K. R. Aravind Britto

Bio: K. R. Aravind Britto is an academic researcher from PSNA College of Engineering and Technology. The author has contributed to research in topics: Load balancing (computing) & Cloud computing. The author has an hindex of 2, co-authored 9 publications receiving 29 citations.

FDLA: Fractional Dragonfly based Load balancing Algorithm in cluster cloud model

Abstract: Cloud computing is a developing technology that enables on-demand network access to the users through a shared pool of cluster computing resources. However, maintaining the stability of processing several tasks in the cloud environment is a complex issue. Hence, it requires a load balancing technique that allocates the task to the Virtual Machines (VMs) without affecting the performance of the system. This paper presents a technique for load balancing, called fractional dragonfly based load balancing algorithm (FDLA), by proposing two selection probabilities and fractional dragonfly algorithm. The proposed load balancing model utilizes certain parameters of VMs and Physical Machines (PMs) to select the tasks to be reallocated in the VMs for load balancing. The selection is based on the probabilities, Task selection probability (TSP) and VM selection probability (VSP), which are newly designed. Further, the proposed fractional dragonfly algorithm that combines dragonfly algorithm (DA) with Fractional Calculus (FC) performs an optimal selection of VMs for the reallocation of the task using a newly designed fitness function. In the performance analysis of FDLA based on load and number of tasks reallocated, the proposed FDLA could achieve a minimum load of 0.2133 with 14 reallocated tasks, indicating its effectiveness in load balancing.

Star Hotel Hospitality Load Balancing Technique in Cloud Computing Environment

Abstract: Cloud computing technology is making advancement recently. Automated service provisioning, load balancing, virtual machine task migration, algorithm complexity, resource allocation, and scheduling are used to make improvements in the quality of service in the cloud environment. Load balancing is an NP-hard problem. The main objective of the proposed work is to achieve low makespan and minimum task execution time. An experimental result proved that the proposed algorithm performs good load balancing than Firefly algorithm, Honey Bee Behavior-inspired Load Balancing (HBB-LB), and Particle Swarm Optimization (PSO) algorithm.

Solar panel integrated compact magneto-dielectric loaded sequentially rotated navigation antenna

Abstract: In this paper we present a novel solar panel integrated navigation antenna for solar powered vehicles and devices. The radiator is a compact sequentially rotated patch array loaded with a magneto-dielectric (MD) layer to achieve miniaturization for ease of integration with solar panels. The magneto-dielectric substrate of the proposed antenna consists of MD unit cells created by vertical split ring resonators (SRR). The unit cells are arranged in 11 × 1 fashion beneath each radiator to reduce the dimension of the same to 70% with equivalent performance compared with the respective original patch radiators. An array of such radiators is formed and fed with a sequential feed network comprising T-junction power dividers and delay lines. The proposed antenna is then integrated and tuned with a solar panel and measured. The measured results show that the antenna operates in the L1 GPS band with an impedance bandwidth of 20 MHz centered at 1.575 GHz. The antenna is Right Handed Circularly Polarized (RHCP) with axial ratio bandwidth (ARBW) of 100% due to the sequential rotation. The peak Axial Ratio (AR) values are

Magnum opus of an efficient hospitality technique for load balancing in cloud environment

Abstract: The load balancing approach in the cloud environment is an open issue in the recent days. It helps obtain better resource utilization in cloud environment. It also improves the performance of the system. A good load balancing algorithm in cloud ensures no under loading or over loading in a single host. This paper proposes an algorithm named Hospitality Load Balancing (HLB) algorithm, which aims to balance the load among virtual machines (VM) for increasing the performance and throughput. The proposed algorithm handles preemptive or nonpreemptive tasks, which may be independent. It helps to reduce make span and obtain low task migration. The HLB algorithm has been analyzed and compared with plenty of load balancing technique. An experimental result shows that it is an excellent and effective algorithm when compared with the existing load balancing algorithm. It also illustrates the performance of proposed load balancing scheme with analysis and proves that it attains low makespan and low task migration.

IoT-Based Applications in Healthcare Devices.

Abstract: The last decade has witnessed extensive research in the field of healthcare services and their technological upgradation. To be more specific, the Internet of Things (IoT) has shown potential application in connecting various medical devices, sensors, and healthcare professionals to provide quality medical services in a remote location. This has improved patient safety, reduced healthcare costs, enhanced the accessibility of healthcare services, and increased operational efficiency in the healthcare industry. The current study gives an up-to-date summary of the potential healthcare applications of IoT- (HIoT-) based technologies. Herein, the advancement of the application of the HIoT has been reported from the perspective of enabling technologies, healthcare services, and applications in solving various healthcare issues. Moreover, potential challenges and issues in the HIoT system are also discussed. In sum, the current study provides a comprehensive source of information regarding the different fields of application of HIoT intending to help future researchers, who have the interest to work and make advancements in the field to gain insight into the topic.

Dragonfly algorithm and its applications in applied science survey

Abstract: One of the most recently developed heuristic optimization algorithms is dragonfly by Mirjalili. Dragonfly algorithm has shown its ability to optimizing different real-world problems. It has three variants. In this work, an overview of the algorithm and its variants is presented. Moreover, the hybridization versions of the algorithm are discussed. Furthermore, the results of the applications that utilized the dragonfly algorithm in applied science are offered in the following area: machine learning, image processing, wireless, and networking. It is then compared with some other metaheuristic algorithms. In addition, the algorithm is tested on the CEC-C06 2019 benchmark functions. The results prove that the algorithm has great exploration ability and its convergence rate is better than the other algorithms in the literature, such as PSO and GA. In general, in this survey, the strong and weak points of the algorithm are discussed. Furthermore, some future works that will help in improving the algorithm’s weak points are recommended. This study is conducted with the hope of offering beneficial information about dragonfly algorithm to the researchers who want to study the algorithm.

Dragonfly algorithm: a comprehensive review and applications

Abstract: Dragonfly algorithm (DA) is a novel swarm intelligence meta-heuristic optimization algorithm inspired by the dynamic and static swarming behaviors of artificial dragonflies in nature. It has proved its effectiveness and superiority compared to several well-known meta-heuristics available in the literature. This paper presents a comprehensive review of DA and its new variants classified into modified and hybrid versions. It also describes the main diverse applications of DA in several fields and areas such as machine learning, neural network, image processing, robotics, and engineering. Finally, the paper suggests some possible interesting research on the applications and hybridizations of DA for future works.

Dragonfly Algorithm: Theory, Literature Review, and Application in Feature Selection

Abstract: In this chapter, a wrapper-based feature selection algorithm is designed and substantiated based on the binary variant of Dragonfly Algorithm (BDA). DA is a successful, well-established metaheuristic that revealed superior efficacy in dealing with various optimization problems including feature selection. In this chapter we are going first present the inspirations and methamatical modeds of DA in details. Then, the performance of this algorithm is tested on a special type of datasets that contain a huge number of features with low number of samples. This type of datasets makes the optimization process harder, because of the large search space, and the lack of adequate samples to train the model. The experimental results showed the ability of DA to deal with this type of datasets better than other optimizers in the literature. Moreover, an extensive literature review for the DA is provided in this chapter.