Hybrid Bat Algorithm for Balancing Load in Cloud Computing

Sharma, Shabnam; Verma, Sahil; Jyoti, Kiran; Kavita, .

doi:10.14419/ijet.v7i4.12.20986

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…So, the proposed change to work summaries is helpful. Load balancing or virtual machine migration after a proposed method corrects workload summarization requires a basic understanding of optimization methods [20][21][22][23][24][25][26]. The recent outcomes also suggest that the summarization process of loads shall lead to the correct identification of the problem [27][28][29].…”

Section: Formulation Of the Research Problem And Proposed Load Summar...mentioning

confidence: 99%

ACO-Inspired Load Balancing Strategy for Cloud-Based Data Centre with燩redictive Machine Learning Approach

Dey¹,

Gunasekhar²,

Purnachand³

2023

Computers, Materials &Amp; Continua

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Virtual Machines are the core of cloud computing and are utilized to get the benefits of cloud computing. Other essential features include portability, recovery after failure, and, most importantly, creating the core mechanism for load balancing. Several study results have been reported in enhancing loadbalancing systems employing stochastic or biogenetic optimization methods. It examines the underlying issues with load balancing and the limitations of present load balance genetic optimization approaches. They are criticized for using higher-order probability distributions, more complicated solution search spaces, and adding factors to improve decision-making skills. Thus, this paper explores the possibility of summarizing load characteristics. Second, this study offers an improved prediction technique for pheromone level prediction over other typical genetic optimization methods during load balancing. It also uses web-based third-party cloud service providers to test and validate the principles provided in this study. It also reduces VM migrations, time complexity, and service level agreements compared to other parallel standard approaches.

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Section: Formulation Of the Research Problem And Proposed Load Summar...mentioning

confidence: 99%

ACO-Inspired Load Balancing Strategy for Cloud-Based Data Centre with燩redictive Machine Learning Approach

Dey¹,

Gunasekhar²,

Purnachand³

2023

Computers, Materials &Amp; Continua

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“…Arabnejad H et al [6] found that although these algorithms achieve good outcomes, their great temporal complexity makes them less suitable for real-world computers. In their investigation, Shabnam et al [7] utilized the bat algorithm. For load balancing and virtual machine optimization, a hybrid strategy is required.…”

Section: Literature Surveymentioning

confidence: 99%

“…. CT m )dct max (7) As restrictions for updating and optimising, the sigmoid function and the minimum completion time for unique VMs are utilised, respectively. The computation of these constraints is detailed in Section 3.5.4.…”

Section: Objective Functionmentioning

confidence: 99%

Optimization of Load Balancing and Task Scheduling in Cloud Computing Environments Using Artificial Neural Networks-Based Binary Particle Swarm Optimization (BPSO)

Alghamdi

2022

Sustainability

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As more people utilize the cloud, more employment opportunities become available. With constraints such as a limited make-span, a high utilization rate of available resources, minimal execution costs, and a rapid turnaround time for scheduling, this becomes an NP-hard optimization issue. The number of solutions/combinations increases exponentially with the magnitude of the challenge, such as the number of tasks and the number of computing resources, making the task scheduling problem NP-hard. As a result, achieving the optimum scheduling of user tasks is difficult. An intelligent resource allocation system can significantly cut down the costs and waste of resources. For instance, binary particle swarm optimization (BPSO) was created to combat ineffective heuristic approaches. However, the optimal solution will not be produced if these algorithms are not paired with additional heuristic or meta-heuristic algorithms. Due to the high temporal complexity of these algorithms, they are less useful in real-world settings. For the NP problem, the binary variation of PSO is presented for workload scheduling and balancing in cloud computing. Considering the updating and optimization constraints stated in this research, our objective function determines if heterogeneous virtual machines (VMs) Phave the most significant difference in completion time. In conjunction with load balancing, we developed a method for updating the placements of particles. According to the experiment results, the proposed method surpasses existing metaheuristic and heuristic algorithms regarding work scheduling and load balancing. This level of success has been attainable because of the application of Artificial Neural Networks (ANN). ANN has demonstrated promising outcomes in resource distribution. ANN is more accurate and faster than multilayer perceptron networks at predicting targets.

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“…Other studies look into the monitoring of cloud resources [4] to help make scheduling and placement decisions. Some studies also look into the optimisation of the decision-making process by employing specific algorithms such as the hybrid bat algorithm [101] and other load balancing techniques [95,97] to enhance data processing efficiency. A survey of "task scheduling in the hybrid cloud" [27] attempts to classify the techniques and their relative execution costs.…”

Section: Task Schedulingmentioning

confidence: 99%

Improving Data-sharing and Policy Compliance in a Hybrid Cloud: The Case of a Healthcare Provider

Azumah¹

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CloudSim was used for task scheduling simulations; MoBuCon tool was used for process mining and visualisation of constraint violations; OpenStack Octavia was used in load-balancing experiments and logging; CPN Tools was used to simulate and visualise dynamics of load-balancing in a hybrid cloud and GNU Octave was used in generating the plots. Acknowledgement"You can make many plans, but the Lord's purpose will prevail." -Prov. 19:21 (NLT) Finally, at the end of this Ph.D. journey, I have the singular privilege of thanking personalities who have helped in divers ways to bring the work this far. To God be all the glory! Firstly, my deepest appreciation to the Chair and Members of Assessment Committee who agreed to spend valuable time in evaluating this thesis.Secondly, a major thanks goes to Professor Knud Erik Skouby, immediate past director of the centre for Communication, Media and Information technologies (CMI) for giving us the opportunity to work at the Center and paving the way for us to learn at many conferences and workshops. A major thanks also goes to Professor Reza Tadayoni, present director of CMI, for playing the initial role of technical supervisor at the formative stages of my Ph.D. journey.I express a deep and heartfelt gratitude to Professors Lene Tolstrup Sørensen and Sokol Kosta, my inspiring supervisors, who laboured day and night to ensure the rough ideas blossomed into interesting article publications.Many thanks to Professor Anders Henten, the Ph.D. School liaison in CMI, for advising on the legal fulfilments and also to the entire leadership of the AAU Ph.D. School for the continuous efforts to ease the academic lives of Ph.D. Fellows.To my co-authors Prof. Paulo R.M. Maciel and Prof. Raffaele Montella, I say thank you for your contributions and for the opportunity to collaborate.I am also thankful for the support I enjoyed from Staff and Ph.D. Fellows of CMI, with special mention to Anette Bysøe, Idongesit Williams, Alexander Osei Owusu and Joseph Wireku for their divers helps.A special thank you to my parents, siblings, wife and children for the love, support and understanding over the years on this arduous but exciting journey."We must be willing to let go of the life we planned so as to have the life that is waiting for us." -Joseph Campbell

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Hybrid Bat Algorithm for Balancing Load in Cloud Computing

Cited by 8 publications

References 15 publications

ACO-Inspired Load Balancing Strategy for Cloud-Based Data Centre with燩redictive Machine Learning Approach

ACO-Inspired Load Balancing Strategy for Cloud-Based Data Centre with燩redictive Machine Learning Approach

Optimization of Load Balancing and Task Scheduling in Cloud Computing Environments Using Artificial Neural Networks-Based Binary Particle Swarm Optimization (BPSO)

Improving Data-sharing and Policy Compliance in a Hybrid Cloud: The Case of a Healthcare Provider

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