Prediction of Cloud Ranking in a Hyperconverged Cloud Ecosystem Using Machine Learning

Tabassum, Nadia; Ditta, Allah; Alyas, Tahir; Abbas, Sagheer; Alquhayz, Hani; Mian, Natash Ali; Khan, Muhammad Adnan

doi:10.32604/cmc.2021.014729

Cited by 35 publications

(20 citation statements)

References 14 publications

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“…Machine learning arose over the last two decades from the increasing capacity of computers to process large amounts of data. Computational Intelligence approaches like Swarm Intelligence [16], Evolutionary Computing [17] like Genetic Algorithm [18], Neural Network [19], Deep Extreme Machine learning [20] and Fuzzy system [21][22][23][24][25][26][27] are strong candidate solution in the field of smart city [28][29][30], smart health [31][32][33], and wireless communication [34,35], etc. Some machines could also uncover secret patterns and complicated interactions that humans could not, allowing them to make appropriate and accurate judgments in the face of extraordinarily disruptive and discontinuous data.…”

Section: Adoption Of Machine Learning In Supply Chain Collaborationmentioning

confidence: 99%

Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques

Ali¹,

Ghazal²,

Ahmed³

et al. 2022

Intelligent Automation &Amp; Soft Computing

Self Cite

179

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Supply Chain Collaboration is the network of various entities that work cohesively to make up the entire process. The supply chain organizations' success is dependent on integration, teamwork, and the communication of information. Every day, supply chain and business players work in a dynamic setting. They must balance competing goals such as process robustness, risk reduction, vulnerability reduction, real financial risks, and resilience against just-in-time and cost-efficiency. Decision-making based on shared information in Supply Chain Collaboration constitutes the recital and competitiveness of the collective process. Supply Chain Collaboration has prompted companies to implement the perfect data analytics functions (e.g., data science, predictive analytics, and big data) to improve supply chain operations and, eventually, efficiency. Simulation and modeling are powerful methods for analyzing, investigating, examining, observing and evaluating real-world industrial and logistic processes in this scenario. Fusion-based Machine learning provides a platform that may address the issues/limitations of Supply Chain Collaboration. Compared to the Classical probable data fusion techniques, the fused Machine learning method may offer a strong computing ability and prediction. In this scenario, the machine learningbased Supply Chain Collaboration model has been proposed to evaluate the propensity of the decision-making process to increase the efficiency of the Supply Chain Collaboration.

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Section: Adoption Of Machine Learning In Supply Chain Collaborationmentioning

confidence: 99%

Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques

Ali¹,

Ghazal²,

Ahmed³

et al. 2022

Intelligent Automation &Amp; Soft Computing

Self Cite

179

View full text Add to dashboard Cite

show abstract

“…Deep & Machine learning arose over the last two decades from the increasing capacity of computers to process large amounts of data empowered with cloud computing [27,28]. Computational Intelligence approaches like Swarm Intelligence [29], Evolutionary Computing [30] like Genetic Algorithm [31], Neural Network [32], Deep Extreme Machine learning [33] and Fuzzy system [34][35][36][37][38] are strong candidate solutions in the field of the smart city [39][40][41], smart health empowered with cloud computing [42,43], and wireless communication [44,45,46], etc.…”

Section: Software As a Service (Saas's) Qosmentioning

confidence: 99%

“…Each host is hosting many virtual machines which can be invoked and removed dynamically. These technical and economic aids like the on-demand service of cloud computing increasing the trends of migration from traditional enterprise computing to cloud computing [6].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Model for Predicting the Quality of Services Violation

Khan¹,

Kanwal²,

Abbas³

et al. 2022

Computers, Materials &Amp; Continua

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Cloud computing is providing IT services to its customer based on Service level agreements (SLAs). It is important for cloud service providers to provide reliable Quality of service (QoS) and to maintain SLAs accountability. Cloud service providers need to predict possible service violations before the emergence of an issue to perform remedial actions for it. Cloud users' major concerns; the factors for service reliability are based on response time, accessibility, availability, and speed. In this paper, we, therefore, experiment with the parallel mutant-Particle swarm optimization (PSO) for the detection and predictions of QoS violations in terms of response time, speed, accessibility, and availability. This paper also compares Simple-PSO and Parallel Mutant-PSO. In simulation results, it is observed that the proposed Parallel Mutant-PSO solution for cloud QoS violation prediction achieves 94% accuracy which is many accurate results and is computationally the fastest technique in comparison of conventional PSO technique.

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“…Multiple virtual machines (VMs) operating on the same physical disk, known as VM consolidation, will increase cloud data center resource usage. Since the cloud infrastructure has become a popular solution, it is more critical than ever to take advantage of success opportunities and boost the cloud platform productivity [1].…”

Section: Introductionmentioning

confidence: 99%

Live Migration of Virtual Machines Using a Mamdani Fuzzy Inference System

Alyas¹,

Javed²,

Namoun³

et al. 2022

Computers, Materials &Amp; Continua

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Efforts were exerted to enhance the live virtual machines (VMs) migration, including performance improvements of the live migration of services to the cloud. The VMs empower the cloud users to store relevant data and resources. However, the utilization of servers has increased significantly because of the virtualization of computer systems, leading to a rise in power consumption and storage requirements by data centers, and thereby the running costs. Data center migration technologies are used to reduce risk, minimize downtime, and streamline and accelerate the data center move process. Indeed, several parameters, such as non-network overheads and downtime adjustment, may impact the live migration time and server downtime to a large extent. By virtualizing the network resources, the infrastructure as a service (IaaS) can be used dynamically to allocate the bandwidth to services and monitor the network flow routing. Due to the large amount of filthy retransmission, existing live migration systems still suffer from extensive downtime and significant performance degradation in crossdata-center situations. This study aims to minimize the energy consumption by restricting the VMs migration and switching off the guests depending on a threshold, thereby boosting the residual network bandwidth in the data center with a minimal breach of the service level agreement (SLA). In this research, we analyzed and evaluated the findings observed through simulating different parameters, like availability, downtime, and outage of VMs in data center processes. This new paradigm is composed of two forms of detection strategies in the live migration approach from the source host to the destination source machine.

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Prediction of Cloud Ranking in a Hyperconverged Cloud Ecosystem Using Machine Learning

Cited by 35 publications

References 14 publications

Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques

Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques

Intelligent Model for Predicting the Quality of Services Violation

Live Migration of Virtual Machines Using a Mamdani Fuzzy Inference System

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