<i>HybridDep</i> : An elastic hybrid resources allocation strategy for I/O‐intensive applications
Abstract Along with the rapid development of B5G/6G, the number of applications grows rapidly and the data amount explodes exponentially, putting a massive burden on the resource‐limited edge servers. To fully utilize the limited resources, virtualization technology is introduced to provide elastic deployment for applications in edge servers. But for I/O‐intensive applications, allocating elastic resources is not as easy as for compute‐intensive ones, because the amount of required I/O resources is unknown due to the request uncertainty. Many existing researches try to solve this multi‐application deployment problem by peaks clipping and valleys filling, to resource utilization. However, in fact, the times of peaks and valleys of most hybrid deployed applications are similar to each other, which invalidates those traditional solutions. To address this challenge, the actual data is analysed and complementary peak and valley periods in time and space dimensions are found. Based on this finding, an elastic hybrid deployment strategy HybridDep is proposed, for multiple I/O‐intensive applications. Validated by simulation experiments using real datasets and traces, this algorithm can reduce about 3.2% deployment cost than the compared algorithm.
Authors
- Ai‐Zhi Wu (ORCID: https://orcid.org/0000-0001-7981-3466)
- Pengmiao Li (ORCID: https://orcid.org/0000-0002-6823-2161)
- Sang Y. Yun
- Fucai Yu (ORCID: https://orcid.org/0009-0001-5659-1642)
- Yuchao Zhang (ORCID: https://orcid.org/0000-0002-0135-8915)
Institutions
- Beijing University of Posts and Telecommunications (CN)
Publication Details
- Journal
- IET Communications
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1049/cmu2.70007
- Citations
- 1
- Primary Topic
- Advanced Data Storage Technologies
- Type
- article
- Field-Weighted Citation Impact
- 1.49
Funders
- National Natural Science Foundation of China
- Beijing Nova Program
- Beijing Science and Technology Planning Project