First-party datacenter workloads present new challenges to kernel memory management (MM), which allocates and maps memory and must balance competing performance concerns in an increasingly complex environment. In a datacenter, performance must be both good and consistent to satisfy service-level objectives. Unfortunately, current MM designs often exhibit inconsistent, opaque behavior that is difficult to reproduce, decipher, or fix, stemming from (1) a lack of high-quality information for policymaking, (2) the cost-unawareness of many current MM policies, and (3) opaque and distributed policy implementations that are hard to debug. For example, the Linux huge page implementation is distributed across 8 files and can lead to page fault latencies in the 100s of ms.
In search of a MM design that has consistent behavior, we designed Cost-Benefit MM (CBMM), which uses empirically based cost-benefit models and pre-aggregated profiling information to make MM policy decisions. In CBMM, policy decisions follow the guiding principle that userspace benefits must outweigh userspace costs. This approach balances the performance benefits obtained by a kernel policy against the cost of applying it. CBMM has competitive performance with Linux and HawkEye, a recent research system, for all the workloads we ran, and in the presence of fragmentation, CBMM is 35% faster than Linux on average. Meanwhile, CBMM nearly always has better tail latency than Linux or HawkEye, particularly on fragmented systems. It reduces the cost of the most expensive soft page faults by 2-3 orders of magnitude for most of our workloads, and reduces the frequency of 10-1000 us-long faults by around 2 orders of magnitude for multiple workloads.
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∅sim: Preparing System Software for a World with Terabyte-scale Memories
Recent advances in memory technologies mean that commodity machines may soon have terabytes of memory; however, such machines remain expensive and uncommon today. Hence, few programmers and researchers can debug and prototype fixes for scalability problems or explore new system behavior caused by terabyte-scale memories.
To enable rapid, early prototyping and exploration of system software for such machines, we built and open-sourced the ∅sim simulator. ∅sim uses virtualization to simulate the execution of huge workloads on modest machines. Our key observation is that many workloads follow the same control flow regardless of their input. We call such workloads data-oblivious. 0sim harnesses data-obliviousness to make huge simulations feasible and fast via memory compression.
∅sim is accurate enough for many tasks and can simulate a guest system 20-30x larger than the host with 8x-100x slowdown for the workloads we observed, with more compressible workloads running faster. For example, we simulate a 1TB machine on a 31GB machine, and a 4TB machine on a 160GB machine. We give case studies to demonstrate the utility of ∅sim. For example, we find that for mixed workloads, the Linux kernel can create irreparable fragmentation despite dozens of GBs of free memory, and we use ∅sim to debug unexpected failures of memcached with huge memories.
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- Award ID(s):
- 1815656
- NSF-PAR ID:
- 10145463
- Date Published:
- Journal Name:
- ASPLOS '20: Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems
- Page Range / eLocation ID:
- 267 to 282
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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First-party datacenter workloads present new challenges to kernel memory management (MM), which allocates and maps memory and must balance competing performance concerns in an increasingly complex environment. In a datacenter, performance must be both good \textit{and} consistent to satisfy service-level objectives. Unfortunately, current MM designs often exhibit inconsistent, opaque behavior that is difficult to reproduce, decipher, or fix, stemming from (1) a lack of high-quality information for policymaking, (2) the cost-unawareness of many current MM policies, and (3) opaque and distributed policy implementations that are hard to debug. For example, the Linux huge page implementation is distributed across 8 files and can lead to page fault latencies in the 100s of ms. In search of a MM design that has consistent behavior, we designed Cost-Benefit MM (CBMM), which uses empirically based cost-benefit models and pre-aggregated profiling information to make MM policy decisions. In CBMM, policy decisions follow the guiding principle that \textit{userspace benefits must outweigh userspace costs}. This approach balances the performance benefits obtained by a kernel policy against the cost of applying it. CBMM has competitive performance with Linux and HawkEye, a recent research system, for all the workloads we ran, and in the presence of fragmentation, CBMM is 35% faster than Linux on average. Meanwhile, CBMM nearly always has better tail latency than Linux or HawkEye, particularly on fragmented systems. It reduces the cost of the most expensive soft page faults by 2-3 orders of magnitude for most of our workloads, and reduces the frequency of 10-1000 mu s-long faults by around 2 orders of magnitude for multiple workloads.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3373376.3378451
