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OpenStack Generalization for Hardware Accelerated Clouds
Date
2019-01-01
Author
Erol, Ahmet
Yazar, Alper
Schmidt, Şenan Ece
Metadata
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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OpenStack is a widely used management tool for cloud computing which is designed to work on servers and allocate standard computing resources such as CPU, memory or disk. The current trend for integrating different hardware accelerators such as FPGAs and GPUs in the cloud requires managing these heterogeneous resources. In this paper, we propose a generalization for OpenStack Nova project which extends the relevant data structures to include these new resources. More importantly, we present a new lightweight Nova Compute module that we call Nova-G Compute. Nova-G Compute is suitable to work with different hardware platforms and can communicate with the rest of the OpenStack Projects. We implement a hypervisor-like software to enable Nova-G Compute accessing the FPGA resources. We perform experimental evaluation of Nova-G Compute using the known and used OpenStack benchmarking tool Rally. Our results show that Nova-G Compute works as desired without any reduced performance compared to standard Nova.
Subject Keywords
Rally
,
Nova
,
FPGA
,
OpenStack
,
Virtualization
,
Cloud computing
URI
https://hdl.handle.net/11511/37181
DOI
https://doi.org/10.1109/icccn.2019.8847115
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
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OpenStack is a widely used management tool for cloud computing which is designed to work on servers and allocate standard computing resources such as CPU, memory or disk. The current trend for integrating different hardware accelerators such as FPGAs and GPUs in the cloud requires managing these heterogeneous resources. In this thesis, we propose a generalization for OpenStack Nova project which extends the relevant data structures to include these new resources. More importantly, we present a new lightweig...
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A. Erol, A. Yazar, and Ş. E. Schmidt, “OpenStack Generalization for Hardware Accelerated Clouds,” 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37181.
