Amelioration of task scheduling in cloud computing using crow search algorithm

Kumar, K. R. Prasanna; Kousalya, K.

doi:10.1007/s00521-019-04067-2

Cited by 63 publications

(12 citation statements)

References 22 publications

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“…CSA has the advantages of easy to understand and implement, few control parameters, good universality and strong global search ability. Since its development, CSA has been widely used in many fields, including numerical optimization (Khalilpourazari and Pasandideh 2020 ; Necira et al 2022 ; Gholami et al 2021 ), feature selection (Sayed et al 2019 ; Ouadfel and Abd Elaziz 2020 ), image processing (Upadhyay and Chhabra 2020 ; Fred et al 2020 ), optimal power flow (Saha et al 2017 ; Fathy and Abdelaziz 2018 ), economic load dispatch (Mohammadi and Abdi 2018 ; Spea 2020 ), cloud computing (Kumar and Vimala 2019 ; Kumar and Kousalya 2020 ), control engineering (Turgut et al 2020 ), and chemical engineering (Abdallh and Algamal 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Enhanced crow search algorithm with multi-stage search integration for global optimization problems

Peng

Zhang

et al. 2023

Soft Comput

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Crow search algorithm (CSA), as a new swarm intelligence algorithm that simulates the crows’ behaviors of hiding and tracking food in nature, performs well in solving many optimization problems. However, while handling complex and high-dimensional global optimization problems, CSA is apt to fall into evolutionary stagnation and has slow convergence speed, low accuracy, and weak robustness. This is mainly because it only utilizes a single search stage, where position updating relies on random following among individuals or arbitrary flight of individuals. To address these deficiencies, a CSA with multi-stage search integration (MSCSA) is presented. Chaos and multiple opposition-based learning techniques are first introduced to improve original population quality and ergodicity. The free foraging stage based on normal random distribution and Lévy flight is designed to conduct local search for enhancing the solution accuracy. And the following stage using mixed guiding individuals is presented to perform global search for expanding the search space through tracing each other among individuals. Finally, the large-scale migration stage based on the best individual and mixed guiding individuals concentrates on increasing the population diversity and helping the population jump out of local optima by moving the population to a promising area. All of these strategies form multi-level and multi-granularity balances between global exploration and local exploitation throughout the evolution. The proposed MSCSA is compared with a range of other algorithms, including original CSA, three outstanding variants of CSA, two classical meta-heuristics, and six state-of-the-art meta-heuristics covering different categories. The experiments are conducted based on the complex and high-dimensional benchmark functions CEC 2017 and CEC 2010, respectively. The experimental and statistical results demonstrate that MSCSA is competitive for tackling large-scale complicated problems, and is significantly superior to the competitors.

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Section: Introductionmentioning

confidence: 99%

Enhanced crow search algorithm with multi-stage search integration for global optimization problems

Peng

Zhang

et al. 2023

Soft Comput

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“…Cloud computing is described as “a structure for providing comfort, necessary network permission to use a common pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be quickly supplied and released with the least amount of supervision attempt or service contributor operation.” 1,2 Even while cloud computing provides customers with a wide range of services, there are several problems with these networks, and one of the major ones is job scheduling 3,4 . Task scheduling allows us to manage the processing of our tasks and increase system throughput 5 …”

Section: Introductionmentioning

confidence: 99%

Machine learning based secure and efficient task allocation in multi‐cloud

Patil¹,

Ket²

2023

Concurrency and Computation

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SummaryTask scheduling is a critical problem in the cloud computing environment since it has a significant impact on the system's performance. Security and robust based task allocation model improves the security of real‐time solicitations whereas preserving a high‐level performance of the data center. Here, to minimize the makespan in task allocation existing research proposed a bio‐inspired algorithm in a multi‐cloud environment, but, it needs more memory to update velocity and increases the processing time. Therefore, this research proposed a Machine Learning based Secure and Efficient Task Allocation in Multi‐Cloud Environment. First, the proposed model has a task classifier module that categorizes the matching it to the appropriate VM type for the task that was submitted. The next is the task allocation module, in which, an Intellectual Gravitational Search‐Genetic Algorithm is suggested to allocate the task to a VM. The tasks are distributed to the VMs in the order specified after the allocating phase in which they will be executed. Finally, to secure the task allocation in multi‐cloud, this research proposed a Naïve Bayes module to detect the attacks. The models and experimental findings demonstrate that our proposed approach produces a shorter makespan, lower cost, improved resource utilization, and better trade‐off between time and economic cost. It is more dependable and effective in multi‐cloud environment.

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“…To their disadvantage, these meta‐heuristic algorithms consume high computational resources, especially time, and in some cases may resort to local optimum whenever they operate upon large search spaces. Moreover, they are plagued by premature convergence and do not offer a balanced solution between local and global searches 10–12 …”

Section: Introductionmentioning

confidence: 99%

Optimization techniques for task scheduling criteria in IaaS cloud computing atmosphere using nature inspired hybrid spotted hyena optimization algorithm

Natesan¹,

Ali

Pradeep

et al. 2022

Concurrency and Computation

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Summary Cloud computing has garnered unprecedented growth in recent years in the field of Information Technology. It has emerged as a high‐performance computing option owing to its infrastructure that comprises of heterogeneous collection of autonomous computers and adaptable network architecture. The tasks that are scheduled in an optimized manner for their execution could be classified under NP‐hard problems. Though meta‐heuristic scheduling algorithms emerge as scheduling options, they need to be much more consistent while dealing with the dynamic set up of the cloud environment. In this paper, we had proposed a multi‐objective meta‐heuristic scheduling algorithm namely Quasi Oppositional Genetic Spotted Hyena Optimization (QOGSHO) algorithm that globally optimizes the makespan, resource consumption and SLA violation QoS parameters, thereby improving the performance. The algorithm proposed is an amalgamated product of meta‐heuristic algorithms like Quasi Oppositional Based Learning (QOBL), Spotted Hyena Optimization (SHO), and Genetic Algorithm (GA). The performance efficiency of the proposed QOGSHO algorithm had been compared with various scheduling algorithms using uniform datasets by varying the data instance sizes in a simulated cloud environment. The obtained results clearly justify the task scheduling efficiency of the proposed algorithm with respect to the QoS parameters namely makespan, resource utilization and SLA violation.

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Amelioration of task scheduling in cloud computing using crow search algorithm

Cited by 63 publications

References 22 publications

Enhanced crow search algorithm with multi-stage search integration for global optimization problems

Enhanced crow search algorithm with multi-stage search integration for global optimization problems

Machine learning based secure and efficient task allocation in multi‐cloud

Optimization techniques for task scheduling criteria in IaaS cloud computing atmosphere using nature inspired hybrid spotted hyena optimization algorithm

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