An Efficient Load Balancing Technique based on Cuckoo Search and Firefly Algorithm in Cloud

Kumar, K. Krishna; Ragunathan, T.; Vasumathi, D.; Prasad, P. V. S. R Bhanu

doi:10.22266/ijies2020.0630.38

Cited by 11 publications

(4 citation statements)

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“…The CSFA algorithm's primary goal was to complete two tasks: first, to identify the optimal virtual machines (VMs) to allot the work, and second, to migrate overloaded VM's tasks to under-loaded VM's tasks. The CSFA algorithm was able to migrate two tasks when the numbers of tasks were equivalent to 40, whereas the present technique can migrate six tasks [18,19]. Karthikeyan [20] used hybrid optimization to overcome the energy restrictions.…”

Section: Related Workmentioning

confidence: 96%

Machine-Learning-Based Approach for Virtual Machine Allocation and Migration

et al. 2022

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Due to its ability to supply reliable, robust and scalable computational power, cloud computing is becoming increasingly popular in industry, government, and academia. High-speed networks connect both virtual and real machines in cloud computing data centres. The system’s dynamic provisioning environment depends on the requirements of end-user computer resources. Hence, the operational costs of a particular data center are relatively high. To meet service level agreements (SLAs), it is essential to assign an appropriate maximum number of resources. Virtualization is a fundamental technology used in cloud computing. It assists cloud providers to manage data centre resources effectively, and, hence, improves resource usage by creating several virtualmachine (VM) instances. Furthermore, VMs can be dynamically integrated into a few physical nodes based on current resource requirements using live migration, while meeting SLAs. As a result, unoptimised and inefficient VM consolidation can reduce performance when an application is exposed to varying workloads. This paper introduces a new machine-learning-based approach for dynamically integrating VMs based on adaptive predictions of usage thresholds to achieve acceptable service level agreement (SLAs) standards. Dynamic data was generated during runtime to validate the efficiency of the proposed technique compared with other machine learning algorithms.

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Section: Related Workmentioning

confidence: 96%

Machine-Learning-Based Approach for Virtual Machine Allocation and Migration

et al. 2022

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“…This has been a particularly profitable playground for bio-inspired optimization techniques, yielding extensive surveys such as the one in [100], which provides a wide coverage of natureinspired meta-heuristic techniques applied in the area of cloud load balancing. In this line [101] addresses the problem of load balancing in cloud environments by proposing a hybrid Cuckoo Search and Firefly Algorithm, showing a promising performance. An additional approach for load balancing is described in [102], focused on both Fog and Cloud Computing environments.…”

Section: Bio-inspired Computation Applied To Cloud Computing Infrastructuresmentioning

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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“…As compared to meta‐heuristic algorithms, the hybrid meta‐heuristic algorithms use strengths of multiple algorithms to solve a problem. 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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An Efficient Load Balancing Technique based on Cuckoo Search and Firefly Algorithm in Cloud

Cited by 11 publications

References 0 publications

Machine-Learning-Based Approach for Virtual Machine Allocation and Migration

Machine-Learning-Based Approach for Virtual Machine Allocation and Migration

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

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

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