Survey on Existing Load Balancing Algorithms in Cloud Environment

Sansanwal, Suman; Jain, Naresh C.

doi:10.2139/ssrn.3884722

Cited by 1 publication

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“…However, traditional load balancing techniques often rely on preset static thresholds for parameters such as CPU utilization, memory usage, network bandwidth, I/O rates, and number of connections [17] [18]. These thresholds remain unchanged and do not adapt dynamically to real-time changes in workload patterns, resource pricing, system performance, or user SLAs [19] [20].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

Khoshaim

2024

IJACSA

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Survey on Existing Load Balancing Algorithms in Cloud Environment

Cited by 1 publication

References 0 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

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