Colocating high- and low-priority VMs under CPU oversubscription is an effective way to improve resource utilization, but it demands careful core allocation to control contention and ensure the QoS. Existing solutions typically rely on Linux cgroup mechanisms such as cpuset, quota, and share. However, our experiments show that these mechanisms have inherent limitations and cannot simultaneously ensure QoS and resource efficiency. Unconditional preemption introduces new opportunities, which is a new kernel feature supported by major cloud vendors. Our experimental analysis reveals that, while unconditional preemption provides strong performance guarantees for high-priority VMs, it also increases the risk of starving low-priority VMs.We present Eunomia, a CPU core allocator for oversubscribed clouds. Eunomia employs a black-box QoS degradation detection model that leverages transmit packet counts and kernel-level KVM tracepoints to identify performance degradation in high-priority VMs. Guided by this model, Eunomia selectively enables unconditional preemption only for degraded high-priority VMs, ensuring QoS while improving CPU efficiency. Experiments show that Eunomia delivers high-priority performance comparable to isolated execution while improving low-priority throughput by 50–64% over the best-performing cpuset-based baseline.

We confront two challenges in the management of a vast and diverse array of online web applications deployed on enterprise-grade auto-scaling infrastructure, primarily focused on ensuring Quality of Service (QoS) for large-scale applications and optimizing resource costs. Firstly, reacting to increased load with a response-based approach can temporarily degrade QoS because many web applications need a few minutes to warm up. Therefore, precise workload prediction is critical for predictive scaling. However, our analysis of real-world applications underscores the substantial challenges arising from the limited precision and robustness of existing single prediction algorithms in the context of predictive auto-scaling. Secondly, guaranteeing the QoS of online applications within a cost-effective structure is crucial, as it is inherently linked to corporate profitability. Nevertheless, our study shows that mainstream auto-scaling methods exhibit various limitations, either being unsuitable for online environments or inadequately ensuring QoS.

Numerous high-performance updatable learned indexes have recently been designed to support the writing requirements in practical systems. Researchers have proposed various strategies to improve the availability of updatable learned indexes. However, it is unclear which strategy is more profitable. Therefore, we deconstruct the design of learned indexes into multiple dimensions and in-depth evaluate their impacts on the overall performance, respectively. Through the in-depth exploration of learned indexes, we reckon that the approximation algorithm is the most crucial design dimension for improving the performance of the learned indexes rather than the popular works that focus on the learned index structure. Moreover, this paper makes a comprehensive end-to-end evaluation based on a high-performance key-value store to answer people’s concerns about which learned index is better and whether learned indexes can outperform traditional ones. Finally, according to end-to-end and in-depth evaluation results, we give some constructive suggestions on designing a better learned index in these dimensions, especially how to design an excellent approximate algorithm to improve the lookup and insertion performance of learned indexes.

The growth in data storage capacity and the increasing demands for high performance have created several challenges for concurrent indexing structures. One promising solution is the learned index, which uses a learning-based approach to fit the distribution of stored data and predictively locate target keys, significantly improving lookup performance. Despite their advantages, prevailing learned indexes exhibit constraints and encounter issues of scalability on multi-core data storage. This paper introduces SALI, the Scalable Adaptive Learned Index framework, which incorporates two strategies aimed at achieving high scalability, improving efficiency, and enhancing the robustness of the learned index. Firstly, a set of node-evolving strategies is defined to enable the learned index to adapt to various workload skews and enhance its concurrency performance in such scenarios. Secondly, a lightweight strategy is proposed to maintain statistical information within the learned index, with the goal of further improving the scalability of the index. Furthermore, to validate their effectiveness, SALI applied the two strategies mentioned above to the learned index structure that utilizes fine-grained write locks, known as LIPP. The experimental results have demonstrated that SALI significantly enhances the insertion throughput with 64 threads by an average of 2.04x compared to the second-best learned index. Furthermore, SALI accomplishes a lookup throughput similar to that of LIPP+.