An approach towards development of new linear regression prediction model for reduced energy consumption and SLA violation in the domain of green cloud computing

Biswas, Nirmal Kr.; Banerjee, Sourav; Biswas, Utpal; Ghosh, Uttam

doi:10.1016/j.seta.2021.101087

Cited by 27 publications

(16 citation statements)

References 22 publications

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“…There are number of data gadgets which can shape start to complete the joins with the help of server through suitable similar forms some of the examples are, Bluetooth, ZigBee, Wi-Fi, and ultra-wideband, in this manner enhancing applications in the domains of smart technology and in-home care, industrial monitoring, among others. By consolidating the IoT with cloud computation domain, and at a greater extent of the executives can likewise be elaborated functionally [19]. With respect to cloud computational environment or IoT, a distributed system has three immense consequences, like independent failures, no global clock, and concurrency.…”

Section: Methodsmentioning

confidence: 99%

Research on secure workload execution scheme in heterogeneous cloud environment

Pasha

Jayapandian²

2023

IJEECS

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<span lang="EN-US">The increasing demand for the hardware, software and infrastructure is playing a big role in the information technology domain towards the need of customer’s specific requirements. Cloud computing is a major backbone for providing such services over the internet. It includes the services such as applications, storage, network, scalability, sharing, virtualization, confidentiality, security, authentication, and integrity. A large number of data intensive workflow applications uses heterogeneous cloud environment for communication and computation operation. An intruder/attacker will utilize these environments for their benefit by flooding malicious links, unwanted information and others. In cloud environment, detecting a malicious device/packet during workflow execution is a critical and challenging task. The various workflow method with security, service level agreement (SLA) and quality of service (QoS) have been modelled in recent time; However, these models are not efficient in detecting malicious users and maintaining high level of QoS or workflow applications. This article focus is on addressing research future direction, issues and challenges of work in meeting secure and efficient workflow execution model for heterogeneous cloud environment.</span>

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Section: Methodsmentioning

confidence: 99%

Research on secure workload execution scheme in heterogeneous cloud environment

Pasha

Jayapandian²

2023

IJEECS

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“…The technique for evaluating the linear regression model will be carried out in stages [18]. The first stage is data collection, the dataset was collated and merged with different dataset within the region.…”

Section: Methodsmentioning

confidence: 99%

Performance Evaluation of Regression Analysis Algorithms in Predicting the Enrollment of Basic Education

Piol¹,

Lacatan²,

Pulumbarit³

2022

IJETAE

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The use of Linear Regression in predicting enrolment has been shown to be beneficial, although it varies with various datasets and attributes; varying weights of the correlation of the attributes can be discarded if they do not impact the prediction. Data collecting had grown since prior investigations, resulting in a more complicated dataset with many varieties. As a result of the data being created by multiple clerks, cleaning and combining proved tough; nonetheless, the fundamental parameters remain intact. Different algorithms were examined but Linear Regression obtained the highest accuracy with a 12.398 percentage for the absolute error and a root mean squared of 26.936 to create a tangible model to anticipate the enrolment of Region IVA CALABARZON in the Philippines. This demonstrates that it was 2.067 percentage points more than the prior research.

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“…Further, the presented method uses virtualization process and migrates some of the VMs from over-loaded hosts and medium-and under-loaded hosts. In literature [15], the authors proposed a New Linear Regression (NLR) prediction system i.e., VM placement policy and host overload/underload in order to minimize SLAV and EC. The presented algorithm is capable of predicting CPU consumption.…”

Section: Related Workmentioning

confidence: 99%

Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

Karthikeyan¹,

Syed²,

Aljohani³

et al. 2023

Computer Systems Science and Engineering

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Cloud data centers consume high volume of energy for processing and switching the servers among different modes. Virtual Machine (VM) migration enhances the performance of cloud servers in terms of energy efficiency, internal failures and availability. On the other end, energy utilization can be minimized by decreasing the number of active, underutilized sources which conversely reduces the dependability of the system. In VM migration process, the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the operations. In this view, the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization (IMFP-VMMO) model in cloud environment. The major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure prediction. To accomplish this, IMFP-VMMO model employs Gradient Boosting Decision Tree (GBDT) classification model at initial stage for effectual prediction of VM failures. At the same time, VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm (QO-AFSA) which in turn reduces the energy consumption. The performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed model. The comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.

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An approach towards development of new linear regression prediction model for reduced energy consumption and SLA violation in the domain of green cloud computing

Cited by 27 publications

References 22 publications

Research on secure workload execution scheme in heterogeneous cloud environment

Research on secure workload execution scheme in heterogeneous cloud environment

Performance Evaluation of Regression Analysis Algorithms in Predicting the Enrollment of Basic Education

Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

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