Bio-inspired Based Task Scheduling in Cloud Computing

Gamal, Marwa; Rizk, Rawya; Mahdi, Hani; Elhady, Basem

doi:10.1007/978-3-030-02357-7_14

Cited by 17 publications

(9 citation statements)

References 21 publications

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“…Cloud computing presents diverse services to the clients; however, there are some problems in these networks, and task scheduling is one of the significant issue of them [3,4]. Through task scheduling, we can increase the throughput of the system and manage our tasks to be processed [5].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Pirozmand

Hosseinabadi

Farrokhzad

et al. 2021

Neural Comput & Applic

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The cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access existing services based on their needs and without knowing where the service is located and how it is delivered, and only pay for the service used. Like other systems, there are challenges in the cloud computing system. Because of a wide array of clients and the variety of services available in this system, it can be said that the issue of scheduling and, of course, energy consumption is essential challenge of this system. Therefore, it should be properly provided to users, which minimizes both the cost of the provider and consumer and the energy consumption, and this requires the use of an optimal scheduling algorithm. In this paper, we present a two-step hybrid method for scheduling tasks aware of energy and time called Genetic Algorithm and Energy-Conscious Scheduling Heuristic based on the Genetic Algorithm. The first step involves prioritizing tasks, and the second step consists of assigning tasks to the processor. We prioritized tasks and generated primary chromosomes, and used the Energy-Conscious Scheduling Heuristic model, which is an energy-conscious model, to assign tasks to the processor. As the simulation results show, these results demonstrate that the proposed algorithm has been able to outperform other methods.

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Section: Introductionmentioning

confidence: 99%

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Pirozmand

Hosseinabadi

Farrokhzad

et al. 2021

Neural Comput & Applic

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“…Thus, depending on the approach, the infrastructure management complexity and the needed tools vary significantly. In this case, bio-inspired metaheuristics have demonstrated a remarkable performance when solving complex problems associated with infrastructure and technologies, such as resource allocation and management [ 41 , 42 ], job scheduling [ 43 ], log synchronization and information security [ 44 ], or anomaly detection in the management and health of the IT infrastructure [ 45 ]. We will later examine them thoroughly.…”

Section: Big Data and Bio-inspired Computation: First Conceptsmentioning

confidence: 99%

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Torre-Bastida

Díaz-de-Arcaya

Osaba

et al. 2021

Neural Comput & Applic

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This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.

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“…Gamal et al [33] presented a hybrid LB algorithm combining the positive traits of Artificial Bee and Ant Colony optimization (ACO). It depends on joining ACO 's critical behavior, like rapidly discovering reasonable solutions, and the Artificial Bee Colony (ABC) algorithm, like communal bee interaction and sharing waggle dancing details.…”

Section: General Load Balancing Techniquesmentioning

confidence: 99%

Load Balancing in Cloud Computing: A Comprehensive Survey on Recent Techniques

2021

IJATCSE

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The Internet has become a fundamental necessity of daily activity. It has a more significant impact on modernizing the digital world. Therefore, cloud computing has been one of the most promising technological advances in recent days. It aims to provide millions of consumers around the globe with smooth computing services. In workload distributions and system behavior, the cloud environment is extremely dynamic, leading to load imbalances across data center resources. Balancing load across the systems is a crucial component of cloud computing, avoiding some nodes' overloading while others have little or no work to do. The problem of load balancing in the cloud has been linked to a large amount of research, recognizing its role and impact. This paper bestows a thorough investigation of the new load balancing algorithms for the cloud environment. This study's primary focus is the concept of load balancing, a literature review on load balancing and task scheduling techniques, and different measurement parameters

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Bio-inspired Based Task Scheduling in Cloud Computing

Cited by 17 publications

References 21 publications

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Load Balancing in Cloud Computing: A Comprehensive Survey on Recent Techniques

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