Guided container selection for data streaming through neural learning in cloud
Springer Science and Business Media LLC - Trang 1-7 - 2021
Tóm tắt
In Big data computing domains with a huge network of connected devices involved in various internet and social network concerns mainly for security, integrity, authentication and data privacy. Allocation and efficient usage of containers provided by cloud service providers has huge impact over efficient data processing and data handling. Batch processing method emphasizes huge databases by letting it into the programmable domains and segregate in accordance with their size, reliability, processing speed and required memory space. Whereas, Stream processing involves scrutinizing data promptly before entering into the stream and scrutinizing will be done accordingly. Container selection plays a major role in such processing methodologies and promptly makes the effective resource scheduling possible and efficient in cloud service providing. In our proposed method, the Guided Container Selection (GCS) process eradicates the bottle neck problem by selecting an efficient and optimal container which satisfies all requirements like required size, reliability, processing speed etc. Implementing either batch processing or stream processing to analyze solutions for multiple domain container selection which will be analyzed and resolved through Deep Neural Learning (DNL). The novel DNL method successfully ranks and handles optimal container selection according to the dynamic data involvement and provides efficient solutions for data processing.In future it also helps selecting appropriate containers for similar requirements for cloud service providers and also for its consumers.
Tài liệu tham khảo
Bhowmik M, Raghunandan A, Rudra B (2020) Distributed adaptive video streaming using inter-server data distribution and agent-based adaptive load balancing. In: 2020 16th international conference on distributed computing in sensor systems (DCOSS), Marina del Rey, CA, USA, pp. 273–280. doi: https://doi.org/10.1109/DCOSS49796.2020.00051.
Chen Y, Zheng Q, Yan Z, Liu D (2021) QShield: Protecting outsourced cloud data queries with multi-user access control based on SGX. IEEE Trans Parall Distri Syst 32(2):485–499. https://doi.org/10.1109/TPDS.2020.3024880
Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107–113
Dos Anjos JCS et al (2020) Data processing model to perform big data analytics in hybrid infrastructures. IEEE Access 8:170281–170294. https://doi.org/10.1109/ACCESS.2020.3023344
Girs S, Sentilles S, Asadollah SA, Ashjaei M, Mubeen S (2020) A systematic literature study on definition and modeling of service-level agreements for cloud services in IoT. IEEE Access 8:134498–134513. https://doi.org/10.1109/ACCESS.2020.3011483
Haggag M, Tantawy MM, El-Soudani MMS (2020) Implementing a deep learning model for intrusion detection on apache spark platform. IEEE Access 8:163660–163672. https://doi.org/10.1109/ACCESS.2020.3019931
Hochreiner C, Vogler M, Schulte S, Dustdar S (2016) Elastic stream processing for the internet of things. In: 2016 IEEE 9th international conference on cloud computing (CLOUD), June 2016.
Jain DK, Jain R, Upadhyay Y et al (2019) Deep refinement: capsule network with attention mechanism-based system for text classification. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04620-z
Kumar J, Saxena D, Singh AK et al (2020) BiPhase adaptive learning-based neural network model for cloud datacenter workload forecasting. Soft Comput 24:14593–14610. https://doi.org/10.1007/s00500-020-04808-9
Li Z, Jin H, Zou D, Yuan B (2020) Exploring new opportunities to defeat low-rate DDoS attack in container-based cloud environment. IEEE Trans Parall Distrib Syst 31(3):695–706. https://doi.org/10.1109/TPDS.2019.2942591
Lin M, Xi J, Bai W, Wu J (2019) Ant colony algorithm for multi-objective optimization of container-based microservice scheduling in cloud. IEEE Access 7:83088–83100. https://doi.org/10.1109/ACCESS.2019.2924414
Ma X, Zhou Q (2020) Special issue on deep learning and neural computing for intelligent sensing and control. Neural Comput Appl 32:1811–1812. https://doi.org/10.1007/s00521-020-04785-y
Marangozova-Martin V, de Palma N, El Rheddane A (2019) Multi-level elasticity for data stream processing. IEEE Trans Parall Distrib Syst 30(10):2326–2337. https://doi.org/10.1109/TPDS.2019.2907950
Prasanth T, Gunasekaran M (2019) Effective big data retrieval using deep learning modified neural networks. Mobile Netw Appl 24:282–294. https://doi.org/10.1007/s11036-018-1204-y
Tareq M, Sundararajan EA, Mohd M, Sani NS (2020) Online clustering of evolving data streams using a density grid-based method. IEEE Access 8:166472–166490. https://doi.org/10.1109/ACCESS.2020.3021684
Wang S, Ding Z, Jiang C (2021) Elastic scheduling for microservice applications in clouds. IEEE Trans Parall Distrib Syst 32(1):98–115. https://doi.org/10.1109/TPDS.2020.3011979
Zhang H, Geng X, Ma H (2020) Learning-driven interference-aware workload parallelization for streaming applications in heterogeneous cluster. IEEE Trans Parall Distrib Syst 32(1):1–15. https://doi.org/10.1109/TPDS.2020.3008725
Zhong X, Liu J, Li L et al (2019) An emotion classification algorithm based on SPT-CapsNet. Neural Comput Appl 32(7):1823–1837. https://doi.org/10.1007/s00521-019-04621-y
Zhou R, Li Z, Wu C (2018) Scheduling frameworks for cloud container services. IEEE/ACM Trans Netw 26(1):436–450. https://doi.org/10.1109/TNET.2017.2781200