cs.DC · 2026-09-24 · No. 123
Distributed, Parallel, and Cluster Computing, 2026-09-24.
4 new papers in cs.DC. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
4 entries-
01
Flamingo: On Load Balancing in DAG-based Consensus Protocols
Zhen Ping Khor, Garvit Gupta, Mohammad Javad Amiri, Boon Thau Loo
cs.DC · cs.DB
Distributed data management systems deployed in untrusted environments rely on Byzantine Fault-Tolerant (BFT) consensus protocols to tolerate malicious failures. DAG-based BFT protocols improve throughput by letting validators disseminate transactions concurrently and by scaling execution across multiple workers. However, imbalances in workload or resource capacity can still degrade performance significantly. This paper presents Flamingo, a...
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02
The KV Cache Working Set: Online Capacity Planning for LLM Inference Systems
Luchang Li, Shuaishuai Wang, Zhao Ruan, Dongfang Li, Bozhao Gong
cs.DC
Prefix caching is critical for efficient large language model (LLM) serving, particularly for agentic workloads that repeatedly invoke the model with a growing conversation and tool-use history. By reusing the key-value (KV) states of previously processed prefixes, prefix caching avoids redundant prefill computation. Its effectiveness, however, depends on retaining a sufficiently large set of KV cache states. Provisioning enough cache to...
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03
Backstitch: Restoring Request Causality Across a Production Microservice Fleet
Ziyue Dang, Qiuyu Wu, Haoyun Xu, Tongjue Wang, Yongqing Ling, Weihao Chen, Guangming Luo
cs.DC
A major video platform runs on thousands of microservices, each request propagating a context so downstream work can be traced and governed. At handoffs outside instrumented paths, e.g., custom queues and callbacks, the payload continues but the context does not, and the request still succeeds under existing tests. Such breaks are silent and widespread: 673 of 1,133 services carried at least one. Backstitch, a specialized agentic system,...
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04
CerebroSim: Scalable Whole-Brain Simulator at 100-Trillion-Synapse Scale on the LineShine Supercomputer
Guangnan Feng, Tianxiang Lyu, Hao Huang, Honghui Liang, Jingjing Li, Zhiguang Chen, Yutong Lu
cs.DC
Building executable brain models is essential for moving neuroscience from description to mechanism and prediction. Human-brain-scale spiking simulation is constrained by highly irregular communication, multithreaded spike delivery, and the memory cost of sparse connectivity. We present CerebroSim, a scalable framework for whole-brain simulation. CerebroSim combines Delay-aware Spike Broadcast (DSB) for aggregated delay-aware communication,...
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