Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers

Ajmal, Muhammad Sohaib; Iqbal, Zeshan; Khan, Farrukh Zeeshan; Ahmad, Muneer; Ahmad, Iftikhar; Gupta, Brij B.

doi:10.1016/j.compeleceng.2021.107419

Cited by 51 publications

(17 citation statements)

References 14 publications

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“…In [11], the authors proposed a Hybrid ant genetic approach for scheduling tasks. The presented technique adopts features of genetic algorithm (GA) and ant colony optimization (ACO) and splits virtual machines and tasks into small groups.…”

Section: Related Workmentioning

confidence: 99%

Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

Chellapraba¹,

Manohari²,

Periyakaruppan³

et al. 2023

Computer Systems Science and Engineering

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Owing to massive technological developments in Internet of Things (IoT) and cloud environment, cloud computing (CC) offers a highly flexible heterogeneous resource pool over the network, and clients could exploit various resources on demand. Since IoT-enabled models are restricted to resources and require crisp response, minimum latency, and maximum bandwidth, which are outside the capabilities. CC was handled as a resource-rich solution to aforementioned challenge. As high delay reduces the performance of the IoT enabled cloud platform, efficient utilization of task scheduling (TS) reduces the energy usage of the cloud infrastructure and increases the income of service provider via minimizing processing time of user job. Therefore, this article concentration on the design of an oppositional red fox optimization based task scheduling scheme (ORFO-TSS) for IoT enabled cloud environment. The presented ORFO-TSS model resolves the problem of allocating resources from the IoT based cloud platform. It achieves the makespan by performing optimum TS procedures with various aspects of incoming task. The designing of ORFO-TSS method includes the idea of oppositional based learning (OBL) as to traditional RFO approach in enhancing their efficiency. A wide-ranging experimental analysis was applied on the CloudSim platform. The experimental outcome highlighted the efficacy of the ORFO-TSS technique over existing approaches.

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Section: Related Workmentioning

confidence: 99%

Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

Chellapraba¹,

Manohari²,

Periyakaruppan³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

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“…They proposed a hybrid fuzzy ant colony algorithm in order to distribute the load efficiently on servers, and showed the effectiveness of the proposed hybrid algorithm, especially in the case of a high number of nodes. Ajmal et al [22] proposed a combined an ant colony algorithm with a genetic algorithm in order to efficiently scheduling tasks in a cloud data center. By splitting tasks into groups and identifying loaded virtual machines, they showed that the proposed approach considerably reduces solution space.…”

Section: Related Workmentioning

confidence: 99%

“…The initial acceptance probability of annealing is close to 1, and the pheromone update is performed on the deployment plan corresponding to the updated centralized solution to make the pheromone distribution more widely and avoid falling into a local optimum. After all the ants complete one iteration, the algorithm performs cooling processing, setting a reasonable cooling processing mechanism, such as in (22). As the number of iterations increases, the temperature gradually decreases, the acceptance probability decreases, and the probability of a poor solution being accepted decreases, which makes the pheromone distribution on the path more concentrated and speeds up the algorithm convergence speed.…”

Section: ) Simulated Annealing Mechanismmentioning

confidence: 99%

An Improved Ant Colony Algorithm for Solving a Virtual Machine Placement Problem in a Cloud Computing Environment

2022

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The world is currently witnessing many successive changes in several fields, especially in the field of healthcare which forced countries to impose health policies to provide good services, which drew health sector strategies focusing on the digital transformation aspect. E-government healthcare applications are used by dozens of millions of users. This high usage of applications generates a huge network traffic and needs reliable cloud computing platforms and efficient virtual machine placement models. In this context, we proposed an improved Ant Colony algorithm to solve a virtual machine placement problem in a cloud computing environment in order to minimize the total network traffic and the maximum link utilization. The experimental results demonstrate the effectiveness and efficiency of our proposed algorithm.

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“…The developed algorithm is of two sections, the first section is used for identifying the load buses required for appropriate load shedding, while the other section decides which of the optimum loads is required to be shed at the chosen load buses, using differential evolution. Validation of the method is conducted using a genetic algorithm, a method that has often been proven by different authors to be fast and accurate [15][16][17][18]. The remaining of this paper is structured as follows: Section 2 describes the proposed methodology, which includes nodal analysis and a DE algorithm for optimizing the load shedding.…”

Section: Introductionmentioning

confidence: 99%

Identification of Weak Buses for Optimal Load Shedding Using Differential Evolution

2022

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For the sustainability of power supply and operation systems, planners aim to deliver power at an optimum value to consumers, while maintaining stability in the system. The load-shedding approach has proven to be an effective means of achieving the desired stability. This paper presents a nodal analysis to establish critical bus identification in the power grid. A power simulation for load shedding was created using the power system analysis toolbox (PSAT) for identifying and isolating weak buses on the power system. A computational algorithm was developed using differential evolution (DE) for minimizing service interruptions and blackouts, and was tested against the conventional genetic algorithm (GA). The proposed algorithm was implemented on an IEEE 30-bus test system. The simulation results were analyzed before and after the application of DE. It was observed that after the application of DE, load shedding gives an efficient result of 10.6%, 8.7%, and 13.4% improvement at buses 26, 29, and 30, respectively, after being tested using a genetic algorithm (GA), with a result of 10.2%, 7.6% and 13.1% on the same respective buses. This work will further expand the reliability and availability of power systems toward a sustainable, steady power supply that is void of nodal or bus cutoffs.

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Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers

Cited by 51 publications

References 14 publications

Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

An Improved Ant Colony Algorithm for Solving a Virtual Machine Placement Problem in a Cloud Computing Environment

Identification of Weak Buses for Optimal Load Shedding Using Differential Evolution

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