Application of an Enhanced Self-adapting Differential Evolution Algorithm to Workload Prediction in Cloud Computing

Attia, Mahmoud A.; Arafa, M.; Sallam, Elsayed A.; Fahmy, Mahmoud

doi:10.5815/ijitcs.2019.08.05

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…The suggested artificial neural network proposed methodology, which increased the artificial neural network model's effectiveness as well as accuracy rate for the purpose of forecasting future workloads, is trained using the MSaDE method. However, this process requires a lot of time [23].…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

SGA Model for Prediction in Cloud Environment

Krishnan¹,

Prasanthi

2023

IJRITCC

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With virtual information, cloud computing has made applications available to users everywhere. Efficient asset workload forecasting could help the cloud achieve maximum resource utilisation. The effective utilization of resources and the reduction of datacentres power both depend heavily on load forecasting. The allocation of resources and task scheduling issues in clouds and virtualized systems are significantly impacted by CPU utilisation forecast. A resource manager uses utilisation projection to distribute workload between physical nodes, improving resource consumption effectiveness. When performing a virtual machine distribution job, a good estimation of CPU utilization enables the migration of one or more virtual servers, preventing the overflow of the real machineries. In a cloud system, scalability and flexibility are crucial characteristics. Predicting workload and demands would aid in optimal resource utilisation in a cloud setting. To improve allocation of resources and the effectiveness of the cloud service, workload assessment and future workload forecasting could be performed. The creation of an appropriate statistical method has begun. In this study, a simulation approach and a genetic algorithm were used to forecast workloads. In comparison to the earlier techniques, it is anticipated to produce results that are superior by having a lower error rate and higher forecasting reliability. The suggested method is examined utilizing statistics from the Bit brains datacentres. The study then analyses, summarises, and suggests future study paths in cloud environments.

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Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

SGA Model for Prediction in Cloud Environment

Krishnan¹,

Prasanthi

2023

IJRITCC

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“…Many recent works address this problem. For example, Attia et al [39] use the Differential Evolution (DE) algorithm named MSaDE and Artificial Neural Network (ANN) to forecast the workload of cloud-hosted applications. Iqbal et al [40] use the unsupervised learning approach to find future workload patterns for web applications.…”

Section: Related Workmentioning

confidence: 99%

Online Workload Burst Detection for Efficient Predictive Autoscaling of Applications

et al. 2020

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Autoscaling methods are employed to ensure the scalability of cloud-hosted applications. The public-facing applications are prone to receive sudden workload bursts, and the existing autoscaling methods do not handle the bursty workloads gracefully. It is challenging to detect the burst online from the incoming dynamic workload traffic, and then identifying appropriate resources to address the burst without overprovisioning is even harder. In this paper, we address this challenge by investigating the appropriate method for online burst detection and then proposed a novel predictive autoscaling method to use burst detection for satisfying specific response time requirements. We compared the proposed method with multiple state-of-the-art baseline autoscaling methods under multiple realistic and synthetic bursty workloads for a benchmark application. Our experimental results show a 60.8% average decrease in response time violations as compared to the baseline method. INDEX TERMS Autoscaling, predictive, SLO violations, response time, workload, burstiness, online burst detection.

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Understanding Web Application Workloads and Their Applications: Systematic Literature Review and Characterization

Aghili,

Qin,

et al. 2024

2024 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Application of an Enhanced Self-adapting Differential Evolution Algorithm to Workload Prediction in Cloud Computing

Cited by 3 publications

References 18 publications

SGA Model for Prediction in Cloud Environment

SGA Model for Prediction in Cloud Environment

Online Workload Burst Detection for Efficient Predictive Autoscaling of Applications

Understanding Web Application Workloads and Their Applications: Systematic Literature Review and Characterization

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