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While permissioned blockchains enable a family of data center applications, existing systems suffer from imbalanced loads across compute and memory, exacerbating the underutilization of cloud resources. This paper presents FlexChain , a novel permissioned blockchain system that addresses this challenge by physically disaggregating CPUs, DRAM, and storage devices to process different blockchain workloads efficiently. Disaggregation allows blockchain service providers to upgrade and expand hardware resources independently to support a wide range of smart contracts with diverse CPU and memory demands. Moreover, it ensures efficient resource utilization and hence prevents resource fragmentation in a data center. We have explored the design of XOV blockchain systems in a disaggregated fashion and developed a tiered key-value store that can elastically scale its memory and storage. Our design significantly speeds up the execution stage. We have also leveraged several techniques to parallelize the validation stage in FlexChain to further improve the overall blockchain performance. Our evaluation results show that FlexChain can provide independent compute and memory scalability, while incurring at most 12.8% disaggregation overhead. FlexChain achieves almost identical throughput as the state-of-the-art distributed approaches with significantly lower memory and CPU consumption for compute-intensive and memory-intensive workloads respectively.
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Can Storage Devices be Power Adaptive?
Power is becoming a scarce resource for data centers, raising the need for power adaptive system design—the ability to dynamically change power consumption—to match available power. Storage makes up an increasing fraction of total data center power consumption. As such, it holds great potential to contribute to data center power adaptivity. To this end, we conduct a measurement study of power control mechanisms on a variety of modern data center storage devices. By changing device power states and shaping IO, we achieve a power dynamic range of up to 59.4% of the device’s maximum operating power. We also study power control trade-offs, including throughput and latency. Based on our observations, we construct storage device power-throughput models and discuss the implications on power adaptive storage system design.
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- PAR ID:
- 10580477
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400706301
- Page Range / eLocation ID:
- 47 to 54
- Format(s):
- Medium: X
- Location:
- Santa Clara CA USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3655038.3665945
