cs.DC · 2026-06-11 · No. 20
Distributed, Parallel, and Cluster Computing, 2026-06-11.
8 new papers in cs.DC. Titles, authors,
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01 — The papers
8 entries-
01
Fair Comparison of Scheduling Algorithms on Heterogeneous Edge Clusters: A Continuous Adaptive Benchmark
Zihang Wang, Boris Sedlak, Juan Luis Herrera, Schahram Dustdar
cs.DC
Modern Artificial Intelligence (AI) workloads deployed across the heterogeneous tiers of an edge--cloud continuum must satisfy multi-dimensional Service Level Objectives (SLOs) over latency, throughput, and output quality. For each incoming task, the scheduler picks both a target node and a processing mode (e.g., full or reduced inference precision). We call this class of problems \emph{Continuous Multi-Mode Scheduling} (CMMS). Comparing CMMS...
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02
Efficient and Robust Online Learning to Rank in Decentralized Systems
Marcel Gregoriadis, Martijn de Vos, Sayan Biswas, Anne-Marie Kermarrec, Johan Pouwelse
cs.DC · cs.IR
In Online Learning to Rank (OLTR), ranking models are trained directly from live user interactions, but existing systems rely on a trusted central server to collect and process these interactions. This leaves operators free to introduce biases that conflict with user interests. Decentralized learning offers an attractive alternative, allowing users to collaboratively train a shared ranking model by exchanging model updates directly with one...
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03
The PM-EdgeMap: Towards Real-Time Process Mining on the Edge-Cloud Continuum
Hendrik Reiter, Christian Imenkamp, Olaf Landsiedel, Andrea Maldonado, Patrick Rathje, Wilhelm Hasselbring
cs.DC
Smart factories are evolving into Cyber-Physical Systems (CPS), demanding increased autonomy. This necessitates real-time decision making, facilitated by insights derived from sensor data. Process mining offers a valuable approach to gain such insights and guide actions. The edge computing paradigm supports this real-time requirement by enabling network communication between sensors and leveraging nearby computing resources. This paper...
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04
From Fork-Join to Asynchronous Tasks: Parallelizing Tiled Cholesky Decomposition with OpenMP and HPX
Alexander Strack, Alexander Van Craen, Dirk Pflüger
cs.DC · cs.PF
Fork-join parallelism, popularized by OpenMP, remains the dominant model for shared-memory parallel programming, but its implicit synchronization barriers can penalize algorithms with inhomogeneous workloads. Asynchronous many-task (AMT) runtimes sidestep these barriers by expressing work as a dependency graph of fine-grained tasks. Yet, the actual performance benefit over a carefully written fork-join baseline is rarely quantified. In this...
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05
Harnessing Routing Foresight for Micro-step-level MoE load balancing in RL Post-training
Yuming Zhou, Haoyang Li, Sheng Lin, Yanfeng Zhao, Tong Zhao, Xupeng Miao, Jie Jiang, Fangcheng Fu, Bin Cui
cs.DC
Mixture-of-Experts (MoE) and reinforcement learning (RL) post-training now dominate large language model (LLM) development, yet expert load imbalance remains a critical challenge. Existing load-balancing systems target pre-training by relying on historical step-level statistics. However, these methods fail under the unique workload dynamics of RL post-training: the step-level load is stable, but the tiny batch sizes processed during...
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06
Optimizing Cloud Deployment: Blending of IaaS and FaaS for Microservice Architecture
Nikhil Kapoor, Sougata Mukherjea
cs.DC
The rapid evolution of cloud computing has resulted in the adoption of hybrid deployments that blend Infrastructure-as-a-Service (IaaS) and Function-as-a-Service (FaaS) service models to optimize resource utilization, scalability, and operational efficiency. This paper presents a comprehensive study and practical implementation of a metrics-driven approach for migrating microservices from a traditional IaaS service model to a hybrid IaaS +...
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07
Consensus Time in 3-Majority and 2-Choices Is Determined by the Maximum Initial Opinion Density
Niccolò D Archivio
cs.DC
We establish the correct parameter governing the convergence time of the 3-Majority and 2-Choices dynamics on the complete graph in the synchronous model. Recent work [Shimizu and Shiraga, PODC'25] provides matching upper and lower bounds on the number of rounds to consensus, but only in a weak sense: the bounds are shown to coincide for some initial opinion configuration. In contrast, we obtain tight bounds in a strong sense, with upper and...
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08
Beyond Per-Token Pricing: A Concurrency-Aware Methodology for LLM Infrastructure Cost Estimation
Chitral Patil
cs.DC · cs.PF
Every public LLM cost calculator we surveyed treats GPU utilization as a fixed input -- entered by the user, baked in as a preset, or silently assumed at 100% -- never measured against the operator's actual load. We show that this assumption is the dominant source of error: on identical H100 hardware, effective cost spans \$0.21 to \$15.25 per million output tokens, an underutilization penalty of 2.5-24x across low-to-moderate enterprise...
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