PSO-COGENT: Cost and energy efficient scheduling in cloud environment with deadline constraint

Kumar, Mohit; Sharma, S. C.

doi:10.1016/j.suscom.2018.06.002

Cited by 58 publications

(46 citation statements)

References 21 publications

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“…The main motivation behind the proposed research is the fact that swarm intelligence metaheuristics proved to be robust and effective optimization methods for solving many different types of real-environment NP hard optimization problems [27,28], as well as the fact that swarm intelligence algorithms have already been successfully applied to various models (single and multi-objective) of resource scheduling in cloud computing [2,29,30].…”

Section: Objectives Contributions Question and Methodology Of The Pmentioning

confidence: 99%

“…One other definition defines cloud computing as an "elastic and distributed system in which computing resources, such as processing power and storage space, information and software, are propagated through the network and delivered in the distributed location in the cloud where, it can be shared and obtained" [35]. The third definition, that we selected from the computer science literature, states that the "cloud represents a collection of heterogeneous hardware and software resources which provides services and fast processing resources with high availability to the cloud users in a way that it is not necessary that the end-users have their own hardware and software infrastructure" [30].…”

Section: Cloud Computing Principles and Architecturementioning

confidence: 99%

“…Some other swarm intelligence representatives that have been implemented for cloud computing problems include particle swarm optimization (PSO) [30], gravitational search algorithm (GSA) [35], ant colony optimization (ACO) [72][73][74], moth search (MS) [75], and many others [76].…”

Section: Swarm Intelligence Overview and Cloud Computing Applicationsmentioning

confidence: 99%

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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: Objectives Contributions Question and Methodology Of The Pmentioning

confidence: 99%

Section: Cloud Computing Principles and Architecturementioning

confidence: 99%

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

et al. 2019

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“…Many definitions that portray the significance of cloud computing paradigm exist, and one of them states that the cloud computing presents an elastic and distributed system in which computing resources such as processing power and storage space, information and software, are propagated through the network and delivered in the distributed location in the cloud where it can be shared and obtained [7]. Another definition states that the cloud represents a collection of heterogeneous hardware and software resources that enables services to the cloud users, hence the fact that is not necessary the cloud users to have their own hardware and software infrastructure [8]. One of the most important definitions of cloud computing that is accepted by many major global cloud vendors is provided by the National Institute of Standards and Technology (NIST).…”

Section: Cloud Computing Definitions and Conceptmentioning

confidence: 99%

“…For example, in [25], the authors proposed a new model to optimize virtual machines selection in cloud-IoT health services applications and adapted original PSO and parallel PSO (PPSO) for tackling this kind of problem. In addition, in [8], by utilizing the PSO-based scheduling (PSO-COGENT) algorithm, makespan, execution cost and the energy consumption of cloud data centers, considering deadline as a constraint, were successfully optimized.…”

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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Experimental performance analysis of cloud resource allocation framework using spider monkey optimization algorithm

Kumar

Dubey

Singh

et al. 2022

Concurrency and Computation

Self Cite

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The cloud services demand has increased exponentially in the last decade due to its plethora of services. It becomes a significant platform to compute large and diverse applications over the internet. On the contrary, on-demand resource allocation to a variety of applications becomes a serious issue due to dynamic workload conditions and uncertainty in the cloud environment. Several existing state of art techniques often fails to allocate the optimal resources to forthcoming demands, leading to an imbalance workload over cloud platform, degrading the performance. This article introduces a secure and self-adaptive resource allocation framework that addressed the mentioned issues and allocates the most suitable resources to users' applications while ensuring the deadline constraints. Further, the proposed framework is integrated with a metaheuristic algorithm named enhanced spider monkey optimization algorithm that is based on the intelligent foraging behavior of spider monkeys. The proposed algorithm finds an optimal resource for the user's application using the fission-fusion approach and improves multiple influential parameters like time, cost, degree of load balancing, energy consumption, task rejection ratio and so on. The experimental CloudSim based results verified that the proposed framework performs superior to state of art approaches like PSO, GSA, ABC, and IMMLB.

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PSO-COGENT: Cost and energy efficient scheduling in cloud environment with deadline constraint

Cited by 58 publications

References 21 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

Experimental performance analysis of cloud resource allocation framework using spider monkey optimization algorithm

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