With the rapid development of cloud computing, numerous cloud services, containers, and virtual machines have been bringing tremendous demands on high-performance memory resources to modern data centers. Heterogeneous memory, especially the newly released Optane memory, offer appropriate alternatives against DRAM in clouds with the advantages of larger capacity, lower purchase cost, and promising performance. However, cloud services suffer serious implementation inconvenience and performance degradation when using hybrid DRAM and Optane memory. This article proposes FlexHM, a practical system to manage transparent heterogeneous memory resources and flexibly optimize memory access performance for all VMs, containers, and native applications. We present an open-source prototype of FlexHM in Linux with several main contributions. First, FlexHM raises a novel two-level NUMA design to manage DRAM and Optane memory as transparent main memory resources. Second, FlexHM provides flexible and efficient memory management, helping optimize memory access performance or save purchase costs of memory resources for differential cloud services with customized management strategies. Finally, the evaluations show that cloud workloads using 50% Optane slow memory on FlexHM can achieve up to 93% of the performance when using all-DRAM, and FlexHM provides up to 5.8× improvement over the previous heterogeneous memory system solution when workloads use the same ratio of DRAM and Optane memory.

Open source software ecosystems evolve ways to balance the workload among groups of participants ranging from core groups to peripheral groups. As ecosystems grow, it is not clear whether the mechanisms that previously made them work will continue to be relevant or whether new mechanisms will need to evolve. The impact of failure for critical ecosystems such as Linux is enormous, yet the understanding of why they function and are effective is limited. We, therefore, aim to understand how the Linux kernel sustains its growth, how to characterize the workload of maintainers, and whether or not the existing mechanisms are scalable. We quantify maintainers' work through the files that are maintained, and the change activity and the numbers of contributors in those files. We find systematic differences among modules; these differences are stable over time, which suggests that certain architectural features, commercial interests, or module-specific practices lead to distinct sustainable equilibria. We find that most of the modules have not grown appreciably over the last decade; most growth has been absorbed by a few modules. We also find that the effort per maintainer does not increase, even though the community has hypothesized that required effort might increase. However, the distribution of work among maintainers is highly unbalanced, suggesting that a few maintainers may experience increasing workload. We find that the practice of assigning multiple maintainers to a file yields only a power of 1/2 increase in productivity. We expect that our proposed framework to quantify maintainer practices will help clarify the factors that allow rapidly growing ecosystems to be sustainable.

Readahead is an important technique to deal with the huge gap between disk drives and applications. It has become a standard in modern operating systems and advanced storage systems. However, it is difficult to develop the common kernel readahead and to achieve full testing coverage of all cases. In this paper, we formulate the kernel read handling, caching and readahead behavior as an absorbing Markov decision process, and present performance evaluations to compare or verify various readaheads. We also introduce algorithms to find optimal prefetching policies for specific read pattern and present the convergence analysis. By exploiting sample-path-based methods, it becomes much easier to evaluate and optimize prefetching policies, which can provide valuable informations to help the design and improvement of practical readaheads. For illustration and verification, we present two examples and some experiments on the readaheads in Linux kernel. The results show that the model-based evaluations agree with the practice and the improved prefetching policy significantly outperforms the original one in Linux kernel for the specified workloads.