Data centers and clouds are increasingly offering low-cost computational resources in the form of transient virtual machines. Whenever demand for computational resources exceeds their availability, transient resources can reclaimed by preempting the transient VMs. Conventionally, these transient VMs are used by low-priority applications that can tolerate the disruption caused by preemptions.
In this paper we propose an alternative approach for reclaiming resources, called resource deflation. Resource deflation allows applications to dynamically shrink (and expand) in response to resource pressure, instead of being preempted outright. Deflatable VMs allow applications to continue running even under resource pressure, and increase the utility of low-priority transient resources. Deflation uses a dynamic, multi-level cascading reclamation technique that allows applications, operating systems, and hypervisors to implement their own policies for handling resource pressure. For distributed data processing, machine learning, and deep neural network training, our multi-level approach reduces the performance degradation by up to 2x compared to existing preemption-based approaches. When deflatable VMs are deployed on a cluster, our policies allow up to 1.6x utilization without the risk of preemption.
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Memtrade: Marketplace for Disaggregated Memory Clouds
We present Memtrade, the first practical marketplace for disaggregated memory clouds. Clouds introduce a set of unique challenges for resource disaggregation across different tenants, including resource harvesting, isolation, and matching. Memtrade allows producer virtual machines (VMs) to lease both their unallocated memory and allocated-but-idle application memory to remote consumer VMs for a limited period of time. Memtrade does not require any modifications to host-level system software or support from the cloud provider. It harvests producer memory using an application-aware control loop to form a distributed transient remote memory pool with minimal performance impact; it employs a broker to match producers with consumers while satisfying performance constraints; and it exposes the matched memory to consumers through different abstractions. As a proof of concept, we propose two such memory access interfaces for Memtrade consumers -- a transient KV cache for specified applications and a swap interface that is application-transparent. Our evaluation using real-world cluster traces shows that Memtrade provides significant performance benefit for consumers (improving average read latency up to 2.8X) while preserving confidentiality and integrity, with little impact on producer applications (degrading performance by less than 2.1%).
more » « less- Award ID(s):
- 1845853
- NSF-PAR ID:
- 10516022
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Measurement and Analysis of Computing Systems
- Volume:
- 7
- Issue:
- 2
- ISSN:
- 2476-1249
- Page Range / eLocation ID:
- 1 to 27
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3589985
