Comparative Study for Different Provisioning Policies for Load Balancing in Cloudsim

Albdour, Layla

doi:10.4018/978-1-7998-5339-8.ch028

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…The proposed O‐LEO algorithm is implemented in CloudSim and it is mainly used to identify the optimal number of VMs used in the process. By default, in the CloudSim the first task is deployed in the first VM, the second task is deployed in the second VM, and so on 38,39 . The overall time taken to develop three cloudlets is presented in Table 2.…”

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

An efficient cloud‐based healthcare services paradigm for chronic kidney disease prediction application using boosted support vector machine

Aswini¹,

Yamini

Jatothu³

et al. 2021

Concurrency and Computation

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Cloud computing is the on-demand access to computer resources such as applications, servers, data storage, development tools, networking capabilities, and other resources that are held in a remote data center maintained by a cloud services provider(CSP) and accessible over the internet. The limited resources and higher time consumption are the major issues faced by cloud users. The appropriate selection of virtual machines (VMs) is used to maximize cloud resource usage and reduce task execution time for the clients (patients, physicians, etc.). In this article, we have introduced an efficient cloud-based healthcare services paradigm (HCS). The opposition-based Laplacian equilibrium optimizer (O-LEO) algorithm is used to select optimal VMs in which the maximization of resource utilization and minimization of task execution time is performed. Additionally, the boosted support vector machine (SVM) effectively predicts chronic kidney disease (CKD) thereby ensuring better prediction results. The CloudSim platform is used as the implementation platform of the proposed method. The overall time taken by the O-LEO-based CloudSim is less than the standard Cloud Sim model to create three cloudlets which improve the system efficiency by 6%. When compared with the existing techniques, both the O-LEO and boosted SVM classifier outperforms superior performances.

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Section: Experimental Analysis and Resultsmentioning

confidence: 99%

An efficient cloud‐based healthcare services paradigm for chronic kidney disease prediction application using boosted support vector machine

Aswini¹,

Yamini

Jatothu³

et al. 2021

Concurrency and Computation

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show abstract

“…The primary aims of load balancing are to achieve high availability, improved performance, efficient resource utilization, maximum throughput, and the ability to meet service level agreements (SLAs) for quality of service [53]. Load balancing helps evenly distribute the www.ijacsa.thesai.org workload across servers and prevents uneven loading or hotspots on particular machines, which could lead to performance impacts or failures [54]. It provides horizontal scalability to handle increasing demands by elastically adding virtualized computing resources.…”

Section: Load Balancingmentioning

confidence: 99%

“…Load balancing is a key enabler for efficiency, scalability, availability, and quality of service (QoS) in cloud computing environments [5] [6] [7]. Load balancing distributes incoming user workloads transparently and optimally across multiple VMs hosted in geographically distributed data centers [8] [9].…”

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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Comparative Study for Different Provisioning Policies for Load Balancing in Cloudsim

Cited by 2 publications

References 19 publications

An efficient cloud‐based healthcare services paradigm for chronic kidney disease prediction application using boosted support vector machine

An efficient cloud‐based healthcare services paradigm for chronic kidney disease prediction application using boosted support vector machine

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

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