Autonomic resource provisioning for multilayer cloud applications with K‐nearest neighbor resource scaling and priority‐based resource allocation

Mazidi, Arash; Golsorkhtabaramiri, Mehdi; Tabari, Meisam Yadollahzadeh

doi:10.1002/spe.2837

Cited by 12 publications

(6 citation statements)

References 46 publications

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“…Take the L i − 1 and R i − 1 of the previous round as the input of the next round, and the iteration rule of L i and R i of the output bits is 16). Here, f is a permutation function [17], which includes E change, S and P transformation, e ⊕ symbol indicates XOR. Step 3: Finally, the ciphertext y is obtained by reversing the IP −1 table.…”

Section: Methodsmentioning

confidence: 99%

“…Dakhil et al task digital twins to update building information models in near real-time using IoT sensors, while blockchain authenticates and increases confidence in all data transactions of digital twins [16]. Mazidi et al found that the scalability nature of cloud computing attracted application service providers (ASPs) to use cloud application hosting [17].…”

Section: Literature Reviewmentioning

confidence: 99%

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Analysis of Co-Construction and Sharing Mechanism of Digital Education Resources based on Digital Information Encryption Technology

Zhang

2022

Security and Communication Networks

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In order to solve the problem of information security, the process of digital resource development and integration has become the key. A method based on focusing on the development and integration of digital instructions is proposed, which discusses digital data encryption in detail, focusing on the DES algorithm in the symmetric encryption algorithm and the RSA algorithm in the asymmetric encryption algorithm and conducts a security evaluation. Through comparative analysis, the comprehensive communication system based on the combination of DES encryption algorithm and RSA encryption algorithm aims to realize the integration and sharing of digital display. Through experimental analysis and comparison with traditional commonly used encryption methods, 64-bit keys are very easy to generate, but the keys must be distributed before communication, and the keys must be changed from time to time. In this way, key management consumes a lot of system overhead. From the final experimental data, it can be concluded that the encryption algorithm has performance advantages over the separate DES and RSA algorithms.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Co-Construction and Sharing Mechanism of Digital Education Resources based on Digital Information Encryption Technology

Zhang

2022

Security and Communication Networks

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“…For multilayer applications of cloud computing, an autonomic resource provisioning method is described in Reference 35. In this method, k-nearest neighbor (KNN) approach is used for analyzing and labeling VMs, and statistical approach is used for making scaling decisions.…”

Section: Autonomic Resource Managementmentioning

confidence: 99%

Autonomic cloud computing based management and security solutions: State‐of‐the‐art, challenges, and opportunities

Agrawal

2021

Trans Emerging Tel Tech

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The advancements and rapid adoption of service oriented architecture, utility computing, virtualization, etc., have emerged in a new computing paradigm called cloud computing, in the last few years. Cloud computing enables on-demand delivery of data, software, and hardware in the form of services over the Internet. But the growing scale of cloud-users and type-of-services urge for an autonomous environment to automate the highly complex cloud environment in a suitable way to improve the overall management of the cloud systems. To counter these automation related issues, autonomic computing (AC) emerges as a potential solution to optimize the performance of cloud computing.The merge of these two prominent technologies gives rise to a new technological domain called autonomic cloud computing (ACC). In this survey article, the existing AC assisted solutions for cloud computing is broadly categorized into two groups, namely features based and parameters based. Features based solutions are further classified into management (of services, workload, and resources) and security aware solutions. Similarly, parameters based solutions are grouped into quality-of-service aware and performance management solutions. Finally, the comparative analysis of state-of-the-art ACC approaches with respect to the approach referred, performance metrics, outcome, and the derived inference is also presented to have an in-depth understanding of the existing literature. Overall, the work excites the research community to design the effective autonomous solutions for ACC resources and services. Additionally, several research gaps and challenges are identified and discussed in this article based on the study with a focus on the future research directions.

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“…With the development of the theory of machine learning, machine learning methods become more and more attractive in the field of cloud computing. Mazidi et al [9] propose a new method based on the K-nearest neighbor algorithm that is used to analyze and label virtual machines, and statistical methods are used to make expansion decisions. Wei et al [10] propose a cloud application auto scaling approach based on Q-learning method to help SaaS providers make optimal resource allocation decisions in a dynamic and stochastic cloud environment.…”

Section: Related Workmentioning

confidence: 99%

A Deep Reinforcement Learning Based Resource Autonomic Provisioning Approach for Cloud Services

Qing

Zheng

Wei

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Resource elastic scheduling is a key feature of cloud services. The elastic makes cloud services have the ability to flexibly increase or decrease resources to satisfy user needs, and dynamically allocate resources for cloud services on demand. The amount of resources to be configured is determined at runtime based on the changes in workload to flexibly respond to the fluctuating demands of cloud services. Appropriate resources need to be configured in advance. In this article, we propose a dynamic resource provisioning framework based on the MAPE loop, and use a two-tier elastic resource configuration for collaborative work. In order to implement the proposed framework, we propose an elastic resource scheduling algorithm based on a combination of the autonomic computing and deep reinforcement learning (DRL) to reduce task rejection rate of the virtual machine (VM) and increase utilization to obtain as much profit as possible. In this paper, Experimental results using actual Google cluster tracking results show that the proposed policy reduces the total cost about 17%-58% and increases the profit by up to not less than 9%, reduces the service level agreement (SLA) violations to less than 0.4% to better guarantee the quality of service (QoS).

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Autonomic resource provisioning for multilayer cloud applications with K‐nearest neighbor resource scaling and priority‐based resource allocation

Cited by 12 publications

References 46 publications

Analysis of Co-Construction and Sharing Mechanism of Digital Education Resources based on Digital Information Encryption Technology

Analysis of Co-Construction and Sharing Mechanism of Digital Education Resources based on Digital Information Encryption Technology

Autonomic cloud computing based management and security solutions: State‐of‐the‐art, challenges, and opportunities

A Deep Reinforcement Learning Based Resource Autonomic Provisioning Approach for Cloud Services

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