Enhancing Web Application Using Adaptive Containerized Application Placement Based on Clustering and Content Caching in The Cloud Environment
How to Cite?
Mohamed. I.El-Shenawy, Hayam Mousa, Khaled M. Amin, "Enhancing Web Application Using Adaptive Containerized Application Placement Based on Clustering and Content Caching in The Cloud Environment," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 162-169, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I1P218
Datacenter traffic increases from day to day due to the massive increase of web applications hosted on the Internet. Some tools are used in resource management and capacity assessment in order to preserve a good performance for these applications. The container is a new trend for packaging and deploying micro-service-based applications. It is widely used to improve performance and achieve high user satisfaction. Autoscaling has become a vital feature in such applications’ performance. This article targets to improve the quality of service through increasing resources utilization and reducing the number of application container kills and recreation. These targets can be achieved through dependency on the healthier nodes that have adequate resources. Machine Learning classification algorithms are used to predict healthy hosts. Then, a clustering algorithm is used to cluster healthy nodes into groups of containers workers` hosts based on their CPU and RAM utilization. In addition, content caching service has been integrated to improve application performance. This service decreases the network traffic to hosts nodes which subsequently decreases the required resources to handle these requests. The results ensure that the proposed model can achieve lower node failure with 33% of the default system. It also saves around 36% of bandwidth.
Cloud computing, containers, autoscaling, virtualization, orchestration, machine learning.
 Celesti, D. Mulfari, M. Fazio, M. Villari, and A. Puliafito, Exploring Container Virtualization in IoT Clouds, 2016 IEEE International Conference on Smart Computing (SMARTCOMP), (2016) 1-6.
 A. M. Potdar, N. D G, S. Kengond, and M. M. Mulla, Performance evaluation of docker container and virtual machine, Procedia Computer Science, 171 (2020) 1419–1428.
 E. Casalicchio, Container Orchestration: A survey, Systems Modeling: Methodologies and Tools, (2018) 221–235.
 P. R. Desai. A survey of performance comparison between virtual machines and containers. ijcseonline. Org, (2016).
 K.B. Laura R.Moore, T.Ellahi. A coordinated reactive and predictive approach to cloud elasticity, The Fourth International Conference on Cloud Computing, GRIDs, and Virtualization, (2013).
 C.-C. Lin, J.-J. Wu, P. Liu, J.-A. Lin, and L.-C. Song, Automatic Resource Scaling for web applications in the cloud, Grid and Pervasive Computing, (2013) 81–90.
 S. Sotiriadis, N. Bessis, and R. Buyya, Self-managed virtual machine scheduling in Cloud Systems, Information Sciences, 433- 434 (2018) 381–400.
 X. Chen, L. Rupprecht, R. Osman, P. Pietzuch, F. Franciosi, and W. Knottenbelt, CloudScope: Diagnosing and Managing Performance Interference in Multi-tenant Clouds. In: 2015 IEEE 23rd International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, (2015).
 S. A. Yousif & A. Al-Dulaimy, Clustering Cloud Workload Traces to Improve the Performance of Cloud Data Centers, In Proceedings of the World Congress on Engineering Conference, (2017).
 M. Xu, C.Q. Wu, A. Hou, Y. Wang, Intelligent scheduling for parallel jobs in big data processing systems, 2019 International Conference on Computing, Networking and Communications, (2019) 22–28.
 M. Nardelli, V. Cardellini, and E. Casalicchio, Multi-level elastic deployment of containerized applications in geo-distributed environments, 2018 IEEE 6th International Conference on Future Internet of Things and Cloud, (2018).
 T.-T. Nguyen, Y.-J. Yeom, T. Kim, D.-H. Park, and S. Kim, Horizontal Pod Autoscaling in Kubernetes for Elastic Container Orchestration, Sensors, (2020).
 Kubernetes. Managing Resources for Containers (2021). [online] Available at: https://kubernetes.io/docs/concepts/configuration/manageresources- containers/
 M. Zekri, S. E. Kafhali, N. Aboutabit, and Y. Saadi, DDoS attack detection using machine learning techniques in cloud computing environments, In:2017 3rd International Conference of Cloud Computing Technologies and Applications CloudTech, (2017) 1-7.
 S. K. Sood, R. Sandhu, K. Singla, and V. Chang, IoT, big data and HPC based smart flood management framework, Sustainable Computing: Informatics and Systems, 20 (2018) 102-11.
 A. Yassine, S. Singh, M. S. Hossain, and G. Muhammad, IOT big data analytics for smart homes with fog and cloud computing, Future Generation Computer Systems, 91 (2019) 563–573.
 M. Liu, F. R. Yu, Y. Teng, V. C. M. Leung and M. Song, Computation Offloading and Content Caching in Wireless Blockchain Networks With Mobile Edge Computing, in IEEE Transactions on Vehicular Technology, 67(11) (2018) 11008- 11021.
 J. Tang, T. Q. S. Quek, T. Chang and B. Shim, Systematic Resource Allocation in Cloud RAN With Caching as a Service Under Two Timescales, in IEEE Transactions on Communications, 67 (2019) 7755-7770,.
 O. Ayoub, F. Musumeci, M. Tornatore, and A. Pattavina, Energy- Efficient Video-On-Demand Content Caching and Distribution in Metro Area Networks, in IEEE Transactions on Green Communications and Networking, 3(1) (2019) 159-169.
 J. Song, M. Sheng, T. Q. S. Quek, C. Xu, and X. Wang, Learning- Based Content Caching and Sharing for Wireless Networks, in IEEE Transactions on Communications, 65(10) (2017) 4309-4324.
 T. Chen, B. Dong, Y. Chen, Y. Du, and S. Li, Multi-Objective Learning for Efficient Content Caching for Mobile Edge Networks, 2020 International Conference on Computing, Networking and Communications (ICNC), (2020) 543-547.
 D. E. Jayanti, R. Umar, and I. Riadi, Implementation of Cloudflare hosting for access speed on trading websites, SISFOTENIKA, 10(2) (2020) 227.