Comparison Study of Load Balancing Algorithm

Rai, Himanshu; Ojha, Sanjeev Kumar; Nazarov, A. S.

doi:10.1109/icacccn51052.2020.9362905

Cited by 5 publications

(6 citation statements)

References 21 publications

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“…Distributing network tra c using cloud load balancing promotes e ciency and dependability in a cloud environment. The goal is to speed up responses while maximizing available resources [9].…”

Section: Load Balancingmentioning

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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Section: Load Balancingmentioning

confidence: 99%

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

Rajpoot,

Singh,

Pant

2023

Preprint

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“…First of all, paper [10] analyzes and compares the existing main load balancing algorithms, and points out that load balancing algorithms can effectively improve the resource utilization, response rate and throughput of server clusters, proving that a reasonable load balancing algorithm is the key to improve the system service performance. Many researchers have proposed a series of load balancing algorithms to solve the problems of overload, load skewing, low efficiency and resource optimization in the system.…”

Section: Related Workmentioning

confidence: 99%

“…Firstly, each vertex labelling l ' (u) in the weighted complete bipartite graph G 3 is modified according to the following equation and verified according to (10).…”

Section: ) Step 3: Find Another Equal Subgraph K L ' Of the Weighted ...mentioning

confidence: 99%

“…Algorithm 1 Dynamic load balancing algorithm based on optimal matching of weighted bipartite graph Input: Task request queue set J, server cluster set S, and server initial load indicator set V Output: The optimal matching H (1) while (J is not empty) do; // There are tasks pending assignment in the task pool (2) establish task set J n and server set S n ; // Take out an equal number of n tasks as the server (3) compute R according to Equation ( 5); (4) compute T k according to Equation ( 6); (5) establish G 1 with J n and S n ; // Construct edgeless bipartite graph G 1 (6) for (k, i = 1 : n) do (7) compute t ki according to Equation ( 7); (8) establish E by comparing T k and t ki ; // Construct the edge set E (9) compute W nn according to Equation (8); // Compute the edge weight matrix W nn (10) end for (11) establish G 2 with J n , S n , E and W nn ; // Construct the bipartite graph G 2 (12) establish G 3 by adding edges with zero-weight to G 2 ; // Construct the weighted complete bipartite graph G 3 (13) find equal subgraph K L ; (14) while do (15) find the perfect matching M of K L by using the Hungarian algorithm; (16) if (M is non-existent) then (17) find a new K L ' to replace K L ; (18) continue; // Find the perfect matching M again (19) else (20) end while (21) end if (22) get the optimal matching H by deleting edges with zero-weight from perfect matching M; (23) end while (24) return H;…”

Section: ) Step 4: Find the Optimal Matching H Of The Weighted Bipart...mentioning

confidence: 99%

“…The program of management server and server cluster is written through the Java. There are task queues of a certain length in the task pool, and the complexity of each task obeys a uniform distribution in the interval (1,10). The task pool will send the task stream to the management server in real time.…”

Section: Simulationmentioning

confidence: 99%

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Dynamic Load Balancing Algorithm Based on Optimal Matching of Weighted Bipartite Graph

Hou

Meng

et al. 2022

IEEE Access

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When the server cluster is processing concurrent task requests, if the performance difference among servers is not fully considered, task allocation will be unreasonable, which will lead to an increase in task making span and a decrease in cluster resource utilization. As one of the core technologies of server cluster, load balancing is used to balance the load of each server by allocating tasks to each server through an algorithm before the task processing. Therefore, this paper proposes a dynamic load balancing algorithm based on optimal matching of weighted bipartite graph. First, we constructed a bipartite graph with servers and tasks as vertices. The management server collects the load indicators of each server in the cluster in real time, using the real-time processing rate of each server as the load indicator. Each edge of the bipartite graph is determined by comparing the expected completion time of the tasks with the load of each server. The degree of matching between each task amount and each server load capacity is defined as the weight matrix of the edges, and the bipartite graph is weighted to construct a weighted bipartite graph. The Kuhn-Munkres algorithm was used to solve the optimal matching of the weighted bipartite graph, and the optimal assignment of tasks to servers was achieved based on the result of the optimal matching. The proposed algorithm fully considers the differentiation of each task amount and each server load capacity. By building a server cluster example and conducting comparison experiments, it is demonstrated that the algorithm can achieve load balancing of the server cluster and improve the resource utilization efficiency of the cluster, while offsetting the extra time overhead caused by the algorithm.

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