LABAREDA: A Predictive and Elastic Load Balancing Service for Cloud-Replicated Databases

Marinho, Carlos S. S.; Moreira, Leonardo O.; Coutinho, Emanuel Ferreira; Filho, José Silveira; Sousa, Flávio R. C.; Machado, Javam C.

doi:10.5753/jidm.2018.1639

Cited by 3 publications

(6 citation statements)

References 11 publications

(19 reference statements)

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“…In order to divide these requests into multiple categories, we represent each request as a point in the N dimensional (RN) space. As the data set of user(s) request on Googlecluster [8], requirenment of two types of resources, CPU and RAM, are associated with each request. Thus two dimensional (R2) space is used to represent these requests shown in figure 1, where each point is a request and the required amount of CPU and RAM associated with the request are shown through the coordinates of R2space.Clustering of theserequest points into k=2 clusters is done using unsupervised learning like-k-Means, K-Medoid, FCM and SOM algorithms.…”

Section: Request Decomposermentioning

confidence: 99%

“…So it is a key challenge in cloud computing, how effective use of resources and the use of useful resources is define. To address these challenges various energy-saving technologies based on virtualization is prescribed [7]. Cloud computing require thousands of watts of energy for providing the efficient services to customers and to executethe demands of user(s) in numerous categories, consume large amounts of energy.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Request Decomposermentioning

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

“…In this way, load balancing methods improves the utilizationof resources; however, certain decisions use the current state of the database service to make decisions. Carlos S. S. Marinho et al [7] provide predictive load balancing of services for databases replicated in the cloud. [8] presented a load balancing solution for replicated cloud databases that is both predictive and elastic.…”

Section: Hajer Toumi Et Almentioning

confidence: 99%

“…Carlos S. S. Marinho et al [7] provide predictive load balancing of services for databases replicated in the cloud. [8] presented a load balancing solution for replicated cloud databases that is both predictive and elastic. Experiments have shown that using prediction models to predict possible SLA violations in time series that indicate workloads of cloud-replicated databases might be beneficial.…”

Section: Hajer Toumi 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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Multi-Tenant RDBMS Migration in the Cloud Environment

Raouf

Abo-Alian

Badr

2021

IJICIS

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In a multi-tenant business environment, tenants share the same applications and databases to store their data. Due to the widespread use of a multi-tenant environment, the service providers face difficult challenges daily. These challenges are condensed in how to guarantee the quality of service provided to tenants, which are documented in a formal document known as a Service Level Agreement (SLA). In addition, SLA should consider the irregular patterns of workload of tenants which may affect the level of guarantee. In this research, an Enhanced Multi-Tenant Database Management System (EMT DBMS) is proposed. In addition, an Enhanced Multi-tenant Migration Algorithm called EMT-M is presented, which aims to migrate the violated tenants depending on both the number of SLA violations and the variance rate. Experimental results prove that the proposed EMT-M algorithm is ideal for International Journal of Intelligent Computing and Information Sciences https://ijicis.journals.ekb.eg/ 97 A.E. Abdel Raouf et al. migrating violated tenants, as it reduces the number of SLA violations compared to previous migration algorithms.

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LABAREDA: A Predictive and Elastic Load Balancing Service for Cloud-Replicated Databases

Cited by 3 publications

References 11 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

Multi-Tenant RDBMS Migration in the Cloud Environment

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