LBRO: Load Balancing for Resource Optimization in Edge Computing

Nayyer, Muhammad Ziad; Raza, Imran; Hussain, Syed Asad; Jamal, M. Hasan; Gillani, Zeeshan; Hur, Soojung; Ashraf, Imran

doi:10.1109/access.2022.3205741

Cited by 16 publications

(2 citation statements)

References 30 publications

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“…To handle LB issues in edge computing, the authors have introduced LB for optimizing resources in a collaborative cloudlet platform [13]. The proposed approach significantly outperforms the traditional edge-based strategy regarding several performance parameters.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load Balancing

Lahande,

Kaveri,

Saini

et al. 2023

IEEE Access

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Cloud computing is a technology that enables the delivery of various computing services over the Internet. The Resource Scheduling (RS) and Load Balancing (LB) mechanisms are essential for the cloud to provide consistent results. The submitted tasks by the users are computed on the cloud platform using its Virtual Machines (VMs). The cloud ensures an ideal LB mechanism, where no VMs will be overloaded or idle. This research paper focuses on this LB mechanism by experimenting in the WorkflowSim environment and computing tasks using the Sipht task dataset. The RS algorithms First Come First Serve (FCFS), Maximum -Minimum (Max -Min), Minimum Completion Time (MCT), Minimum -Minimum (Min -Min), and Round-Robin (RR) are utilized to balance the computational load of VMs. The experiment was conducted in four phases, where the Sipht task dataset varied in task length in each phase. Each phase included sixteen scenarios, where each scenario differed from another by the number of VMs used. The final results of this experiment convey that the load balanced by the algorithms

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Section: Related Workmentioning

confidence: 99%

Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load Balancing

Lahande,

Kaveri,

Saini

et al. 2023

IEEE Access

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show abstract

“…The concept of smart healthcare involves the utilization of technological advancements, such as wearable devices, the Internet of Things, and mobile Internet, to facilitate the dynamic acquisition of information. This information is then utilized to establish connections between individuals, resources, and institutions related to healthcare [2]. The ultimate goal of this system is to actively manage and respond to the needs of the medical ecosystem intelligently and e ciently.…”

mentioning

confidence: 99%

Implementation of Delay-Sensitive Smart Healthcare Framework in Cloud and Fog Environment

Rajpoot,

Singh,

Pant

2023

Preprint

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Smart healthcare systems are novel innovations that can improve healthcare services by giving individuals details about their medical conditions in real time. However, processing and analyzing huge amounts of information stored in these systems can frequently result in latency and interruptions, negatively impacting the system's efficiency. To solve this issue, this research presents an innovative healthcare framework incorporating cloud and fog computing techniques to reduce latency and enhance whole system performance. The framework has three different tiers: the user tier, the fog tier, and the cloud tier. Health-related information is gathered from users in the user tier using cloud pulses. An optimized Ant Colony Optimization load balancing algorithm subsequently monitors this data. The load balancer allocates user requests to fog servers in the fog tier, considering factors including response time and cost. The fog tier is comprised of fog servers that are located in closer proximity to end-users and are tasked with the real-time processing and analysis of data. The cloud tier accommodates vast quantities of healthcare data and facilitates sophisticated analytics and processing functionalities. The performance of the proposed framework was assessed by implementing it on the Cloud Analyst tool and utilizing metrics such as response time, cost, and system throughput. The experiment findings indicate that the proposed framework performs better in mitigating delay in healthcare compared to conventional healthcare systems. The load balancer that was optimized using Ant Colony Optimization algorithm efficiently allocated the workload among the fog servers, resulting in enhanced system response time and decreased latency. The concept of fog computing proved to be highly productive in mitigating latency by enabling local data processing and analysis near the end-user, thereby reducing reliance on the cloud tier. The empirical findings indicate that the framework put forward can transform the provision of healthcare services, rendering them more streamlined, economical, and focused on the needs of patients.

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Reliable information delivery and dynamic link utilization in MANET cloud using deep reinforcement learning

Kuang,

Zhang,

Mohajer

2024

Trans Emerging Tel Tech

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Modern networking demands efficient and reliable information delivery within Mobile Ad‐hoc Network (MANET) and cloud environments. This paper introduces a novel approach that employs Multi‐Agent Deep Learning (MADL) for adaptive resource allocation, addressing the challenges of optimizing traffic and ensuring dependable information delivery while adhering to Service Level Agreement (SLA) constraints. Our method dynamically allocates resources across nodes, leveraging the synergy between Advanced Cloud Computing and Edge Computing to balance centralized processing and localized adaptability. The integration of Graph Neural Networks (GNNs) further enhances this process by adapting resource allocation decisions based on network topology. Through iterative learning, our algorithm fine‐tunes continuous‐time resource optimization policies, resulting in substantial improvements in throughput and latency minimization. Simulations validate the effectiveness of our approach, demonstrating its potential to contribute to the advancement of MANET cloud networks by offering adaptability, efficiency, and real‐time optimization for reliable information delivery and dynamic link utilization.

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LBRO: Load Balancing for Resource Optimization in Edge Computing

Cited by 16 publications

References 30 publications

Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load Balancing

Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load Balancing

Implementation of Delay-Sensitive Smart Healthcare Framework in Cloud and Fog Environment

Reliable information delivery and dynamic link utilization in MANET cloud using deep reinforcement learning

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