AMO Based Load Balancing Approach in Cloud Computing

Mittal, Sudhanshu; Dubey, Madhukar

doi:10.9790/0661-1902046266

Cited by 3 publications

(5 citation statements)

References 6 publications

(6 reference statements)

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“…The task length, resource size and priority increase the execution time and response time of the task. Sudhanshu Mittal et al [10] applied AMO technology to improve load balance. Initially the data center works according to population and then AMO works.…”

Section: Hamidreza Moradi Et Almentioning

confidence: 99%

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Clustering Based Prediction for VM Workload in Green Computation

Saxena

2024

AJCT

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In order to support the different new generation equipment and technologies, cloud computing is depends to deal with the bulky data. Because a rich amount of data is generated using these devices and processing such big data need the cloud servers which can scale the computational ability according to demand. On the other hands to perform computation we need huge power supply and cooling system that increases the power consumption and emission of harmful gases. Thus, need to achieve green computing by reducing power consumption of computational cloud. In this context, we found VM(virtual machine) workload scheduling can be a good strategy to efficiently utilize the computational resources and reducing power consumption of cloud server.Basically, the physical machines contain a number of virtual machines (VMs). These VMs are used to deal with the workload appeared for processing. If we better utilize the resources then we can process large number of jobs in less amount of VMs. Additionally, we can also turn off the ideal machines to reduce the power consumption. In this context the proposed work is motivated to work with VM scheduling techniques to achieve green computing. In recent literature we also identified that there are two kinds of VM scheduling approaches active and proactive. The proactive technique is more effective as compared to active approaches, due to prior knowledge of the workload on VM. So, in this paper we proposed green cloud predictive model for VMs workload using unsupervised learning (i.e clustering) like K-Mean, K-Medoid, Fuzzy C-Mean (FCM),Self-Organizing Map(SOM) to predict the future workload for VM's scheduling and find the efficient clustering among them for workload prediction in view of green computing. The efficiency of clustering-based prediction is measured on parameters like accuracy, error rate.

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Section: Hamidreza Moradi Et Almentioning

confidence: 99%

“…Last year, cloud sources consumed around 1.1% to 1.5% of the world's energy. By the end of 2021, cloud data centers are estimated to consume more than 140 TH [10].…”

Section: Introductionmentioning

confidence: 99%

Clustering Based Prediction for VM Workload in Green Computation

Saxena

2024

AJCT

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“…Load balancing is an important facet of cloud computing which is necessary for uniform load distribution among resources like servers, network interfaces, hard drives (storage) and virtual machines (VMs) hosted on physical servers (Mittal & Dubey, 2017). When tasks are scheduled on VMs hosting on physical nodes, there might arise a situation that some of the VMs are overutilized, while others remain underutilized.…”

Section: Present Workmentioning

confidence: 99%

“…The objective is to distribute the load among machines evenly to improve the overall performance of task execution over cloud resources (processing power, memory, network and storage). Load Balancing techniques are responsible for allocating resources with minimum resource wastage when machines are utilized below their capacity and avoiding overloading/ underloading of Virtual Machines (VMs) (Mittal & Dubey, 2017;Ghomi et al, 2017). When a large number of requests are targeted to a single Copyright © 2020 MECS I.J.…”

Section: Introductionmentioning

confidence: 99%

Load Balancing Optimization Based on Deep Learning Approach in Cloud Environment

Kaur¹,

Kaur²,

Singh³

et al. 2020

IJITCS

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Load balancing is a significant aspect of cloud computing which is essential for identical load sharing among resources like servers, network interfaces, hard drives (storage) and virtual machines (VMs) hosted on physical servers. In cloud computing, Deep Learning (DL) techniques can be used to achieve QoS such as improve resource utilization and throughput; while reduce latency, response time and cost, balancing load across machines, thus, increasing the system reliability. DL results in effective and accurate decision making of intelligent resource allocation to the incoming requests, thereby, choosing the most suitable resource to complete them. However, in previous researches on load balancing, there is limited application of DL approaches. In this paper, the significance of DL approaches have been analysed in the area of cloud computing. A Framework for Workflow execution in cloud environment has been proposed and implemented, namely, Deep Learningbased Deadline-constrained, Dynamic VM Provisioning and Load Balancing (DLD-PLB). Optimal schedule for VMs has been generated using Deep Learning based technique. The Genome workflow tasks have been taken as input to the suggested framework. The results for makespan and cost has been computed for the proposed framework and has been compared with our earlier proposed framework for load balancing optimization -Hybrid approach based Deadline-constrained, Dynamic VM Provisioning and Load Balancing (HDD-PLB)" framework for Workflow execution. The earlier proposed approaches for load balancing were based on hybrid Predict-Earliest-Finish Time (PEFT) with ACO for underutilized VM optimization and hybrid PEFT-Bat approach for optimize the utilization of overflow VMs.

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“…Last year, cloud sources consumed around 1.1% to 1.5% of the world's energy. By the end of 2021, cloud data centers are estimated to consume more than 140 TH [10]. Green cloud computing is intended to reduce energy consumption by enabling for more efficient operation and utilization of computing resources.The foremost vital objective of novice cloud computing is that the management of energy-conscious data, virtualization algorithms, economic policies, resource management, employment and activities within the field, moreover as several land and eco-friendly standard technologies for upcoming years [11].…”

Section: Introductionmentioning

confidence: 99%

Clustering Based Prediction For VM Workload In Green Computation

Saxena,

Khan,

Singh

et al. 2024

Kuey

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In order to support the different new generation equipment and technologies, cloud computing is depends to deal with the bulky data. Because a rich amount of data is generated using these devices and processing such big data need the cloud servers which can scale the computational ability according to demand. On the other hands to perform computation we need huge power supply and cooling system that increases the power consumption and emission of harmful gases. Thus, need to achieve green computing by reducing power consumption of computational cloud. In this context, we found VM(virtual machine) workload scheduling can be a good strategy to efficiently utilize the computational resources and reducing power consumption of cloud server. Basically, the physical machines contain a number of virtual machines (VMs). These VMs are used to deal with the workload appeared for processing. If we better utilize the resources then we can process large number of jobs in less amount of VMs. Additionally, we can also turn off the ideal machines to reduce the power consumption. In this context the proposed work is motivated to work with VM scheduling techniques to achieve green computing. In recent literature we also identified that there are two kinds of VM scheduling approaches active and proactive. The proactive technique is more effective as compared to active approaches, due to prior knowledge of the workload on VM. So, in this paper we proposed green cloud predictive model for VMs workload using unsupervised learning (i.e clustering) like K-Mean, K-Medoid, Fuzzy C-Mean (FCM),Self-Organizing Map(SOM) to predict the future workload for VM’s scheduling and find the efficient clustering among them for workload prediction in view of green computing. The efficiency of clustering-based prediction is measured on parameters like accuracy, error rate.

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AMO Based Load Balancing Approach in Cloud Computing

Cited by 3 publications

References 6 publications

Clustering Based Prediction for VM Workload in Green Computation

Clustering Based Prediction for VM Workload in Green Computation

Load Balancing Optimization Based on Deep Learning Approach in Cloud Environment

Clustering Based Prediction For VM Workload In Green Computation

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