Load balancing prediction method of cloud storage based on analytic hierarchy process and hybrid hierarchical genetic algorithm

Zhou, Xiuze; Lin, Fan; Yang, Lvqing; Nie, Jing; Tan, Qian; Zeng, Wenhua; Zhang, Nian

doi:10.1186/s40064-016-3619-x

Cited by 11 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, a comparison of performance between the (WRR) and MEMA is presented. Xiuze Zhou et al [12] proposed a method to evaluate the load status of server nodes using AHPGD (Analytic Hierarchy Process Group Decision). We trained using HHGA (Hybrid Hierarchical Genetic Algorithm) to optimize RBFNN (Radial Basis Function Neural Network).…”

Section: Background and Motivationmentioning

confidence: 99%

“…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]. [12].In terms of analysis, due to excess resources, both the short-term and long-term use of IT infrastructure in cloud services face high operating costs [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Clustering Based Prediction For VM Workload In Green Computation

Saxena,

Khan,

Singh

et al. 2024

Kuey

View full text Add to dashboard Cite

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.

show abstract

Section: Background and Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clustering Based Prediction For VM Workload In Green Computation

Saxena,

Khan,

Singh

et al. 2024

Kuey

View full text Add to dashboard Cite

show abstract

“…Moreover, a comparison of performance between the (WRR) and MEMA is presented. [12] proposed a method to evaluate the load status of server nodes using AHPGD (Analytic Hierarchy Process Group Decision). We trained using HHGA (Hybrid Hierarchical Genetic Algorithm) to optimize RBFNN (Radial Basis Function Neural Network).…”

Section: Hamidreza Moradi Et Almentioning

confidence: 99%

“…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 energyconscious 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]. [12].In terms of analysis, due to excess resources, both the short-term and long-term use of IT infrastructure in cloud services face high operating costs [13].…”

Section: Introductionmentioning

confidence: 99%

Clustering Based Prediction for VM Workload in Green Computation

Saxena

2024

AJCT

View full text Add to dashboard Cite

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.

show abstract

Accurate workload prediction for edge data centers: Savitzky-Golay filter, CNN and BiLSTM with attention mechanism

Chen

Zhang

2022

Appl Intell

View full text Add to dashboard Cite

Load balancing prediction method of cloud storage based on analytic hierarchy process and hybrid hierarchical genetic algorithm

Cited by 11 publications

References 34 publications

Clustering Based Prediction For VM Workload In Green Computation

Clustering Based Prediction For VM Workload In Green Computation

Clustering Based Prediction for VM Workload in Green Computation

Accurate workload prediction for edge data centers: Savitzky-Golay filter, CNN and BiLSTM with attention mechanism

Contact Info

Product

Resources

About