GECKO: Graph-Evolving aware ChecKpOinter for Intermittent Systems

Chipmink: E ! icient Delta Identification for Massive Object Graphs

Enhancing Database Write Performance with the Write Operation Pushdown Framework

Sparsity-Aware Scheduling for Efficient MoE-Based LLM Serving on Edge Servers

Gutenberg: Taming Latency-Critical Cloud Services with Near-Data-Processing

Latency-critical cloud services place growing pressure on memory while requiring isolation, fairness, and predictable QoS. Near-data processing (NDP) reduces data movement by executing requests close to memory, and prior systems further improve locality through caching and replication. However, writes make replica maintenance expensive, while uneven compute and memory traffic can overload a few NDP units and increase tail latency. Existing throughput-oriented schedulers do not fully address these challenges for co-located cloud services. We present Gutenberg, a CPU+NDP for mutable, latency-critical cloud services. Gutenberg stages subpage updates in a CPU-resident delta buffer, allowing hot writable pages to remain replicated without eager full-page synchronization. It also adopts CPU helper cores to assist request execution when NDP execution or replica maintenance becomes costly. An online controller jointly decides page placement, replication, CPU/NDP execution, and routing using access patterns, queue pressure, and feedback from prior decisions. The system further enforces isolation and fair resource allocation across services. We also model-check CPU--NDP coordination protocol for correctness. We evaluate on TailBench using ZSim with Ramulator-calibrated memory timing. Across evaluated services, Gutenberg outperforms prior systems, reducing average and p99 latency by up to 80.4% and 85.8%. It also improves isolation and fairness while adapting to changing workload behaviors.

High-Performance Graph Processing on NVMe SSDs via Chunk-Based Representation and Access

This paper introduces a novel chunk-based graph representation model, featuring classified and hierarchical vertex storage and chunk layout optimization, to improve I/O utilization and presents a latency-optimized access mechanism featuring user-space asynchronous I/O execution and hotness-aware chunk caching management to accelerate I/O and boost cache efficiency.

RARO: Reliability-Aware Read Optimization for Hybrid Flash Storage

Hybrid flash storage combines large-capacity highdensity flash memory with high-performance low-density flash memory, providing excellent cost-effectiveness. Existing data placement strategies for hybrid flash storage typically employ hotness-based data migration relying on a twotier architecture. This approach not only overlooks the variations in read retry counts across different reliability stages, but also leads to severe capacity degradation and exacerbated write amplification. To address these issues, we propose RARO, a Reliability-Aware Read performance Optimization scheme for hybrid flash storage. RARO redesigns data placement by leveraging application-level data access patterns and device-level flash reliability. This is achieved by dynamically migrating data based on its retry count and access hotness. Data residing in an unsuitable flash type triggers immediate migration, with higher priority given to data exhibiting both high retry counts and hotness for placement into low-density flash, thereby enabling proactive performance optimization. Furthermore, RARO employs a progressive threetier (SLC-TLC-QLC) management framework to mitigating the capacity loss. The evaluation of RARO, conducted on the FEMUbased emulator, significantly enhances read performance, with IOPS reaching 9.46× to 15.9× of the baseline across different aging stages, while also recovering 57% to 92% of the usable device capacity.