Cloud computing approaches in health care

Faridi, Farhan; Sarwar, Huzefa; Ahtisham,; Kumar, Sarvesh; Jamal, Khalid

doi:10.1016/j.matpr.2021.07.210

Cited by 12 publications

(4 citation statements)

References 32 publications

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“…Heuristic are proved better when the solutions are needed immediately, these algorithms helps to achieve a decent solution, whereas optimization based algorithms are used to find the most optimal solution of a problem i.e. They help to reach the global [99].Different Nature-Inspired [18] load-balancing algorithms can be categorized in two ways: swarm based load load-balancing algorithms and evolutionary based LB algorithms Swarmbased LB algorithms are further decomposed to ten types: particle swarm optimization (PSO) algorithms [55][56][57][58], ant colony optimization (ACO) [30][31][32][33][34][35] algorithms, artificial bee colony algorithms, bees algorithms [75], cuckoo algorithms [101], differential Evolution algorithms, flower algorithms, and bat algorithms. Similarly, Evolutionary based algorithms are grouped as genetic algorithm [15], genetic programming, evolutionary programming, Neuro Evolution, learning classifier system [29], gene expression programming, differential evolution, and firefly algorithms [5] as illustrated in Fig 5 . B. Metrices for LB in Cloud Computing These are the parameters which determine the efficiency of algorithms also called performance of the algorithm.…”

Section: A Taxanomy Of Lb Algorithmsmentioning

confidence: 99%

Load Balancing in Cloud Environment: A State-of-the-Art Review

Lohumi,

Gangodkar,

Srivastava

et al. 2023

IEEE Access

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IT services and resources on-demand through Internetwork are offered by Cloud Computing (CC), including the pay-for-you-go aspect. A lot is offered by the CC paradigm, such as Infrastructure related services, computing services, storage, and environments for deployment are also provided. The objective of this study is to survey one of the significant challenges in cloud computing, which is a multi-variant, multi-constraint issue termed Load unbalancing, resulting in the demising of the scalability, efficiency, and performance of the system. Equilibrium in the server workload distribution is still strived for by cloud service providers. The unbalancing issue is resolved by load balancing solutions in two ways: overloading and underloading. An extensive structural literature analysis of Load balancing and its constituent domains with the inclusion of various parameters, such as scalability, make-span, and throughput, are depicted in this research paper to enhance the QoS. A detailed and organized taxonomy of all the Load balancing algorithms based on nature system state, techniques, functionality, and types is also presented. The major focus of the survey is around the Static, dynamic, hybrid, and nature-inspired Load-Balancing algorithms.

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Section: A Taxanomy Of Lb Algorithmsmentioning

confidence: 99%

Load Balancing in Cloud Environment: A State-of-the-Art Review

Lohumi,

Gangodkar,

Srivastava

et al. 2023

IEEE Access

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“…Smart healthcare systems based on cloud-Internet integration illustrate the connectivity potential for data sharing and analysis [15]. Cloud computing approaches in healthcare, encompassing health monitoring applications, provide a holistic overview of research and applications in the field [16]. Cloud connectivity plays a pivotal role in enhancing healthcare monitoring, providing a foundation for innovation, efficiency, and improved patient care.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Remote Patient Monitoring: A Review of Biosensors Integrated with Multi-Hop IoT Systems via Cloud Connectivity

Uddin,

Koo

2024

Applied Sciences

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This comprehensive review paper explores the intricate integration of biosensors with multi-hop Internet of Things (IoT) systems, representing a paradigm shift in healthcare through real-time remote patient monitoring. The strategic deployment of biosensors in different locations in medical facilities, intricately connected to multiple microcontrollers, serves as a cornerstone in the establishment of robust multi-hop IoT networks. This paper highlights the role of this multi-hop IoT network, which efficiently facilitates the seamless transmission of vital health data to a centralized server. Crucially, the utilization of cloud connectivity emerges as a linchpin in this integration, providing a secure and scalable platform for remote patient monitoring. This cloud-based approach not only improves the accessibility of critical health information but also transcends physical limitations, allowing healthcare providers to monitor patients in real-time from any location. This paper highlights the transformative potential of this integration in overcoming traditional healthcare limitations through real-time remote patient monitoring.

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“…The landscape of computational technology has been profoundly transformed by the advent of cloud computing, which has redefined the paradigms of resource allocation, scalability, and computing on demand [1][2][3]. As businesses and individuals increasingly rely on cloud services for a wide range of applications, the efficiency and effectiveness of these services have become paramount [4].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Cloudlet Scheduling for Optimized Execution Time in Cloud Computing Environments

Yadav,

Varshney,

Kumar

2023

J. Comput. Mech. Manag

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Cloud computing has become a cornerstone of modern IT infrastructure, offering scalable and flexible resources. However, efficient resource management, particularly cloudlet scheduling, presents a significant challenge due to its NP-hard nature. This paper introduces a novel heuristic-based cloudlet scheduling algorithm aimed at minimizing execution time and improving load balancing in cloud computing environments. We detail the development and implementation of the algorithm, along with a simulation setup using the CloudSim toolkit to evaluate its performance against existing methods. Results from extensive simulations demonstrate that the proposed algorithm consistently reduces turnaround times, thus optimizing resource allocation. The findings suggest that our approach can significantly impact cloud computing efficiency, paving the way for improved service provider offerings and user satisfaction. The implications of these advancements are discussed, alongside potential directions for future research in dynamic cloud environments.

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Cloud computing approaches in health care

Cited by 12 publications

References 32 publications

Load Balancing in Cloud Environment: A State-of-the-Art Review

Load Balancing in Cloud Environment: A State-of-the-Art Review

Real-Time Remote Patient Monitoring: A Review of Biosensors Integrated with Multi-Hop IoT Systems via Cloud Connectivity

Intelligent Cloudlet Scheduling for Optimized Execution Time in Cloud Computing Environments

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