A taxonomy of load balancing algorithms and approaches in fog computing: a survey

Ebneyousef, Sepideh; Shirmarz, Alireza

doi:10.1007/s10586-023-03982-3

Cited by 12 publications

(3 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fog computing, also known as "Edge Computing", is a way of distributing computing and data storage closer to the source of the data. Instead of relying on a central, remote server like in traditional cloud computing, fog computing decentralizes computation power to sensors and devices at the edge of the network [28][29][30]. This is depicted in Figure 2, which highlights the shift towards distributing computation power to edge devices and sensors rather than relying solely on cloud processing.…”

Section: Fog Computingmentioning

confidence: 99%

Review of Intelligent Healthcare for the Internet of Things: Challenges, Techniques and Future Directions

2024

J Sen Net Data Comm

View full text Add to dashboard Cite

Connected devices, sensors, and other technologies can be used to enhance healthcare delivery and administration, and this is what is meant by “intelligent healthcare” for the IoT. Medical care, illness prevention, and overall health management could all be dramatically altered by this discovery. The Internet of Things (IoT), Big Data, AI, and ML have all come a long way, and their expanding use in our daily lives makes it evident that technology will play a crucial role in the future of healthcare. These technological advancements, after overcoming technical hurdles like dealing with huge volumes of data, present a variety of ethical and security problems that must be addressed for the successful integration of technology in healthcare. Ethical concerns around the use of AI and ML technologies, as well as issues of getting people used to working alongside machines. These obstacles need to be dealt with and conquered if smart healthcare is ever going to reach its full potential and enhance people’s lives through the integration of technology and healthcare. The complexity of IoT-enabled smart healthcare in the present day is the subject of this research.

show abstract

Section: Fog Computingmentioning

confidence: 99%

Review of Intelligent Healthcare for the Internet of Things: Challenges, Techniques and Future Directions

2024

J Sen Net Data Comm

View full text Add to dashboard Cite

show abstract

“…Furthermore, many of these review papers have limitations in terms of the factors used to analyze the performance parameters of load balancing. These QoS parameters include response time, migration cost, energy conservation, throughput, convergence, and scalability [40][41][42][43][44][45]. None of the existing reviews has provided an appropriate classification for classifying various load balancing strategies.…”

Section: Research Motivationmentioning

confidence: 99%

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Syed,

Muhammad,

Rizvi

2024

IASC

View full text Add to dashboard Cite

Cloud Computing has the ability to provide on-demand access to a shared resource pool. It has completely changed the way businesses are managed, implement applications, and provide services. The rise in popularity has led to a significant increase in the user demand for services. However, in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization. This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms. Specifically, metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic. In a cloud-based context, this paper describes load balancing measurements, including the benefits and drawbacks associated with the selected load balancing techniques. It also summarizes the algorithms based on implementation, time complexity, adaptability, associated issue(s), and targeted QoS parameters. Additionally, the analysis evaluates the tools and instruments utilized in each investigated study. Moreover, comparative analysis among static, traditional dynamic and metaheuristic algorithms based on response time by using the CloudSim simulation tool is also performed. Finally, the key open problems and potential directions for the state-of-the-art metaheuristic-based approaches are also addressed.

show abstract

“…However, this algorithm may be less than optimal when the servers have varying specifications, as it can lead to some servers becoming overloaded due to the cyclic load distribution. In the Least-connection Algorithm, server selection is determined by the server with the fewest active connections [12]. The rationale behind this approach is to maintain optimal server performance for serving new users.…”

Section: Introductionmentioning

confidence: 99%

Improving Moodle Performance Using HAProxy and MariaDB Galera Cluster

Prakasa,

Hanani,

Hariri

et al. 2024

Appl. Inf. Syst. Manage.

View full text Add to dashboard Cite

Moodle is a widely used Learning Management System in various educational institutions worldwide. However, frequent reports on internet forums indicate performance degradation when massive simultaneous users access Moodle. One of the most resource-intensive components supporting Moodle is the database, as all user-accessed data is stored in it. This study aims to optimize Moodle’s performance through distributed databases. Distributing the database into multiple database servers allows the database load to be distributed across all the database servers, resulting in an overall improvement in Moodle performance. This study compares the performance of Moodle installed on a single server with that installed on multiple database servers. Various testing parameters are employed to get valid results. Namely, course read, course write, and database performance, utilizing the server performance plugin available in Moodle. This research reveals a performance improvement of 384% in course read, 193% in course write, and 260% in the Moodle database in the multi-server scenario compared to the single-server scenario. This result validates that the database is the most crucial part of Moodle.

show abstract

A taxonomy of load balancing algorithms and approaches in fog computing: a survey

Cited by 12 publications

References 40 publications

Review of Intelligent Healthcare for the Internet of Things: Challenges, Techniques and Future Directions

Review of Intelligent Healthcare for the Internet of Things: Challenges, Techniques and Future Directions

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Improving Moodle Performance Using HAProxy and MariaDB Galera Cluster

Contact Info

Product

Resources

About