloud services which aims to provide the quality and guaranteed QoS. It used to have the following points [24]: As per the literature [25,29,42] suggests, the objective of signing the SLA between the CSP and the user is to select the best available CSP [37] on the basis of their declaration in the agreement, to control the overprovisioning which might affect the profitability of the service provider. The author in [31], presents the model based on virtual SLA to ensure the quality of QoS by applying them to the delegating tasks.…”

Section: E Service Level Agreement (Sla)mentioning

confidence: 99%

Cloud Computing: A Paradigm Shift in the Way of Computing

Agarwal¹,

Srivastava²

2017

IJMECS

Self Cite

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Cloud computing has been emerging out as a new and evolving paradigm with tremendous momentum. It is one of the most acceptable information technology based service which drew the attention of the people not only from the academia, industry but also registered its popularity among the general people. Features like scalability, elasticity, less entry cost, easy to access and subscription and pay per use etc. compel the businesses and end users to migrate themselves from the traditional platform to the cloud based platform. With the wide acceptability of cloud computing based services in the society, people have various myths like some think it as a new name of internet, as it shares many features of the internet while others feel it as another name of existing technology like distributed system, grid computing, and parallel computing etc.. This paper will help in making people aware of this technology by highlighting the points of difference with the existing technology and focusing on the various advantages and area of application which presents the evidence of its popularity and continual growth. The work in the paper will end with the discussion on the status of various issues and shortcomings from which it is suffering along with the present and future scope in this popular area.

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A Cuckoo Search Algorithm-Based Task Scheduling in Cloud Computing

Cited by 44 publications

References 9 publications

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

Cloud Computing: A Paradigm Shift in the Way of Computing

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