Proactive Auto-Scaling for Service Function Chains in Cloud Computing Based on Deep Learning
Auto-scaler system enables high Quality of Service (QoS) with low cost to survive in a competitive market. Indeed, the auto-scaling of Virtual Network Functionality (VNFs) can adaptively allocate the Cloud resources for various VNFs based on workload demands at any time. However, the intensity of wo...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10464297/ |
| _version_ | 1797243329912504320 |
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| author | Mohammad Bany Taha Yousef Sanjalawe Ahmad Al-Daraiseh Salam Fraihat Salam R. Al-E'mari |
| author_facet | Mohammad Bany Taha Yousef Sanjalawe Ahmad Al-Daraiseh Salam Fraihat Salam R. Al-E'mari |
| author_sort | Mohammad Bany Taha |
| collection | DOAJ |
| description | Auto-scaler system enables high Quality of Service (QoS) with low cost to survive in a competitive market. Indeed, the auto-scaling of Virtual Network Functionality (VNFs) can adaptively allocate the Cloud resources for various VNFs based on workload demands at any time. However, the intensity of workload is dynamically changed because of the variation in service demand over time. The predominant auto-scaling approaches use scaling rules (threshold-based reactive approach) or scaling policies (schedule-based proactive approach) to adapt resources and meet the performance requirements of each VNF. The reactive approaches can significantly degrade the VNF performance for improper reconfiguration or variation of auto-scaling rules. Conversely, the proactive approaches dynamically adjust the scaling policies according to the workload variation. These approaches rely on accurate workload predictive models (e.g., time-series models). This paper proposes a real-time proactive auto-scalar system based on a deep learning model that can efficiently predict the future values of CPU, Memory, and Bandwidth for VNFs for a Service Function Chain (SFC) to proactively auto-scale the resources allocated to each VNF in a Cloud platform. A hybrid model of MLP-LSTM is used to forecast the values of different features. Auto-correlation is used to identify the abnormal events of instances in the Cloud platform by measuring the repeated pattern for each identified impact feature. Moreover, the auto-scalar system enables to predict the abnormal values for some features during the online stage using the Auto-regression model to meet the QoS requirements of an SFC. |
| first_indexed | 2024-04-24T18:53:24Z |
| format | Article |
| id | doaj.art-cba5f5b7c49b4b0bac30ab37530f0d53 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:53:24Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cba5f5b7c49b4b0bac30ab37530f0d532024-03-26T17:48:58ZengIEEEIEEE Access2169-35362024-01-0112385753859310.1109/ACCESS.2024.337577210464297Proactive Auto-Scaling for Service Function Chains in Cloud Computing Based on Deep LearningMohammad Bany Taha0https://orcid.org/0000-0001-8748-0243Yousef Sanjalawe1https://orcid.org/0000-0002-4442-1865Ahmad Al-Daraiseh2Salam Fraihat3https://orcid.org/0000-0002-1025-7868Salam R. Al-E'mari4https://orcid.org/0000-0002-2134-4158Data Science and Artificial Intelligence Department, Faculty of Information Technology, American University of Madaba, Amman, JordanCybersecurity Department, Faculty of Information Technology, American University of Madaba, Amman, JordanComputer Science Department, Faculty of Information Technology, American University of Madaba, Amman, JordanArtificial Intelligence Research Centre, Ajman University, Ajman, United Arab EmiratesInformation Security Department, Faculty of Information Technology, University of Petra, Amman, JordanAuto-scaler system enables high Quality of Service (QoS) with low cost to survive in a competitive market. Indeed, the auto-scaling of Virtual Network Functionality (VNFs) can adaptively allocate the Cloud resources for various VNFs based on workload demands at any time. However, the intensity of workload is dynamically changed because of the variation in service demand over time. The predominant auto-scaling approaches use scaling rules (threshold-based reactive approach) or scaling policies (schedule-based proactive approach) to adapt resources and meet the performance requirements of each VNF. The reactive approaches can significantly degrade the VNF performance for improper reconfiguration or variation of auto-scaling rules. Conversely, the proactive approaches dynamically adjust the scaling policies according to the workload variation. These approaches rely on accurate workload predictive models (e.g., time-series models). This paper proposes a real-time proactive auto-scalar system based on a deep learning model that can efficiently predict the future values of CPU, Memory, and Bandwidth for VNFs for a Service Function Chain (SFC) to proactively auto-scale the resources allocated to each VNF in a Cloud platform. A hybrid model of MLP-LSTM is used to forecast the values of different features. Auto-correlation is used to identify the abnormal events of instances in the Cloud platform by measuring the repeated pattern for each identified impact feature. Moreover, the auto-scalar system enables to predict the abnormal values for some features during the online stage using the Auto-regression model to meet the QoS requirements of an SFC.https://ieeexplore.ieee.org/document/10464297/Deep learningtime series forecastingVNFsQoSLSTMSFC |
| spellingShingle | Mohammad Bany Taha Yousef Sanjalawe Ahmad Al-Daraiseh Salam Fraihat Salam R. Al-E'mari Proactive Auto-Scaling for Service Function Chains in Cloud Computing Based on Deep Learning IEEE Access Deep learning time series forecasting VNFs QoS LSTM SFC |
| title | Proactive Auto-Scaling for Service Function Chains in Cloud Computing Based on Deep Learning |
| title_full | Proactive Auto-Scaling for Service Function Chains in Cloud Computing Based on Deep Learning |
| title_fullStr | Proactive Auto-Scaling for Service Function Chains in Cloud Computing Based on Deep Learning |
| title_full_unstemmed | Proactive Auto-Scaling for Service Function Chains in Cloud Computing Based on Deep Learning |
| title_short | Proactive Auto-Scaling for Service Function Chains in Cloud Computing Based on Deep Learning |
| title_sort | proactive auto scaling for service function chains in cloud computing based on deep learning |
| topic | Deep learning time series forecasting VNFs QoS LSTM SFC |
| url | https://ieeexplore.ieee.org/document/10464297/ |
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