EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

Ibrahim, Muhammad; Imran, Muhammad; Jamil, Faisal; Lee, Yun‐Jung; Kim, Do‐Hyeun

doi:10.3390/sym13040690

Cited by 28 publications

(16 citation statements)

References 33 publications

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“…But it has SLA degradation conflicts which vigorously affect the resource allocation to the host. Kawsar et al [5] presented a paper on the power consumption of the data center and utilized the regression-based algorithm to predict the appropriate host for VM migration with high CPU utilization.…”

Section: Energy and Sla Degradation (Esv)mentioning

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: Energy and Sla Degradation (Esv)mentioning

confidence: 99%

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

Devi¹,

Kumar²

2022

INDJCSE

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show abstract

“…Cloud computing environment provides ubiquitous access to resources and services to its customers on a pay as you use model. Different types of cloud computing models exists, which are IaaS, PaaS, and SaaS [36]. In recent years, several task scheduling approaches have been proposed for Cloud computing.…”

Section: Related Workmentioning

confidence: 99%

Alts: An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing

et al. 2021

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According to the research, many task scheduling approaches have been proposed like GA, ACO, etc., which have improved the performance of the cloud data centers concerning various scheduling parameters. The task scheduling problem is NP-hard, as the key reason is the number of solutions/combinations grows exponentially with the problem size, e.g., the number of tasks and the number of computing resources. Thus, it is always challenging to have complete optimal scheduling of the user tasks. In this research, we proposed an adaptive load-balanced task scheduling (ALTS) approach for cloud computing. The proposed task scheduling algorithm maps all incoming tasks to the available VMs in a load-balanced way to reduce the makespan, maximize resource utilization, and adaptively minimize the SLA violation. The performance of the proposed task scheduling algorithm is evaluated and compared with the state-of-the-art task scheduling ACO, GA, and GAACO approaches concerning average resource utilization (ARUR), Makespan, and SLA violation. The proposed approach has revealed significant improvements concerning the makespan, SLA violation, and resource utilization against the compared approaches.

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“…The edge node has a limited number of resources in edge computing (EC). Thus, relocation of computing resources to the cloud is necessary for performing huge and complex tasks [62,63]. Data trimming is considered a major issue in the Cloud of Things (CoT).…”

Section: Internet Of Thingsmentioning

confidence: 99%

Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions

Imran

Zaman²,

Imran

et al. 2021

Electronics

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Internet of Things (IoT) communication technologies have brought immense revolutions in various domains, especially in health monitoring systems. Machine learning techniques coupled with advanced artificial intelligence techniques detect patterns associated with diseases and health conditions. Presently, the scientific community is focused on enhancing IoT-enabled applications by integrating blockchain technology with machine learning models to benefit medical report management, drug traceability, tracking infectious diseases, etc. To date, contemporary state-of-the-art techniques have presented various efforts on the adaptability of blockchain and machine learning in IoT applications; however, there exist various essential aspects that must also be incorporated to achieve more robust performance. This study presents a comprehensive survey of emerging IoT technologies, machine learning, and blockchain for healthcare applications. The reviewed articles comprise a plethora of research articles published in the web of science. The analysis is focused on research articles related to keywords such as `machine learning’, blockchain, `Internet of Things or IoT’, and keywords conjoined with `healthcare’ and `health application’ in six famous publisher databases, namely IEEEXplore, Nature, ScienceDirect, MDPI, SpringerLink, and Google Scholar. We selected and reviewed 263 articles in total. The topical survey of the contemporary IoT-based models is presented in healthcare domains in three steps. Firstly, a detailed analysis of healthcare applications of IoT, blockchain, and machine learning demonstrates the importance of the discussed fields. Secondly, the adaptation mechanism of machine learning and blockchain in IoT for healthcare applications are discussed to delineate the scope of the mentioned techniques in IoT domains. Finally, the challenges and issues of healthcare applications based on machine learning, blockchain, and IoT are discussed. The presented future directions in this domain can significantly help the scholarly community determine research gaps to address.

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EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

Cited by 28 publications

References 33 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

Alts: An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing

Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions

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