Cloud Computing Scalability

Sehgal, Neeta; Bhatt, Pramod Chandra P.; Acken, John M.

doi:10.1007/978-3-031-07242-0_13

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…Virtualization refers to the abstraction and sharing of underlying physical hardware resources such as computing, memory, storage, and network bandwidth using virtualization layer software [41] [42]. This virtualization layer Creates isolated virtual environments known as virtual machines (VMs) which behave like real computers with dedicated processor, memory, storage, operating system, drivers, applications [43] [44]. However, multiple VMs can operate concurrently with the same physical server hardware.…”

Section: B Virtualizationmentioning

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: B Virtualizationmentioning

confidence: 99%

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

Khoshaim

2024

IJACSA

View full text Add to dashboard Cite

show abstract

Cloud Computing Scalability

Cited by 1 publication

References 8 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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