Recent advances in recommendation scaling laws have led to foundation models of unprecedented complexity. While these models offer superior performance, their computational demands make real-time serving impractical, often forcing practitioners to rely on knowledge distillation—compromising serving quality for efficiency. To address this challenge, we present SOLARIS (Speculative Offloading of Latent-bAsed Representation for Inference Scaling), a novel framework inspired by speculative decoding. SOLARIS proactively precomputes user-item interaction embeddings by predicting which user-item pairs are likely to appear in future requests, and asynchronously generating their foundation model representations ahead of time. This approach decouples the costly foundation model inference from the latency-critical serving path, enabling real-time knowledge transfer from models previously considered too expensive for online use. Deployed across Meta's advertising system serving billions of daily requests, SOLARIS achieves 0.67% revenue-driving top-line metrics gain, demonstrating its effectiveness at scale.

Storage tiering (e.g., SSD+HDD) is designed to achieve a better tradeoff between performance and cost-effectiveness for storage systems. With the development of Shingled Magnetic Recording (SMR) drives, replacing conventional HDD with a higher density of SMR drives in tiered storage can further improve cost-effectiveness. However, with data tracks overlapped in SMR drives, the "non-sequential' writes in SMR drives cause explicit performance penalties, which is the most challenging issue of using SMR drives in storage tiering.In this paper, we present SMRTS, a file system for SSD-SMR tiered storage with data deduplication. First, SMRTS deduplicates the files being migrated from SSD to SMR to solve the non-sequential write issue of SMR drives and further optimize the space utilization. Second, to address the performance overhead caused by deduplication, we propose file recipe reuse and refresh, hints-based container allocations, and fast container validation to address the penalties caused by data fragmentations. We conduct experimental evaluations of SMRTS using both benchmarks and real-world workloads. The evaluation results show that compared with a compatible file system on SSD+HDD tiered storage, SMRTS achieves a similar performance but provides a much larger space (at least 1.25X). The proposed optimizations improve migration performance up to 17X.

Kubernetes (k8s) is a system for managing containerized applications across multiple hosts. It offers automatic deployment, maintenance, scaling, and resource management for applications. Applications in k8s usually have different storage requirements in the form of service-level objectives (SLOs). However, the current k8s storage management has several limitations which cause explicit performance and cost overhead. K8s administrators have to configure storage in advance manually, and users must know configurations and capabilities of provided storage. Users' storage SLOs can be easily violated in k8s.In this paper, we design and implement k8s Enhanced Storage (k8sES) which efficiently supports applications with various storage SLOs along with all other requirements in the Kubernetes environment. We design and incorporate storage scheduling as part of the node scheduling process in k8s. Applications will be scheduled onto the correct nodes and storage without intervention from either users or administrators. Proper storage resources will be dynamically carved based on users' storage SLOs. In addition, we provide a tool to monitor the I/O activities of both applications and storage devices in k8sES. The evaluation shows that k8sES can better meet users' storage SLOs along with other requirements. Also, k8sES can achieve higher resource utilization efficiency with overhead similar to that of the current k8s.

Interlaced Magnetic Recording (IMR) is a promising technology which achieves higher data density and lower write amplification (WA) than Shingled Magnetic Recording (SMR). In IMR, top tracks and bottom tracks are interlaced so each bottom track is partially overlapped with two adjacent top tracks. Top tracks can be updated without any WA, but bottom track updates require reading and rewriting of affected valid data on the two neighboring top tracks. There are few published studies discussing WA in IMR drives. We propose TrackLace to reduce WA for IMR. TrackLace consists of three techniques: Z-Alloc allocates user data to the tracks in alternating directions and spreads unallocated tracks among allocated tracks; Top-Buffer opportunistically utilizes unallocated top tracks to buffer bottom track updates; and Block-Swap progressively swaps bottom track hot data with top track cold data during high space utilization. To further optimize TrackLace performance, we propose a virtual frame design that can keep the relocated block (due to Top-Buffer or Block-Swap) close to its original location and an adaptive buffering mechanism that can avoid unnecessary redirections depending on the write locality. Evaluations show that TrackLace can reduce WA by 45 percent and lower average latency by 31percent compared with baseline schemes.

Hybrid Shingled Magnetic Recording (H-SMR) drives are the most recently developed SMR drives, which allow dynamic conversion of the recording format between Conventional Magnetic Recording (CMR) and SMR on a single disk drive. We identify the unique opportunities of H-SMR drives to manage the tradeoffs between performance and capacity, including the possibility of adjusting the SMR area capacity based on storage usage and the flexibility of dynamic data swapping between the CMR area and SMR area. We design and implement FluidSMR, an adaptive management scheme for hybrid SMR Drives, to fully utilize H-SMR drives under different workloads and capacity usages. FluidSMR has a two-phase allocation scheme to support a growing usage of the H-SMR drive. The scheme can intelligently determine the sizes of the CMR and the SMR space in an H-SMR drive based on the dynamic changing of workloads. Moreover, FluidSMR uses a cache in the CMR region, managed by a proposed loop-back log policy, to reduce the overhead of updates to the SMR region. Evaluations using enterprise traces demonstrate that FluidSMR outperforms baseline schemes in various workloads by decreasing the average I/O latency and effectively reducing/controlling the performance impact of the format conversion between CMR and SMR.

This paper presents a Virtual Persistent Cache design to remedy the long latency behavior and to ultimately improve the write responsiveness of the Host-Aware Shingled Magnetic Recording (HA-SMR) drives. Our design keeps the cost-effective model of the existing HA-SMR drives, but at the same time asks the great help from the host system for adaptively providing some computing and management resources to improve the drive performance when needed. The technical contribution is to trick the HA-SMR drives by smartly reshaping the access patterns to HA-SMR drives, so as to avoid the occurrences of long latencies in most cases and thus to ultimately improve the drive performance and responsiveness. We conduct experiments on real Seagate 8 TB HA-SMR drives to demonstrate the advantages of Virtual Persistent Cache over the real workloads from Microsoft Research Cambridge. The results show that the proposed design can remedy most of the long latencies and improve the drive performance by at least 58.11 percent, under the evaluated workloads.

This paper presents a Virtual Persistent Cache design to remedy the long latency behavior of the Host-Aware Shingled Magnetic Recording (HA-SMR) drive. Our design keeps the cost-effective model of the existing HA-SMR drives, but at the same time asks the great help from the host system for adaptively providing some computing and management resources to improve the drive performance when needed. The technical contribution is to trick the HA-SMR drives by smartly reshaping the access patterns to HA-SMR drives, so as to avoid the occurrences of long latencies in most cases and thus to ultimately improve the drive performance and responsiveness. We conduct experiments on real Seagate 8 TB HA-SMR drives to demonstrate the advantages of Virtual Persistent Cache over the real workloads from Microsoft Research Cambridge. The results show that the proposed design can remedy most of the long latencies and improve the drive performance by at least 58.11%, under the evaluated workloads.

Shingled Magnetic Recording (SMR) drives can benefit large-scale storage systems by reducing the Total Cost of Ownership (TCO) of dealing with explosive data growth. Among all existing SMR models, Host Aware SMR (HA-SMR) looks the most promising for its backward compatibility with legacy I/O stacks and its ability to use new SMR-specific APIs to support host I/O stack optimization. Building storage systems using HA-SMR drives calls for a deep understanding of the drive's performance characteristics. To accomplish this, we conduct in-depth performance evaluations on HA-SMR drives with a special emphasis on the performance implications of the SMR-specific APIs and how these drives can be deployed in large storage systems. We discover both favorable and adverse effects of using HA-SMR drives under various workloads. We also investigate the drive's performance under legacy production environments using real-world enterprise traces. Finally, we propose a novel host-controlled buffer that can help to reduce the severity of the decline in HA-SMR performance under our discovered unfavorable I/O access patterns. Without a detailed comprehensive design, we show the potential of the host-controlled buffer by a case study.

This paper studies delay-bounded service discovery in urban Vehicular Ad-hoc Networks (VANETs), which refers to locating resources and services (e.g., local sensor data and multimedia content) distributed on individual vehicles in the network within a certain delay bound. To facilitate the discovery process, a set of vehicles, called service directories (SDs), can be selected to store the index information of all the resources in the network. Selecting an optimal SD set with minimal size while satisfying the users' requirement of a bounded query response delay is very difficult due to the disruptive nature of VANETs. In this paper, we formulate the Delay Bounded Service Directory Selection (DB-Sel) problem as an optimization problem that minimizes the number of SDs under the delay bound constraint. We prove theoretically that the DB-Sel problem is NP-Complete even when the future positions of vehicles are known a priori. We observe and prove that the number of vehicles encountered by arbitrarily selected SDs within a given delay follows a normal distribution. We also find the contact probabilities among the vehicles exhibit strong temporal correlation. With these observations, we develop a heuristic algorithm which iteratively selects the best candidate according to the normal distribution property and the historical contact probability. We prove that our algorithms have a guaranteed performance approximation ratio compared to the optimal solution. Extensive trace-driven simulation results demonstrate that our algorithm can guarantee the required query delay and select SD sets 20% smaller than those selected by alternative algorithms.