Nature-Inspired Metaheuristics in Cloud: A Review

Abrol, Preeti; Guupta, Savita; Singh, Sukhwinder

doi:10.1007/978-981-15-0936-0_2

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…The list of research works is not exhaustive, but are characteristic of how optimization algorithms can support this challenge. A review of optimization-based resource scheduling algorithms is given by [27].…”

Section: Resource Management In Cloud Computingmentioning

confidence: 99%

On the Optimization of Self-Organization and Self-Management Hardware Resource Allocation for Heterogeneous Clouds

et al. 2021

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There is a tendency, during the last years, to migrate from the traditional homogeneous clouds and centralized provisioning of resources to heterogeneous clouds with specialized hardware governed in a distributed and autonomous manner. The CloudLightning architecture proposed recently introduced a dynamic way to provision heterogeneous cloud resources, by shifting the selection of underlying resources from the end-user to the system in an efficient way. In this work, an optimized Suitability Index and assessment function are proposed, along with their theoretical analysis, for improving the computational efficiency, energy consumption, service delivery and scalability of the distributed orchestration. The effectiveness of the proposed scheme is being evaluated with the use of simulation, by comparing the optimized methods with the original approach and the traditional centralized resource management, on real and synthetic High Performance Computing applications. Finally, numerical results are presented and discussed regarding the improvements over the defined evaluation criteria.

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Section: Resource Management In Cloud Computingmentioning

confidence: 99%

On the Optimization of Self-Organization and Self-Management Hardware Resource Allocation for Heterogeneous Clouds

et al. 2021

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“…Several hybrid algorithms for load balancing optimization in the cloud environment are presented in the literature, such as a combination of Firefly and genetic algorithms (Rajagopalan et al, 2020) and a hybrid of Cuckoo and Firefly (Kumar et al, 2020). In addition, several literature reviews (Abrol et al, 2020; Pradhan et al, 2021) show the use of various meta‐heuristic based algorithms to optimize load balancing in the cloud computing environment.…”

Section: Introductionmentioning

confidence: 99%

CA‐MLBS: content‐aware machine learning based load balancing scheduler in the cloud environment

et al. 2022

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Cloud computing is the on-demand provision of computing resources over the Internet, such as cloud storage, computing power, network, and so on. Cloud computing has several advantages, including high speed, cost reduction, data security, and scalability. The main challenge in cloud environment is to balance the workloads and network traffic among the available resources to achieve maximum performance.Several methods have been proposed in the literature for effective load balancing, including heuristic, meta-heuristic, and hybrid algorithms. The performance of these techniques has been improved by combining machine learning based Artificial Intelligence (AI) techniques and meta-heuristic algorithms. Most of the existing load balancing techniques are not aware of the content type of user tasks. However, from the literature, the content type of the tasks can be very effective to design a balanced workload distribution system in the cloud. In this work, a novel AI-assisted hybrid approach called Content-aware Machine Learning based Load Balancing Scheduler (CA-MLBS) is proposed. The scheduling system CA-MLBS combines machine learning and meta-heuristic algorithms to perform classification based on file type. To achieve this, a Support Vector Machine (SVM) based classifier is used to classify user tasks into different content types such as video, audio, image, and text. A metaheuristic algorithm based on Particle Swarm Optimization (PSO) is used to map users' tasks in the cloud. The proposed approach was implemented and evaluated using a renowned Cloudsim simulation kit and compared with Ant Colony Optimization File Type Format (ACOFTF) and Data Files Type Formatting (DFTF) heuristics. The results of the proposed study show that the proposed CA-MLBS technique achieved improvements of up to 29%, 29%, and 44% in terms of makespan, response time, and throughput, respectively.

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A Nature-Inspired-Based Multi-objective Service Placement in Fog Computing Environment

Apat

Bhaisare

Sahoo

et al. 2021

Intelligent Systems

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Nature-Inspired Metaheuristics in Cloud: A Review

Cited by 4 publications

References 38 publications

On the Optimization of Self-Organization and Self-Management Hardware Resource Allocation for Heterogeneous Clouds

On the Optimization of Self-Organization and Self-Management Hardware Resource Allocation for Heterogeneous Clouds

CA‐MLBS: content‐aware machine learning based load balancing scheduler in the cloud environment

A Nature-Inspired-Based Multi-objective Service Placement in Fog Computing Environment

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