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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Resource allocation for MEC system with multi-users resource competition based on deep reinforcement learning approach
- NSF-PAR ID:
- 10356728
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Computer Networks
- Volume:
- 215
- Issue:
- C
- ISSN:
- 1389-1286
- Page Range / eLocation ID:
- 109181
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.comnet.2022.109181
