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Data-intensive applications often suffer from significant memory pressure, resulting in excessive garbage collection (GC) and out-of-memory (OOM) errors, harming system performance and reliability. In this paper, we demonstrate how lightweight virtualization via OS containers opens up opportunities to address memory pressure and realize memory elasticity: 1) tasks running in a container can be set to a large heap size to avoid OutOfMemory (OOM) errors, and 2) tasks that are under memory pressure and incur significant swapping activities can be temporarily "suspended" by depriving resources from the hosting containers, and be "resumed" when resources are available. We propose and develop Pufferfish, an elastic memory manager, that leverages containers to flexibly allocate memory for tasks. Memory elasticity achieved by Pufferfish can be exploited by a cluster scheduler to improve cluster utilization and task parallelism. We implement Pufferfish on the cluster scheduler Apache Yarn. Experiments with Spark and MapReduce on real-world traces show Pufferfish is able to avoid OOM errors, improve cluster memory utilization by 2.7x and the median job runtime by 5.5x compared to a memory over-provisioning solution.
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Fast in-memory CRIU for docker containers
Server systems with large amounts of physical memory can benefit from using some of the available memory capacity for in-memory snapshots of the ongoing computations. In-memory snapshots are useful for services such as scaling of new workload instances, debugging, during scheduling, etc., which do not require snapshot persistence across node crashes/reboots. Since increasingly more frequently servers run containerized workloads, using technologies such as Docker, the snapshot, and the subsequent snapshot restore mechanisms, would be applied at granularity of containers. However, CRIU, the current approach to snapshot/restore containers, suffers from expensive filesystem write/read operations on image files containing memory pages, which dominate the runtime costs and impact the potential benefits of manipulating in-memory process state. In this paper, we demonstrate that these overheads can be eliminated by using MVAS -- kernel support for multiple independent virtual address spaces (VAS), designed specifically for machines with large memory capacities. The resulting VAS-CRIU stores application memory as a separate snapshot address space in DRAM and avoids costly file system operations. This accelerates the snapshot/restore of address spaces by two orders of magnitude, resulting in an overall reduction in snapshot time by up to 10× and restore time by up to 9×. We demonstrate the utility of VAS-CRIU for container management services such as fine-grained snapshot generation and container instance scaling.
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- Award ID(s):
- 1822972
- PAR ID:
- 10192799
- Date Published:
- Journal Name:
- MEMSYS '19: Proceedings of the International Symposium on Memory Systems
- Page Range / eLocation ID:
- 53 to 65
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Data-intensive applications often suffer from significant memory pressure, resulting in excessive garbage collection (GC) and out-of-memory (OOM) errors, harming system performance and reliability. In this paper, we demonstrate how lightweight virtualization via OS containers opens up opportunities to address memory pressure and realize memory elasticity: 1) tasks running in a container can be set to a large heap size to avoid OutOfMemory (OOM) errors, and 2) tasks that are under memory pressure and incur significant swapping activities can be temporarily "suspended" by depriving resources from the hosting containers, and be "resumed" when resources are available. We propose and develop Pufferfish, an elastic memory manager, that leverages containers to flexibly allocate memory for tasks. Memory elasticity achieved by Pufferfish can be exploited by a cluster scheduler to improve cluster utilization and task parallelism. We implement Pufferfish on the cluster scheduler Apache Yarn. Experiments with Spark and MapReduce on real-world traces show Pufferfish is able to avoid OOM errors, improve cluster memory utilization by 2.7x and the median job runtime by 5.5x compared to a memory over-provisioning solution.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3357526.3357542
