Analyzing the theoretical merits of Loxi load balancer for improving the efficiency of load balancing in 5G‐edge IoT applications based on Kubernetes DOI

R. Vijayakumar,

Manisha Mali,

Sonali A. Patil

и другие.

Internet Technology Letters, Год журнала: 2024, Номер unknown

Опубликована: Июль 29, 2024

Abstract Load balancing, a critical aspect of cloud and cloud‐based applications, is major challenge that demands our attention. Due to the increasing dynamic workloads, load balancing becomes more important in cloud. One hyperscale models stands out for its ability efficiently balance by scaling allocating resources Loxi‐Load‐Balancer (LLB). This paper explores explicitly LLB's application context 5G‐Edge IoT applications based on Kubernetes. unique features, such as open‐source nature cloud‐native loads, use eBPF core engine avoid adding additional software modules configure kernel, change services using existing layers, set it apart from other balancers. These features provide high security, observability, networking. delves into how LLB used Kubernetes increase speed flexibility customizable services. automates all internal external administrations concerning monitoring, deployment, scaling, migration, routing, configuration, resource allocation. focused developing an efficient allocation management system Loxi‐Load‐Balancer‐extended Berkeley Packet Filter (LLB‐eBPF). Detailed information about LLB‐eBPF‐Kubernetes given this help you understand basics LLB, eBPF,

Язык: Английский

Grid-connected desalination plant economic management powered by renewable resources utilizing Niching Chimp Optimization and hunger game search algorithms DOI

Yuanshuo Guo,

Yassine Bouteraa, Mohammad Khishe

и другие.

Sustainable Computing Informatics and Systems, Год журнала: 2024, Номер 42, С. 100976 - 100976

Опубликована: Фев. 2, 2024

Язык: Английский

Процитировано

8
A Comparative Analysis of Metaheuristic Techniques for High Availability Systems DOI Creative Commons
Darakhshan Syed, Ghulam Muhammad Shaikh, Hani Alshahrani

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 7382 - 7398

Опубликована: Янв. 1, 2024

In the ever-evolving technological landscape, ensuring high system availability has become a paramount concern. This research paper focuses on cloud computing, domain witnessing exponential growth and emerging as critical use case for high-availability systems. To fulfil criteria, many services in infrastructures should be combined, relying user's demands. Central to this study is load balancing, an integral element harnessing full potential of heterogeneous computing environments, dynamic management balancing crucial. explores how virtual machines can effectively remap resources response fluctuating loads dynamically, optimizing overall network performance. The core involves in-depth analysis several metaheuristic algorithms applied computing. These include Genetic Algorithm, Particle Swarm Optimization, Ant Colony Artificial Bee Colony, Grey Wolf Optimization. Utilizing CloudAnalyst, conducts comparative these techniques, focusing key performance metrics such Total Response Time (TRT) Data Center Processing (DCPT). findings offer insights into varying behaviors under different configurations user retention levels. ultimate aim pave way developing innovative load-balancing strategies By providing comprehensive evaluation existing methods, contributes advancing systems, underscoring importance tailored solutions realm technology.

Язык: Английский

Процитировано

4
Optimizing Cloud Resource Management with an IoT-enabled Optimized Virtual Machine Migration Scheme for Improved Efficiency DOI
Chunjing Liu, Lixiang Ma, M. Zhang

и другие.

Journal of Network and Computer Applications, Год журнала: 2025, Номер unknown, С. 104137 - 104137

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms DOI Creative Commons
Darakhshan Syed, Ghulam Muhammad,

Safdar Rizvi

и другие.

Intelligent Automation & Soft Computing, Год журнала: 2024, Номер 39(3), С. 437 - 476

Опубликована: Янв. 1, 2024

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

Язык: Английский

Процитировано

2
Classification of Load Balancing Optimization Algorithms in Cloud Computing: A Survey Based on Methodology DOI
Elaheh Moharamkhani, Reyhaneh Babaei Garmaroodi, Mehdi Darbandi

и другие.

Wireless Personal Communications, Год журнала: 2024, Номер 136(4), С. 2069 - 2103

Опубликована: Июнь 1, 2024

Язык: Английский

Процитировано

2
Hybrid Markov Chain-Based Dynamic Scheduling to Improve Load Balancing Performance in Fog-Cloud Environment DOI
Navid Khaledian,

Shiva Razzaghzadeh,

Zeynab Haghbayan

и другие.

Sustainable Computing Informatics and Systems, Год журнала: 2024, Номер unknown, С. 101077 - 101077

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

2
Secure data transmission in cloud computing using a cyber-security trust model with multi-risk protection scheme in smart IOT application DOI

Torana Kamble,

Madhuri Ghuge,

Ritu Jain

и другие.

Cluster Computing, Год журнала: 2024, Номер 28(2)

Опубликована: Ноя. 26, 2024

Язык: Английский

Процитировано

1
Optimizing low-power task scheduling for multiple users and servers in mobile edge computing by the MUMS framework DOI Creative Commons
Guangxu Li, Junke Li

Heliyon, Год журнала: 2024, Номер 10(11), С. e31622 - e31622

Опубликована: Май 23, 2024

In today's increasingly popular Internet of Things (IoT) technology, its energy consumption issue is also becoming prominent. Currently, the application Mobile Edge Computing (MEC) in IoT important, and scheduling tasks to save imperative. To address aforementioned issues, we propose a Multi-User Multi-Server (MUMS) framework aimed at reducing MEC. The starts with model definition phase, detailing multi-user multi-server systems through four fundamental models: communication, offloading, energy, delay. Then, these models are integrated construct an optimization for MUMS. final step involves utilizing proposed L1_PSO (an enhanced version standard particle swarm algorithm) solve problem. Experimental results demonstrate that, compared typical algorithms, MUMS both reasonable feasible. Notably, algorithm reduces by 4.6% Random Assignment 2.3% conventional Particle Swarm Optimization algorithm.

Язык: Английский

Процитировано

0
Analyzing the theoretical merits of Loxi load balancer for improving the efficiency of load balancing in 5G‐edge IoT applications based on Kubernetes DOI

R. Vijayakumar,

Manisha Mali,

Sonali A. Patil

и другие.

Internet Technology Letters, Год журнала: 2024, Номер unknown

Опубликована: Июль 29, 2024

Abstract Load balancing, a critical aspect of cloud and cloud‐based applications, is major challenge that demands our attention. Due to the increasing dynamic workloads, load balancing becomes more important in cloud. One hyperscale models stands out for its ability efficiently balance by scaling allocating resources Loxi‐Load‐Balancer (LLB). This paper explores explicitly LLB's application context 5G‐Edge IoT applications based on Kubernetes. unique features, such as open‐source nature cloud‐native loads, use eBPF core engine avoid adding additional software modules configure kernel, change services using existing layers, set it apart from other balancers. These features provide high security, observability, networking. delves into how LLB used Kubernetes increase speed flexibility customizable services. automates all internal external administrations concerning monitoring, deployment, scaling, migration, routing, configuration, resource allocation. focused developing an efficient allocation management system Loxi‐Load‐Balancer‐extended Berkeley Packet Filter (LLB‐eBPF). Detailed information about LLB‐eBPF‐Kubernetes given this help you understand basics LLB, eBPF,

Язык: Английский

Процитировано

0