Utility based load balancing using firefly algorithm in cloud

Tapale, Manisha T.; Goudar, R. H.; Birje, Mahantesh N.; Patil, Rudragouda S.

doi:10.1007/s42488-020-00022-2

Cited by 14 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper [25] proposed a distributed scheduling algorithm for resource-intensive mobile applications using the Lagrangian method. A bargaining protocol to maximize resource utilization and profit was introduced in [26]. It balanced the load by distributing jobs to reliable VMs using FA.…”

Section: Related Workmentioning

confidence: 99%

“…Simulation has been carried out on a 64-bit Windows 8 machine having Intel Core i3 and 4 GB RAM using the Cloud Analyst tool with Eclipse Java Neon.3 IDE. The proposed work is compared with dynamic algorithms ACOQDM [24], DSOA [25], UFA [26], in two different scenarios, i.e., different number of tasks and different number of VMs. Two datasets, NASA and CLARKNET [24], are used for evaluation purposes.…”

Section: Simulation Setupmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy Firefly Based Intelligent Algorithm for Load Balancing in Mobile Cloud Computing

Poonam¹,

Sangwan²

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan. The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly. It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function. It works for 3-tier architecture, including cloudlet and public cloud. As cloudlets have limited resources, fuzzy logic is used for cloudlet selection using capacity and waiting time as input. Fuzzy provides human-like decisions without using any mathematical model. Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed. It balances the load on cloud and cloudlet while minimizing makespan and execution time. However, it may trap in local optimum; levy flight can handle it. Hybridization of fuzzy firefly with levy flight is a novel technique that provides reduced makespan, execution time, and Degree of imbalance while balancing the load. Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration (NASA) and Clarknet datasets. Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker (ACOQDM), Distributed Scheduling Optimization Algorithm (DSOA), and Utility-based Firefly Algorithm (UFA) when compared in terms of makespan, Degree of imbalance, and Figure of Merit.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Simulation Setupmentioning

confidence: 99%

Fuzzy Firefly Based Intelligent Algorithm for Load Balancing in Mobile Cloud Computing

Poonam¹,

Sangwan²

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Tapale et al [6] proposed a modern workflow design that incorporates negotiating algorithms and redistributes resources to cloud VMs to use dependable ones. The suggested simulation is run using the tool and tested against actual work.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Data centers that contain many "virtualized" servers and high-bandwidth networks are the resources of "cloud computing [4,5]. Besides, cloud computing considers as a cost-effective paradigm due to resource sharing [6].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Survey for Cloud Computing Based Nature-Inspired Algorithms Optimization Scheduling

Yahia

Zeebaree

Sadeeq

et al. 2021

AJRCoS

View full text Add to dashboard Cite

Many applications in the real world include optimizing specific targets, such as cost minimization, energy conservation, climate, and maximizing production, efficiency, and sustainability. The optimization problem is strongly non-linear with multifunctional landscapes under several dynamic, non-linear constraints in some instances. It is challenging to address those issues. Also, with the increasing strength of modern computers, simplistic brute force methods are still inefficient and unwanted. Practical algorithms are also vital for these implementations whenever possible. Cloud computing has become an essential and popular emerging computing environment that supports on-demand services and provides internet-based services. Cloud computing allows a range of services and tools to be easily accessed from anywhere in the world. Since cloud computing has global access to its services, there will always be threats and challenges facing its servers and services, such as; task scheduling, security, energy efficiency, network load, and other challenges. In the research area, many algorithms have been addressed to solve these problems. This paper investigates relevant analysis and surveys on the above topics, threats, and outlooks. This paper offers an overview of nature-inspired algorithms, their applications, and valuation, emphasizing cloud computing problems. Many problems in science and engineering can be viewed as optimization problems with complex non-linear constraints. Highly nonlinear solutions typically need advanced optimization algorithms, and conventional algorithms can have difficulty addressing these issues. Because of its simplicity and usefulness, nature-inspired algorithms are currently being used. There are nevertheless some significant concerns with computing and swarming intelligence influenced by evolution.

show abstract

“…The degree of imbalance is calculated to accurately test the efficiency of the proposed load balancing algorithm, which indicates that the suggested LBA achieves better results for a greater number of allocated cloudlets. Tapale et al [43] proposed an approach on utility-based load balancing that uses the firefly algorithm to upgrade the utilization of resources and optimizes the service provider's profit using bargaining protocol. The method avoids starvation of tasks by taking into account the multi-level queues and decreases the load imbalance and makespan.…”

Section: General Load Balancing Techniquesmentioning

confidence: 99%

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

2021

IJATCSE

View full text Add to dashboard Cite

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

show abstract

Utility based load balancing using firefly algorithm in cloud

Cited by 14 publications

References 27 publications

Fuzzy Firefly Based Intelligent Algorithm for Load Balancing in Mobile Cloud Computing

Fuzzy Firefly Based Intelligent Algorithm for Load Balancing in Mobile Cloud Computing

Comprehensive Survey for Cloud Computing Based Nature-Inspired Algorithms Optimization Scheduling

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

Contact Info

Product

Resources

About