To accelerate the inference of machine-learning (ML) model serving, clusters of machines require the use of expensive hardware accelerators (e.g., GPUs) to reduce execution time. Advanced inference serving systems are needed to satisfy latency service-level objectives (SLOs) in a cost-effective manner. Novel autoscaling mechanisms that greedily minimize the number of service instances while ensuring SLO compliance are helpful. However, we find that it is not adequate to guarantee cost effectiveness across heterogeneous GPU hardware, and this does not maximize resource utilization. In this paper, we propose HetSev to address these challenges by incorporating heterogeneity-aware autoscaling and resource-efficient scheduling to achieve cost effectiveness. We develop an autoscaling mechanism which accounts for SLO compliance and GPU heterogeneity, thus provisioning the appropriate type and number of instances to guarantee cost effectiveness. We leverage multi-tenant inference to improve GPU resource utilization, while alleviating inter-tenant interference by avoiding the co-location of identical ML instances on the same GPU during placement decisions. HetSev is integrated into Kubernetes and deployed onto a heterogeneous GPU cluster. We evaluated the performance of HetSev using several representative ML models. Compared with default Kubernetes, HetSev reduces resource cost by up to 2.15× while meeting SLO requirements.

Cross-modal retrieval aims to elucidate information fusion, imitate human learning, and advance the field. Although previous reviews have primarily focused on binary and real-value coding methods, there is a scarcity of techniques grounded in deep representation learning. In this paper, we concentrated on harmonizing cross-modal representation learning and the full-cycle modeling of high-level semantic associations between vision and language, diverging from traditional statistical methods. We systematically categorized and summarized the challenges and open issues in implementing current technologies and investigated the pipeline of cross-modal retrieval, including pre-processing, feature engineering, pre-training tasks, encoding, cross-modal interaction, decoding, model optimization, and a unified architecture. Furthermore, we propose benchmark datasets and evaluation metrics to assist researchers in keeping pace with cross-modal retrieval advancements. By incorporating recent innovative works, we offer a perspective on potential advancements in cross-modal retrieval.

In large-scale distributed storage simulations, disk simulation plays a critical role in evaluating system reliability, scalability, and performance. However, the existing virtual disk technologies face challenges in supporting ultra-large capacities and high-concurrency workloads under constrained physical resources. To address this limitation, we propose an overcapacity mapping (OCM) virtual disk technology that substantially reduces simulation costs while preserving functionality similar to real physical disks. OCM integrates thin provisioning and data deduplication at the Linux Device Mapper layer to construct virtual disks whose logical capacities greatly exceed their physical capacities. We further introduce an SSD-based tiered asynchronous I/O strategy to mitigate performance bottlenecks under high-concurrency random read/write workloads. Our experimental results show that OCM achieves substantial space savings in scenarios with data duplication. In high-concurrency workloads involving small-block random I/O, cache acceleration yields up to 7.8× write speedup and 248.2× read speedup. Moreover, we deploy OCM in a Kubernetes environment to construct a Ceph system with 3 PB logical capacity using only 8.8 TB of physical resources, achieving 98.36% disk cost savings.