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Abstract
Hardware disaggregation decouples resources (e.g., processors and memory) from monolithic servers, potentially improving service reliability. However, from another perspective, directly exposing resource modules to a shared network may adversely affect service reliability. In this paper, we study a reliable resource allocation problem in disaggregated DCs (DDCs), considering network impact and different disaggregation scales. We provide a mixed-integer linear programming formulation and a resource allocation framework named Radar for this problem. Numerical results demonstrate that the benefits of hardware disaggregation may be adversely affected by an imperfect network. It also shows that both the hardware backup and a proposed migration-based restoration can be applied to overcome this potential adverse effect.
Original language | English |
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Pages (from-to) | 8551-8563 |
Journal | IEEE Transactions on Industrial Informatics |
Volume | 19 |
Issue number | 8 |
Online published | 15 Nov 2022 |
DOIs | |
Publication status | Published - Aug 2023 |
Research Keywords
- Composable/disaggregated infrastructure
- hardware disaggregation
- data center
- reliability
- network
Publisher's Copyright Statement
- COPYRIGHT TERMS OF DEPOSITED POSTPRINT FILE: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Guo, C., Zukerman, M., & Wang, T. (2023). Radar: Reliable Resource Scheduling for Composable/Disaggregated Data Centers. IEEE Transactions on Industrial Informatics, 19(8), 8551-8563. https://doi.org/10.1109/TII.2022.3222348.
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- 1 Finished
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DON_RMG: Solutions and Methodologies for Telecommunications Networks Design - RMGS
ZUKERMAN, M. (Principal Investigator / Project Coordinator)
1/01/20 → 27/02/23
Project: Research