Linear and Logistic Regression Based Monitoring for Resource Management in Cloud Networks

Daraghmeh, Mustafa; Melhem, Suhib Bani; Agarwal, Anjali; Goel, Nishith; Zaman, Musharraf

doi:10.1109/ficloud.2018.00045

Cited by 16 publications

(4 citation statements)

References 7 publications

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“…Some default parameters (𝛼, 𝛽 , 𝛽 , 𝑒𝑝𝑠𝑖𝑙𝑜𝑛, 𝑀𝑎𝑥𝐼𝑡𝑒𝑟𝑎𝑡𝑖𝑜𝑛) are assigned with a default value and then coefficient parameters (𝜃, 𝑋, 𝑌) are initialized which is easily configurable. The Host threshold is assigned with the value of 0.85 [18]. 𝜃 , 𝜃 gradient values are calculated.…”

Section: Host Overloaded Detectionmentioning

confidence: 99%

Novel Load Balancing Approach Towards Cloud Computing by Reducing Sla Degradation and Energy Consumption

Devi¹,

Kumar²

2022

INDJCSE

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The advent development of cloud computing is an on-demand adaptive technology for many IT organizations due to its dynamic scalability and virtualization resources as a global service. There are numerous energy-conscious approaches in existence that attempt to minimize energy consumption and Service Level Agreements (SLAs) degradation of the host. In this paper, we proposed a novel load balancing approach that identifies overloaded or underloaded hosts, and selects the VMs (Virtual Machines) for allocation to the host based on the predicted load. We consider CPU utilization parameter for evaluating host loading which is far enough to compute all the characteristics of Physical Machines (PM) provided to the Linear Adam Algorithm for Overloaded detection followed by Interquartile Range (IQR) method for underloaded detection. The Power-Aware Best Fit Decreasing (PABFD) algorithm sorts the VM in descending order based on CPU utilization. Then those filtered VM is allocated to the corresponding host thereby reducing the energy consumption and SLAs degradation. We evaluate the proposed algorithm through CloudSim-3.0.3 simulation and random datasets with different workloads on a real Planet Lab. After the evaluation, the simulation result of the proposed method indicates a significant performance by reducing the metric parameters such as SLA degradation, number of VM migration, the total number of host shutdowns, and reduce energy consumption.

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Section: Host Overloaded Detectionmentioning

confidence: 99%

Novel Load Balancing Approach Towards Cloud Computing by Reducing Sla Degradation and Energy Consumption

Devi¹,

Kumar²

2022

INDJCSE

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“…Among several indicators to evaluate the algorithm's performance, the following indicators are used to measure the performance of our proposed model [10,30].…”

Section: Performance Evaluation Indicatormentioning

confidence: 99%

Min-max exclusive virtual machine placement in cloud computing for scientific data environment

et al. 2021

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In cloud computing, there is a trade-off between SLAV (Service Level Agreement Violation) and system operating cost. Violation rates can be decreased when using more hosts, which increases system operating costs. Therefore, to manage the resources of those hosts efficiently, finding an optimal balancing point between SLAV and system operating cost is critical. In addition, a cost-effective load balancing approach based on the proper migration of VMs (Virtual Machines) in the hosts is needed. For this purpose, some indicators are also necessary to identify the abnormal hosts that violate SLA. One of the primary indicators, CPU usage, is closely related to energy consumption and can be used to reduce SLAV and energy consumption effectively. Our approach focuses on the special environment such as the cloud environment for the scientific data. Here, most of the jobs are data-intensive and a large amount of disk operations is required. Owing to disk operations are likely to affect the performance degradation of the host, disk bandwidth usage as well as CPU usage should be also considered. In this study, we propose the Min-Max Exclusive VM Placement (MMEVMP) strategy to minimize both SLAV and energy consumption. The current working system called KIAF (KISTI Analysis Facility), the CERN ALICE experimental cloud environment for scientific data analysis, is used to analyze the characteristics of data-intensive jobs within it. In this experiment, a lightweight version of the CloudSim simulator was developed and the results were compared to the other methods of different policies. Our evaluation showed that our proposed strategy can reduce SLA violation reasonably as well as the system operating cost-effectively.

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“…Effectively the proposed HOPBLR algorithm outperforms overall performance metrics comparing to other benchmark algorithms, again, in order to reduce SLA violation and at the same time energy consumption, while adapting the changes of different types of workloads running on the cloud provider DC. This proposed algorithm [67] uses adapted linear predicted value of host utilization multiplied by a safety parameter as an input to the logistic regression model, to predict the probability of host being overloaded. The proposed model performs better in power consumption reduction, SLA violation as well as the number of VM migrations comparing with the RS, MU, MMT, and MC.…”

Section: Regression Techniquesmentioning

confidence: 99%

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

et al. 2020

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Given the perpetual surging of cloud services' requests, energy consumption of cloud data centers with their related CO 2 emissions still represents major issues. Efficient use of cloud's resources becomes then the driven force ensuring both, the satisfaction of service-level agreements and the sobriety of cloud's energy consumption. This paper purports to survey for the first time, a comprehensive literature of some actual challenges facing various cloud's resources utilization scheduling approaches dealing with energy conservation. Indeed, cloud resources involve not only computing servers, but also a wide set of intra-and inter-cloud network's resources to be considered. These resources are mainly provisioned through two wellknown technologies: virtualization and/or containerization. As existing studies in the area of cloud resources provisioning are generally categorized into different groups, this research depicts a complete taxonomy of energy-efficient cloud resources scheduling. In this same perspective, we survey some recent efforts made in energy-efficient virtual and containerized resources, by emphasizing first the most used reactive then proactive techniques for managing the whole cloud resources energy efficiency scheduling.

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Linear and Logistic Regression Based Monitoring for Resource Management in Cloud Networks

Cited by 16 publications

References 7 publications

Novel Load Balancing Approach Towards Cloud Computing by Reducing Sla Degradation and Energy Consumption

Novel Load Balancing Approach Towards Cloud Computing by Reducing Sla Degradation and Energy Consumption

Min-max exclusive virtual machine placement in cloud computing for scientific data environment

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

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