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1. CBMM: Financial Advice for Kernel Memory Managers

   Mark Mansi, Bijan Tabatabai (August 2022, Proceedings of the USENIX Annual Technical Conference)

   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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2. CBMM: Financial Advice for Kernel Memory Managers

   Mark Mansi, Bijan Tabatabai (January 2022, Proceedings of the USENIX Annual Technical Conference)

   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. 

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3. Mnemo: Boosting Memory Cost Efficiency in Hybrid Memory Systems

   https://doi.org/10.1109/IPDPSW.2019.00080  

   Doudali, Thaleia Dimitra; Gavrilovska, Ada (January 2019, Proceedings on IPDPS'19 Workshops, Workshop on High-Performance Big Data and Cloud Computing (HPBDC))

   For hosting data-serving and caching workloads based on key-value stores in clouds, the cost of memory represents a significant portion of the hosting expenses. The emergence of cheaper, but slower, types of memories, such as NVDIMMs, opens opportunities to reduce the hosting costs for such workloads. The question explored in this paper is how to determine adequate allocations of different memory types in future systems with heterogeneous memory components, so as to retain desired performance SLOs and maximize the cost efficiency of the memory resource. We develop Mnemo, a memory sizing and data tiering consultant, that permits quick exploration of the cost-benefit tradeoffs associated with different configurations of the hybrid memory components used by key-value store workloads. Using experimental evaluation with different workload patterns, Mnemo is able to afford applications such as Redis, Memcached and DynamoDB, with substantial reduction in their hosting costs, at negligible impact on application performance, thus improving the overall system memory cost efficiency. 

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4. Subpage migration in heterogeneous memory systems

   Shashank Adavally, Krishna Kavi (June 2021, Workshop on Heterogeneous Memory Systems (HMEM-2021), Colocated with ICS 2021)

   With the increasing demands for very large physical address spaces and the advent of memory technologies that can support large mem- ories, there is a need to reduce the sizes of system tables such as TLBs and page tables. One can use very large (huge) pages instead of traditional 4K byte pages. However, huge pages are likely to lead to internal fragmentation and may make page migration strategies that aim to move heavily used pages to faster memories inefficient. If only a small portion of a huge page is heavily accessed, it may be worth migrating only that portion to a faster memory. This paper proposes two hardware-based page migration techniques (i) subpage migration with Address Reconciliation (that is, updating physical addresses of migrated pages) and (ii) subpage migration with Re- verse Migration (whereby no Address Reconciliation is needed). We observed speedup ranging up to 17% over migrating huge pages and up to 55% over the baseline (no migration). 

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5. Formal Verification of a Multiprocessor Hypervisor on Arm Relaxed Memory Hardware

   https://doi.org/10.1145/3477132.3483560  

   Tao, Runzhou; Yao, Jianan; Li, Xupeng; Li, Shih-Wei; Nieh, Jason; Gu, Ronghui (October 2021, Proceedings of the ACM SIGOPS 28th Symposium on Operating Systems Principles)

   Concurrent systems software is widely-used, complex, and error-prone, posing a significant security risk. We introduce VRM, a new framework that makes it possible for the first time to verify concurrent systems software, such as operating systems and hypervisors, on Arm relaxed memory hardware. VRM defines a set of synchronization and memory access conditions such that a program that satisfies these conditions can be mostly verified on a sequentially consistent hardware model and the proofs will automatically hold on relaxed memory hardware. VRM can be used to verify concurrent kernel code that is not data race free, including code responsible for managing shared page tables in the presence of relaxed MMU hardware. Using VRM, we verify the security guarantees of a retrofitted implementation of the Linux KVM hypervisor on Arm. For multiple versions of KVM, we prove KVM's security properties on a sequentially consistent model, then prove that KVM satisfies VRM's required program conditions such that its security proofs hold on Arm relaxed memory hardware. Our experimental results show that the retrofit and VRM conditions do not adversely affect the scalability of verified KVM, as it performs similar to unmodified KVM when concurrently running many multiprocessor virtual machines with real application workloads on Arm multiprocessor server hardware. Our work is the first machine-checked proof for concurrent systems software on Arm relaxed memory hardware. 

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