Analysis of Different Load Balancing Algorithms in Cloud Computing

Chahal, Poonam; Bura, Deepa; Singh, Meeta; Kumar, S.

doi:10.4018/ijcac.2021100106

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…In cloud computing environments, load balancing is implemented by distributing user application workloads and network traffic across multiple virtual machines (VMs) Provisioned across geographically distributed data centers [56]. The load balancer monitors the VMs and transparently directs incoming requests based on optimization algorithms and policies.…”

Section: Load Balancingmentioning

confidence: 99%

Adaptive Threshold Tuning-based Load Balancing (ATTLB) for Cost Minimization in Cloud Computing

Khoshaim

2024

IJACSA

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Cloud computing has revolutionized the ondemand resource provisioning through virtualization. However, dynamic pricing of cloud resources presents cost management challenges. Load balancing is critical for cloud efficiency; however, current algorithms use static thresholds and are unable to adapt to fluctuating prices. This study proposes a novel Dynamic Threshold Tuning (ATTLB) algorithm that optimizes the CPU and memory thresholds of a load balancer based on real-time pricing. The ATTLB algorithm has a pricing monitor to track spot prices; a VM profiler to record capacities; a threshold optimizer to tune thresholds based on pricing, capacity, and SLAs; and a load dispatcher to assign requests to VMs using the optimized thresholds. Extensive simulations compare ATTLB with weighted round-robin (WRR), ant colony optimization (ACO), and least connection-based load balancing (LCLB) algorithms using the CloudSim toolkit. The results demonstrate the ability of ATTLB to reduce total costs by over 35% and improve SLA violations by 41% compared with prior techniques for cloud load balancing. Adaptive threshold tuning provides robustness against dynamic pricing and demand changes. ATTLB balances cost, performance, and utilization through realtime threshold adaptation.

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Section: Load Balancingmentioning

confidence: 99%

Adaptive Threshold Tuning-based Load Balancing (ATTLB) for Cost Minimization in Cloud Computing

Khoshaim

2024

IJACSA

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“…The most popular static load balancing methods are Round Robin, Weighted Round-Robin, Min-Min, Max-Min, and Opportunistic load balancing methods. The dynamic algorithms include metaheuristic methods [15]. Figure 3 represents the primary taxonomy of cloud load balancing methods.…”

Section: Taxonomy Of Load Balancing Algorithmsmentioning

confidence: 99%

Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing

Zhou,

Lilhore,

et al. 2023

J Cloud Comp

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Load balancing is a serious problem in cloud computing that makes it challenging to ensure the proper functioning of services contiguous to the Quality of Service, performance assessment, and compliance to the service contract as demanded from cloud service providers (CSP) to organizations. The primary objective of load balancing is to map workloads to use computing resources that significantly improve performance. Load balancing in cloud computing falls under the class of concerns defined as "NP-hard" issues due to vast solution space. Therefore it requires more time to predict the best possible solution. Few techniques can perhaps generate an ideal solution under a polynomial period to fix these issues. In previous research, Metaheuristic based strategies have been confirmed to accomplish accurate solutions under a decent period for those kinds of issues. This paper provides a comparative analysis of various metaheuristic load balancing algorithms for cloud computing based on performance factors i.e., Makespan time, degree of imbalance, response time, data center processing time, flow time, and resource utilization. The simulation results show the performance of various Meta-heuristic Load balancing methods, based on performance factors. The Particle swarm optimization method performs better in improving makespan, flow time, throughput time, response time, and degree of imbalance.

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“…Different load balancing procedures occur to reach better resource exploitation. In [ 6 ], authors developed a discussions of load balancing procedures. In addition, the authors in the latter paper, give a comparison between the proposed algorithms on the basis of different indicators like mean no-waiting time, processing time, and data cost time.…”

Section: Introductionmentioning

confidence: 99%

Algorithms for the executable programs planning on supercomputers

Algashami

2022

PLoS ONE

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This research dealt with the problem of scheduling applied to the supercomputer’s execution. The goal is to develop an appreciated algorithm that schedules a group of several programs characterized by their time consuming very high on different supercomputers searching for an efficient assignment of the total running time. This efficient assignment grantees the fair load distribution of the execution on the supercomputers. The essential goal of this research is to propose several algorithms that can ensure the load balancing of the execution of all programs. In this research, all supercomputers are assumed to have the same hardware characteristics. The main objective is to minimize the gap between the total running time of the supercomputers. This minimization of the gap encompasses the development of novel solutions giving planning of the executable programs. Different algorithms are presented to minimize the gap in running time. The experimental study proves that the developed algorithms are efficient in terms of performance evaluation and running time. A comparison between the presented algorithms is discussed through different classes of instances where in total the number of instances reached 630. The experiments show that the efficient algorithm is the best-programs choice algorithm. Indeed, this algorithm reached the percentage of 72.86%, an average running time of 0.0121, and a gap value of 0.0545.

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Analysis of Different Load Balancing Algorithms in Cloud Computing

Cited by 8 publications

References 15 publications

Adaptive Threshold Tuning-based Load Balancing (ATTLB) for Cost Minimization in Cloud Computing

Adaptive Threshold Tuning-based Load Balancing (ATTLB) for Cost Minimization in Cloud Computing

Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing

Algorithms for the executable programs planning on supercomputers

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