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Multi-cloud Task Management Technique for Diverse Computing Environment Tasks

다양한 컴퓨팅 환경 작업을 위한 멀티 클라우드 작업 관리 기법

  • Yoon-Su Jeong (Department of Game Software Engineering, Mokwon University)
  • 정윤수 (목원대학교 게임소프트웨어공학과)
  • Received : 2024.12.02
  • Accepted : 2025.01.20
  • Published : 2025.01.30

Abstract

Recently, the cloud environment is changing from a single cloud to a multi-cloud environment for service safety and efficient management. However, the multi-cloud service has a problem that the security and cost problems have not yet been completely solved. In this paper, we propose a multi-cloud task management technology to maximize interoperability in a multi-cloud environment. The proposed technology improves task quality and improves integrated management by applying correlation to multi-cloud tasks in various environments. In addition, the proposed technique recovers the damaged location of cloud tasks stored in the block by sharing various elements (time, place, URL, etc.) so that cloud tasks can be collected, processed, and stored on a cloud server and shared with different cloud users. As a result of performance evaluation, the accuracy of cloud task processing improved by 3.52%, the efficiency by 2.84% on average, and the latency by 1.02 ms on average.

최근 클라우드 환경은 서비스 안전성 및 효율적 관리를 위해서 단일 클라우드에서 다중 클라우드 환경으로 변화하고 있다. 그러나, 다중 클라우드 서비스는 보안 및 비용 문제가 아직 완벽하게 해결되지 않은 문제점이 있다. 본 논문에서는 다중 클라우드 환경에서 상호 운용성을 극대화하기 위한 멀티 클라우드 작업 관리 기술을 제안한다. 제안 기술은 다양한 환경에서 멀티 클라우드 작업에 상관도를 적용하여 작업 품질을 높이고 통합 관리 용이성을 향상시킨다. 또한, 제안 기법은 클라우드 작업을 클라우드 서버로 수집·처리·저장하여 서로 다른 클라우드 사용자와 공유할 수 있도록 다양한 요소(시간, 장소, URL 등)들을 공유함으로써, 블록에 저장된 클라우드 작업의 손상된 위치를 복구한다. 성능 평가 결과, 클라우드 작업 처리의 정확도는 평균 3.52%, 효율성은 평균 2.84%, 지연 시간은 평균 1.02 ms 향상된 결과를 얻었다.

Keywords

References

  1. Dayarathna, M., Wen, Y., & Fan, R. (2015). Data center energy consumption modeling: A survey. IEEE Communications surveys & tutorials, 18(1), 732-794. DOI : 10.1109/COMST.2015.2481183
  2. Jeong, Y. S. (2024). Design of HIoTAI Model with Big Data-Based AI Model in Cloud Environment. Smart Convergence Contents Society, 3(1), 8-15.
  3. Yadav, V., Malik, P., Kumar, A., & Sahoo, G. (2015, November). Energy efficient data center in cloud computing. In 2015 IEEE international conference on cloud computing in emerging markets (CCEM) (pp. 59-67). IEEE. DOI : 10.1109/CCEM.2015.14
  4. Chu, R. C., Simons, R. E., Ellsworth, M. J., Schmidt, R. R., & Cozzolino, V. (2004). Review of cooling technologies for computer products. IEEE Transactions on Device and materials Reliability, 4(4), 568-585. DOI : 10.1109/TDMR.2004.840855
  5. Jeong, Y. S. (2024). Blockchain-based Important Information Management Techniques for IoT Environment. Advanced Industrial SCIence, 3(1), 30-36. DOI : 10.23153/AI-Science.2024.3.1.030
  6. Barhate, S. (2022). An Implementation of Divide and Conquer Clustering Technique for Improving the Interoperability in Hybrid Cloud Environemnt. International Journal on Recent and Innovation Trends in Computing and Communication, 10, 182-189, DOI : 10.17762/ijritcc.v10i1s.5822
  7. Chee Yin, H. N., Kassem, M. M., & Mohamed Nazri, F. (2022). Comprehensive review of community seismic resilience: concept, frameworks, and case studies. Advances in Civil Engineering, 2022(1), 7668214. DOI : 10.1155/2022/7668214
  8. Jeong, Y. S. (2024). Blockchain and AI-based big data processing techniques for sustainable agricultural environments. Advanced Industrial SCIence, 3(2), 17-22. DOI : 10.23153/AI-Science.2024.3.2.017
  9. Jeong, Y. S. (2023). Hierarchical IoT Edge Resource Allocation and Management Techniques based on Synthetic Neural Networks in Distributed AIoT Environments. Advanced Industrial SCIence, 2(3), 8-14. DOI : 10.23153/AI-Science.2023.2.3.008
  10. Alyas, T., Alissa, K., Alqahtani, M., Faiz, T., Alsaif, S. A., Tabassum, N., & Naqvi, H. H. (2022). Multi‐Cloud Integration Security Framework Using Honeypots. Mobile Information Systems, 2022(1), 2600712. DOI : 10.1155/2022/2600712
  11. Kaur, J., Kaur, J., Kapoor, S., & Singh, H. (2021). Design & development of customizable web API for interoperability of antimicrobial resistance data. Sci Rep, 11, 11226. DOI : 10.1038/s41598-021-90601-z
  12. Xu, Z., & Chopra, S. S. (2023). Interconnectedness enhances network resilience of multimodal public transportation systems for Safe-to-Fail urban mobility. Nature communications, 14(1), 4291. DOI: 10.1038/s41467-023-39999-w
  13. Agbaegbu, J., Arogundade, O. T., Misra, S., & Damaševičius, R. (2021). Ontologies in cloud computing—review and future directions. Future Internet, 13(12), 302. DOI : 10.3390/fi13120302
  14. Cabrini, F. H., Valiante Filho, F., Rito, P., Barros Filho, A., Sargento, S., Venâncio Neto, A., & Kofuji, S. T. (2021). Enabling the industrial Internet of Things to cloud continuum in a real city environment. Sensors, 21(22), 7707. DOI : 10.3390/s21227707
  15. Tomarchio, O., Calcaterra, D., Di Modica, G., & Mazzaglia, P. (2021). Torch: a tosca-based orchestrator of multi-cloud containerised applications. Journal of Grid Computing, 19(1), 5. DOI : 10.1007/s10723-021-09549-z
  16. Saxena, D., Vaisla, K. S., & Rauthan, M. S. (2019). Abstract model of trusted and secure middleware framework for multi-cloud environment. In Advanced Informatics for Computing Research: Second International Conference, ICAICR 2018, Shimla, India, July 14-15, 2018, Revised Selected Papers, Part II 2 (pp. 469-479). Springer Singapore. DOI : 10.1007/978-981-13-3143-5_38